Earth Science Essay

– Discuss stellar evolution (describing each stage in brief). What forces are opposing one another throughout the life of a star and how do they influence the various stages in the life cycle of a star Stellar evolution stars exist because of gravity. The two opposing forces in a star are gravity (contracts) and thermal nuclear energy (expands). Stage 1 Birth is where gravity contracts the cloud and the temperature rises, becoming a protostar. Protostars are a hypothetical cloud of dust and atoms in space which are believed to develop into a star. Astronomers are fairly certain of their existence. Protostars are formed about a million years after a gas clump from an interstellar gas cloud has started to rotate and from a disk. The protostar is simply the core of the disk that formed from the clump of gas that was compressed inside the gas cloud. The star becomes a stable main-sequence star, which are characterized by the source of their energy. They are all undergoing fusion of hydrogen into helium within their cores. The rate at which they do this and the amount of fuel available depends upon the mass of the star. Mass is the key factor in determining the lifespan of a main sequence star, its size and its luminosity. Stars on the main sequence also appear to be unchanging for long periods of time. Any model of such stars must be able to account for their stability. Ninety percent of a stars life is in the main-sequence. A red giant is a luminous giant star of low intermediate mass that is in a late phase of stellar evolution. The outer atmosphere is inflated and tenuous, making the radius immense and the surface temperature low, somewhere from 5,000 K and lower. The appearance of the red giant is from yellow orange to red, including the spectral types K and M, but also class S stars and most carbon stars. The burnout and death final stage of a star depends on its mass. After a low mass star like the Sun exhausts the supply of hydrogen in its core, there is no longer any source of heat to support the core against gravity. Hydrogen burning continues in a shell around the core and the star evolves into a red giant. When the Sun becomes a red giant, its atmosphere will envelope the Earth and our planet may be consumed in a fiery death. Meanwhile, the core of the star collapses under gravity’s pull until it reaches a high enough density to start burning helium to carbon. The helium burning phase will last about 100 million years, until the helium is exhausted in the core and  the star becomes a red supergiant. At this stage, the Sun will have an outer envelope extending out towards Jupiter. During this brief phase of its existence, which lasts only a few tens of thousands of years, the Sun will lose mass in a powerful wind. Eventually, the Sun will lose all of the mass in its envelope and leave behind a hot core of carbon embedded in a nebula of expelled gas. Radiation from this hot core will ionize the nebula, producing a striking â€Å"planetary nebula†, much like the nebulae seen around the remnants of other stars. The carbon core will eventually cool and become a white dwarf, the dense dim remnant of a once bright star. Reference Lutgens, F. K. & Tarbuck, E. J. (2011). Foundations of earth science (6th ed.). Upper Saddle River, NJ: Prentice Hall ES 1010, Unit 8, Question 12 – How do we calculate or determine the distances to stars? What units do we use and what are the limitations (if any) of the method used for such calculations? Measuring distance to stars has been considered a very difficult task. Stellar parallax is a method used to determine distance, the extremely back and forth shifting in a nearby star’s apparent position due to the orbiting motion of earth. The farther away a star is, the less its parallax. The light year is a unit used to express stellar distance, which is the distance light travels in a year, which is approximately 9.5 trillion kilometers (5.8 trillion miles). The parallax angles are very small. Proxima Centauri is the parallax angle nearest to the star. It is less than one second or arc, which equals 1/3600 of a degree. A human finger is roughly 1 degree wide. The distances to stars are so large that conventional units such as kilometers or astronomical units are often too cumbersome to use. Some limitations are that parallax angles of less than 0.001 arcsec are very difficult to measure from Earth because of the effects on the Earth’s atmosphere. This limits Earth based telescopes to measuring the distances to stars about 10.01 or 100 parsecs away. Spaced based telescopes can get accuracy to 0.001, which has increased the number of stars whose distance could be measured with this method. However, most stars even in our own galaxy are much further away than 1000 parsecs, since  the Milky Way is about 30,000 parsecs across. Reference Lutgens, F. K. & Tarbuck, E. J. (2011). Foundations of earth science (6th ed.). Upper Saddle River, NJ: Prentice Hall

How Technology Has Changed Our Lives Essay

Even though it seems like technology has reached its limits and will stop changing, it’s still improving and will probably stop improving itself. Just twenty years ago, personal computers became small and affordable enough for families to buy and use them at home. Since then, technology has shown no signs of stopping or even slowing down. These days, it seems hard to imagine the original size of computers. Over just a few years, they have become smaller, and much thinner, and even more powerful and faster than ever before. When computers were first invented and started being used, Technology advancements have changed our lives almost completely, but not always in a good way. But luckily, there are still many good things that come with technology. Twenty years ago, if someone was to say that almost everyone would have a cell phone, they would have been called crazy. During that time only the richest people have cell phones, and those phones were much different than the ones we have now. They were much bigger and could only made calls, they also had terrible call quality. However, they were still the building blocks of the future and all the phones that we have now. Smartphone’s can now do almost anything, with Smartphone’s, we are now able to keep in contact with everyone no matter where we are. People can talk to their family members around the world or their friends just a few blocks away. Smartphone’s can also help with safety, if a person is in danger, instead of having to look for someone to help them, they can just call 911 and get help very quickly. Overall, cell phones have revolutionized the way we stay connected with friends and family, and have also increased the amount of safety we have with us.

Traditional Way and Agile Way of Management Essay

Traditional Way and Agile Way of Management - Essay Example Iterative or agile method involves incremental development. The system and the process is gradually built and has an advantage over other methods. For instance, if there is a need to modify the management process or the direction of the system needs to go through sudden abrupt changes; agile management system has a better chance to cater for the change then other methods. One relatively rigid method is ‘waterfall method’.In comparison with the agile project management, the waterfall method uses clearly (or with less flexibility) defined deliverables for the life cycle of the project. The higher management of a company performs a complete audit of the system before it is incorporated into the project management. Agile project management method cuts the processing time considerably. Projects that took months to complete in the past are being accomplished within weeks or days. This project managing method modifies the conventional iteration processes waterfall and makes the m more flexible and advanced.Despite the fact that agile management is rarely used to manage a mega project, it is no less crucial than the conventional of traditional project management methods and tools. A mega project can be divided into several parts and then these small parts can then be governed and managed by agile management. That is why a collage of management methods and tactics are incorporated in one project. The core difference between agile and traditional project management rests in their estimating techniques.

Paraphrasing Essay Example | Topics and Well Written Essays - 500 words - 3

Paraphrasing - Essay Example The pumping station will be self-sufficient and require no external power to pump the water to the reservoir tank. Fuel Cell is a system that can transfer the chemical energy to electrical energy. That means to convert the reaction between hydrogen and oxygen to get electricity. These processes do not produce any air pollution. Hydrogen gas helps a lot in the pump station, because it can produce energy by reacting in fuel cell with oxygen to produce electricity. It acts as a basic material in this Remote Pumping Station System Project because it can generate power. The context diagram (Figure 3 in Attachment) gives the necessary information that we need to calculate to redesign the project. This diagram makes it easier to find out which formula we are going to use in calculations. Experiment 1 (Table1 in Attachment) was to determine the angle of the solar panel. We used the load measurement box of the rotary switch. We set the protractor handout at angel 0. Then we adjusted the solar to the lamp. We had to consider the distance between the solar and the lamp like 50 cm. After that, we wrote down the current (A) in CW (+) and CCW (-) in different angles between 0 and 90 by turning the solar. Experiment 2 (Table 2 in Attachment) we were able to measure the amount of hydrogen created by the electrolyte. We observed the level of hydrogen created, after each 2 ml increment on the cylinder; we wrote down the time it took to reach this increment with the current and voltage. We continued writing down the time until 10 ml of hydrogen was produced. Experiment 3(Table 3 in Attachment) was to determine the fuel cell consumption efficiency. This experiment looked like experiment 2, both of them done by creating hydrogen. When hydrogen had reached 10 ml, the light source turned off. In this time, their would be the electrolyzer storage gas in the storage cylinder. We used several formulas to get the right data for this project. The first calculation was

Marketing Communications. Dramatic changes Essay

Marketing Communications. Dramatic changes - Essay Example To be able to satisfy customer's wants and needs it is necessary to consider the marketing mix of the product or service the company is providing. The marketing mix involves four marketing strategy areas, namely product, price, promotion (The marketing communication and place). In marketing, the "four PS" determine how a product is made or a service provided, how much it costs, where it is distributed and how it is presented in all company's communication. Keller (2001) contends that, the role of marketing communication in modern business practices has been identified as a key factor in survival in modern day business. Companies like Sony, Tesco, Volvo, BMW, McDonald and a host of others have increasingly used the four Ps as a competitive weapon. As part of the search for business effectiveness the entire process of marketing communication is being approached in a comprehensive and unified manner where by all activities of business communication functions in unison. This approach is called Integrated Marketing Communication. This is a relatively new concept in management. It is engineered to harness all aspects of marketing communication such as advertising, promotion of sales, public relation, and direct marketing in a highly focused manner eschewing the former tendency of these departments to function in isolation.Competitiveness in business the world over is so acute that survival is possible for only those organizations, which are ready to employ every possible means to increase profit by reducing cost in production, while remaining uncompromising in quality and aggressive in marketing. The account of the phenomenon of Integrated Marketing Communication by Aaker, Batra & Myers (1992) constitutes a fair working explanation of the phenomenon Having said this so far, in the remaining part of the paper, using Sony as a product and at the same time a company I will outline the key characteristic of the target audience, the communication strategies used with the impact of its marketing strategies. 1.1 Sony and the Four Ps I have chosen Sony as my institution under case study. Focusing on Sony is based on a number of important advantages. Sony is a global leader in the electronic industry, it activities have been benefited in almost all the continents. "The influence of words over men is astounding." -- Napoleon. As the whole world gears for peace, marketers and other business people talk in terms of war. We see battle words everywhere in business: Japan bashing, corporate raiders, hostile takeovers, cola wars. Marketing is war (Duncan 2001). With the advent of events, ordinary business executive fancy themselves as warriors. Aggressively attacking weak companies, and defending market shares. Positioning products in people's minds and making them attractive to market segments requires careful formulation of the marketing mix. Getting the right blend of the product, promotion price and distribution is essential to put the carefully carried out analysis into operation. The aim is to portray an image for the product or service that will match with how one wants the product to be visualized in people's mine (Keller 2001, Duncan 2001) Table 1:A summary of what constitute each of the Ps of the 4Ps Product Tangible products Psychological attributes Quality Services Benefits and features Packaging Styling Image Branding Customer service After care Guarantees Image Pricing Selling price Price positioning Distributor margins

Nutrition, Metabolism, and Thermoregulation Essay

Nutrition, Metabolism, and Thermoregulation - Essay Example On the other hand, I also appear to take more eggs in some days than the recommended amount. In support of eating eggs, the Harvard Medical School identifies the important  role of eating one egg each day due to the nutrients the eggs provided. However, I need to restrict the number of eggs that I took daily since there are times that I can prepare four eggs in a single meal, which goes beyond the recommended amount of dietary cholesterol recommended. In this case, dieticians recommend 500mg of cholesterol in a day while an egg contains about 214mg of cholesterol (Sefcik, 2011). Therefore, four eggs contain more than the required  daily  dietary recommendation of cholesterol considering that I do not participate in physical exercises. One part of my diet, which I should improve on, regards the amount of veggies and salad that I consumed. In line with this, I should point out that eating salads and veggies gave me an energetic feeling each day. In addition, the same case applies the moment I eat apples, which are the only fruits that form my weekly diet. In this case, I find myself very energetic and in a buoyant mood when I eat apples. However, I have realized that my diet does not include allocations for many fruits as should be the case. Consequently, instead of taking a bottle of soda each day, I will ensure that I ate more fruits in order to maintain a healthy body. I discovered something else in the course of the week. In this case, I realized that I experienced changes in my body after taking some foods. For example, eggs made  me  have an itchy throat, made me hyperactive, and made me have dazed thinking. However, I should point out that this was only in instances when I took four eggs. Nonetheless, taking one egg did not give me any of these symptoms. Consequently, I realize that I need to stop taking this number of eggs in order for me

Electronic commerce as the concept of marketing Essay

Electronic commerce as the concept of marketing - Essay Example Electronic commerce as the concept of marketing Information technology plays an important role in the development and growth of industries of any economy.. Incorporation of information technologies has changed the business process of all the industries whether they are small-scale or large scale. It has entirely changed the aspects of market competitiveness in terms of products and processes. The amalgamation of information technology into business sector recompenses for size and distance and enables companies to expand and to work in a global market. Using new innovative tools and techniques of information distribution, they can no longer be isolated from international market. Such technologies includes electronic/video conferencing, mailing, tele-conferencing, electronic commerce, electronic networking etc. Internet is pool that can be used to access any kind of information without compensating on quality, legal and regulatory requirements, fiscal regulations and opportunities. It becomes very simple and easy to attain, collect any kind of information on technologies and markets with the use of various networking components. The acquired information can be used as a source of analysis to increase the productivity, profit and market share of the enterprise. The information is accessible with in a few seconds.Evolution in the world of computing and in the era of communications takes the form of global information networking. The net result of this innovation is that it decreases the cost, the time for collection the required valuable information no matter how far that information is. Along with this, the ability to collect, analyze and the frequency of transmission of data has enhanced extremely. Local knowledge can be assimilated, distributed among economic agents and then can be merged with global knowledge to give the valuable piece of information. The net effect of all these activities and use of communications technology has drastically decreased the transaction costs; expedite the triumph of scope with the familiar rapid and continuous customization. Such transmissions undermine authoritative controls since the hoarding of information is no longer possible. For all the Internet's promise as the consummate commercial marketing vehicle many companies are skeptical of their ability to accurately judge the return on their cyberspace investment. The development of internet-based technologies opens endless opportunities for Marketers. Drawing coop concentration to the ethical facet of the use of web-based technologies in the area of business might comprise of differentiating force for proactive firms. So, eCommerce is everywhere whether it is e Mail and messaging or shopping cart or order processing system or domestic or international payment systems. But in this rapidly changing environment of e business, business executives need to react immediately and sufficiently by converting their traditional business strategies to e-commerce processes. In doing so, they must assess opportunitie s and threats by examining closely the economic, demographic, political, cultural and technological factors that affect businesses trading online. Economic Factors With the emergence of whole world as global market, the significance of e

Managing Contention for Shared Resources on Multicore Processors Case Study

Managing Contention for Shared Resources on Multicore Processors - Case Study Example The advantages of parallel computing include saving on the time required for computing and providing concurrency where the multiple processors can solve several computational problems simultaneously. The modern computer systems with multicore processors apply the concept of parallel computing in their operation. In parallel computing, the computer systems share several hardware resources such as LLC and memory controllers to enhance their operation. Several processors cores are assigned to a common memory resource. Processors operating under the same memory resource may compete for the shared memory resource thus, causing traffic and congestions. This is referred to as contention. Contention causes slowing down of the computer system thus reducing the performance of the system (Yuejian, 2012). There are two types of contentions namely; communication contention and memory contention. Communication contention occurs when several processor cores contend for a common communication link. This causes traffic and performance degradation, and in turn slows performance. Memory contention on the other hand, occurs when several processors compete for resources from the same memory module. In a test to demonstrate how contention for the shared resources affects the operation of the computer system, three applications namely Soplex, Sphinx, and Namd were run simultaneously on an Intel quad core xeon system. Soplex, Sphinx, and Namd were paired to run in the same memory domain in different schedules. The result of the combinations of applications indicated a dramatic difference between the different pairs of applications. The applications run as a whole performed 20 percent better with the best schedule, while by running Soplex and Sphinx applications simultaneously the performance was great as 50 percent. The Soplex and Sphinx pair of combination sharing the same memory module was considered as the best schedule. The

Literature of the African Diaspora Essay Example | Topics and Well Written Essays - 1250 words

Literature of the African Diaspora - Essay Example Of great interest when studying Callaloo is the establishment of exactly how the persons of African heritage are able to claim their multiple identities and especially so in light of despite persons of a diaspora essentially inhabiting a number of different these persons are nevertheless unable to call these different places home. Black persons of the African diaspora tend to adapt or create a number of new identities as they continuously move from place to place. In his book, Reversing sail, Gomez points out that the relatively small trickle of African slaves that had been captured as slaves during the fifteenth century eventually evolved into becoming a veritable flood by the end of the seventeenth century. Within a time period of ten years after Columbus’ maiden voyage in 1492, numerous enslaved Africans were enslaved in the New World along with a number of other slaves drawn from Portugal and the Canaries and sources as experienced sugarcane planters. Of particular note is the fact that by 1560, the total number of African slaves was seen to greatly outnumber Europeans in Hispaniola and Cuba, this impressive growth in number eventually saw the number of African outnumber that of Europeans in Vera Cruz and Mexico City by 1570 (62-63). Numerous countries across Europe were seen to join in the slave trade in a move that saw an approximated 6.5 million Africans get shipped out of the African continent between 1700 and 1810. During this period, more European nations were seen to get involved in the slave trade. Some of these nations included Denmark, Britain, France, Portugal, Holland, Sweden and Spain. A number of other non-European countries such as Brazil and the United States also joined the slave trade (Gomez 64). By participating in the slave trade these countries were seen to essentially promote the spread of the Black African diaspora as a review of the regions from which they were drawn from can be seen to essentially reveal a considerable degree of complexity not only in respect to culture and language, but also as pertaining to the different forms of government, technology, regional and trans-regional commerce and agriculture. The Africans transported into the various different regions across the world were to eventually face systems that were essentially quite diverse resulting in increased diversity and multiplicity on the part of these Africans. The Development of the African Class of Mixed Heritage (Mulatto) and their Attempts at Acceptance by Whites Although the questions pertaining to race were seen to be a complex matter in most of the regions that the Africans had been enslaved, it was generally found to be quite complex in some regions such as in Saint Domingue where there arose a class of free blacks or affranchis. This class of free blacks was seen to primarily comprise of persons of mixed ancestry who were mostly women and numbered an estimated 27,000 in 1789. This new class of citizenry owned about 25 percent of the African slave population and accounted for 11 percent of Saint Domingue’s urban population. About two thirds of these citizens were the product of white slave holders and enslaved females and children born out of such unions were born as free men. The affranchis population quickly expanded and by the middle of the eighteenth century, they were able to be widely

Literacy Narrative Essay Example | Topics and Well Written Essays - 750 words - 2

Literacy Narrative - Essay Example At a moment, he had no prior role in my life while next moment he proved out to be a blessing in disguise, defining for me some purposefulness in life. I never had much regard for literacy and education but that event was pivotal in making me question my own ignorance and beliefs. A turning stone We were born and brought in Middle East, where Arabic was our primary native language while ability to speak English was an add-on. I never did much effort trying to learn other languages or seek education since Arabic sufficed all my needs to communicate on daily basis. I still can clearly visualize that day when I met Hashim for the first time at his home 6 years back when he invited Ali for lunch and Ali tagged me along. Hashim was only 11 years old then which makes him 5 years younger than me. After getting done with our meals, we rested in couch while Hashim connected his laptop to internet. After signing in to Windows Live Messenger, he called out to me, ‘Hey, give me your email address so I can add you to my friends’ list and we can chat online sometimes’. This came as a shock for me as I wasn’t expecting him to be so fluent at English, especially at such young age, that he was capable of chatting online in this language. I still had to hear it from him once so I confirmed and received his confident reply, ‘Yes, I can speak in English fluently’. ... Soon I was able to realize that he belongs to an extremely rich family who must have spent massive amounts of money on his education. Nevertheless, that moment I promised myself that I shall work on my English language and soon be a fluent at writing, speaking and understanding it. To enable this, the very next day I joined a language institute to improve my English and enhanced my fluency within few days. This was a turning stone for my life and career: I got accepted at King Fahd University of Petroleum and Minerals, one of the best reputable universities in gulf countries. Additionally, the first year was a probation year where we had to go through extensive testing, including two English courses where the mentors were native English speakers. This also gave me an edge to get a better grip on American as well as British English language, terminologies and accent and distinctions between them. Due to my strong skills in the language, I was easily able to survive the first year with excellent grades. Concluding remarks Owing to my embarrassing moment triggered by Hashim’s question, not only did I get an aim and direction in life to be determined about, I also became part of a prestigious educational institution, built a strong career and have excellent communication skills in English language as of today. This was a crucial milestone in my life which modified my beliefs and attitude towards literacy. I became more mature and responsible and developed respect for educational concerns. Moreover, I started helping people in my vicinity to overcome their communication problems so they do not have to face similar embarrassing situation

From Poland to USA Essay Example for Free

From Poland to USA Essay Life, for me, has always been about taking risks in order to fulfill our dreams. Sometimes, we are required to face the difficult challenges in order to succeed in life. Originally, I am from Poland, and five years ago, I decided to move to the United States to fulfill my dreams. Moving to the United States was a very big leap from my end. My determination to succeed in life has been my driving force for survival in a country away from mine. My dream of acquiring a degree from an American college was now within reach. All I needed was a little bit of luck, and a handful of determination. The first few years I spent in the United States was a struggle. I had to learn a new language and adapt a new culture. I found myself adjusting to a new environment, where I was able to appreciate the new things that surrounded me. Living a life in New York was something out of the extraordinary. To fend for myself, I took on odd jobs, from being a waitress, to a cashier, and a paralegal for different employers. Although I was living a comfortable life, I was not contented with what I had. I wanted to enhance myself by continuing my education. I wanted so much to be a successful career woman. Although I had acquired a Masters degree in Sociology, I still wanted to pursue a Masters degree in Public Administration. I have always been ambitious and determined to achieve my goals. A new degree will help me enhance my knowledge about things, and eventually help me to be of service to the United States. New York City faces many issues that require the implementation of new policies. With the education provided to me, I may be able to face such and do the necessary changes for the betterment of society. The knowledge I have acquired from studying Sociology has given me a well rounded education in liberal arts, that may be used to my advantage in my future careers. Having a fulfilling career definitely means a lot to me. The education that this university will give me will help me become a more diverse individual. In addition to this, I may be able to integrate my Polish culture with that of the Americans, that may be used to my advantage in further job opportunities.

advertisements concerning attention, cognitive learning and motivation

advertisements concerning attention, cognitive learning and motivation 1.0 Executive Summary This proposal examines broad areas of issues in advertisements concerning attention, cognitive learning and motivation in messages as problem in the communication field. The first section elaborates about that background of advertising, followed by the definitions of problems. In the later section, an integrated oriented literature review of previous research conducted will give a short insight of the methods and social research that were carried out. In section 4.0, the objectives of the proposed study will give the highlights what the study can obtain and follow by the methods of research, data collection and analysis. The summary of the proposal is included in the section 6.0, which is the conclusion. 2.0 Background To The Problem 2.01 Advertising Belch and Belch (2004) defined advertising as space or time that is bought by an identified sponsor to use any form of nonpersonal communication elements (e.g., television, radio, magazines, or newspapers) to deliver messages to a large number of individuals of potential consumers, frequently at the same time about an organisation, product or service (Belch Belch, 2004, pp16). Wells, et al (2003) alleged that advertisements strive to satisfy consumers objectives by engaging them and delivering a relevant message. Hence, the consumer may remember the advertisement if it is sufficiently entertaining and possibly learn to relate the advertisement to personal needs. Furthermore, the information extracted from the advertisement may provide incentive and reinforce the consumers decision. Whilst from the advertisers perspective, the definitive objective of placing an advertisement is to persuade or influence consumers to do something. The advertiser aims to move consumers to action by attaining the consumers attention, seizing their interests for a period of time to convince the consumers to change their behaviours, try the advertisers product or build brand loyalty (Wells, el at 2003, pp.5). According to Wells, et al (2003) people are concerned about the society being overrun by advertisements, thus many aspects of ethical advertising issues such as advocacy, accuracy and acquisitiveness are being investigated. Hence, advertisers must make mindful decisions to either adhere or breach the codes of ethics (Wells, el at 2003, pp.30 33). 2.02 Problem Definitions Wells, et al (2003) articulate puffery as one of the key issues in advertising, which is defined as ‘advertising or other sales representation, which praise the item to be sold with subjective opinions and superlatives or exaggerations, vaguely and generally stating no specific proofs, the empirical evidence on the effectiveness of puffery indicated that reasonable people do not believe such claims whilst there are public who expects the advertisers to prove the truth of their superlative messages. Ergo, advertisers are advised to conduct necessary research that verifies facts about ethical messages for effective advertising. Advertisers and advertising agencies that have insights into the minds of the potential consumers views and evidences on their perceptions will prove to be helpful in assessing what are ethical conducts (Wells, el at 2003, pp.33 34). Wells, et al (2003) elucidate ‘subliminal messages is transmitted below the threshold of normal perception, where the receiver is not consciously aware of receiving, the embedment of messages are placed to manipulate. Research has yet to prove subliminal messages can affect behaviours due to physiological limitations, while the results in different research has shown indications that subliminal stimuli can cause some types of minor reactions (Wells, el at 2003, pp.42). This proposed research aims to examine the hierarchy of issues in advertising from the consumers perspectives, hence the research process is designated to investigate the important levels of attention, cognitive learning and motivational messages in advertising. 3.0 Literature Review The evidence from studies on advertising overwhelming indicates that additional studies are needed to cover the broad spectrum of issues concerning advertising practice. Rosbergen, et al (1997) adduce a methodology to examine the effects of physical ads of consumers attention to visuals elements on the accounts of heterogeneity, to inquire when and how consumers devote their attention to commercial stimuli and what determines the consumers attentional strategies and patterns. The proposed methodology was driven by the lack of research conducted on consumer attention, even though the importance of attention has been acknowledged (Rosbergen, et al 1997, pp.305). A growing body of research indicates that exposures to ubiquitous advertisements over a period of time have lead to increased physical dissatisfaction amongst a large proportion of women (Halliwell, el at 2005, pp. 408). Other research findings proved that women portrayed in the advertisements do not control for attractiveness. For example, Posavac, et al (1998) compared viewing fashion models with realistically-sized women ‘you might meet in everyday life. Although they do not report attractiveness ratings, they note that the attractiveness of models is accentuated by artificial means. (Halliwell, el at 2005, pp. 408) There are many theoretical reasons to expect that consumer reactions to advertising are affected by their response to the program or print material in which the advertising is inserted. Indeed many studies have looked at the impact of media context on the effectiveness of advertising. At present, however, two major issues arise with this literature. One concerns the need for more specific theories about how media context can affect advertising as well as the other relates to when context affects advertising positively and when it affects it negatively. (Halliwell, el at 2005, pp. 408) Researchers increasingly recognise the interest in on the psychology of consumers has been steadily on the rise. Much of this research has focused on changes in information processing (e.g., Roedder-John and Cole 1986). The research indicates that, consumers of different ages have different level of susceptibility to misleading advertising (Gaeth and Heath 1987) and the truth-inflating effects of repetition (Law, Hawkins, and Craik 1998; Skurnik et al. 2005). The research has shown evidences that consumers of younger age rely more on schema-based whilst older consumers adopt detailed processing strategies. However, aging also has important effects on motivational processes that can significantly affect information processing. In particular, aging is associated with an increase in the motivation to attend to emotional versus factual information (e.g., Labouvie-Vief and Blanchard-Fields 1982; Williams Drolet, 2005, pp.343) Williams and Drolet (2005) conducted their first study on how time horizon perspective affects older and young adult consumers attitudes toward and recall of emotional (vs. rational) appeals. The experiment 1 design was a 2 (age group: older vs. young) x 2 (appeal type: emotional vs. rational) x 3 (time horizon perspective: limited vs. expansive vs. control). In control conditions, where the researchers were expecting age to interact with appeal type that: (1) older participants will have more favourable attitudes toward and better recall of emotional (vs. rational) appeals and (2) young participants will have more favourable attitudes toward and better recall of rational (vs. emotional) appeals (Williams Drolet, 2005, pp.345). Additionally to expectation time horizon perspective to moderate the above effects such that in limited time horizon conditions, where researchers anticipate young participants will show increased attitudes toward and recall of emotional (vs. rational) appeals. In expansive time horizon conditions, Williams and Drolet (2005) look at the prospect of older participants showing increased attitudes toward and recall of rational (vs. emotional) appeals (Williams Drolet, 2005, pp.346) From the analysis tested for potential differences due to the use of two different products (coffee and film), the results indicated no significant differences in results (all ps 1 .30), and analysis are collapsed across the two products. The product categories were tested to use as a potential covariate in the analysis. No effects were significant ( ps 1 .30) and were not discussed further. As expected by Williams and Drolet (2005) the findings from Experiment 1 indicated that in the control time horizon conditions, older participants had greater liking and recall of the emotional appeals whilst the younger participants had greater liking and recall of the rational appeals. Whilst in limited time horizon conditions, both older and young participants attitudinal and memory responses were higher for the emotional appeals. In contrast, in the expansive time horizon conditions, the attitudinal and memory responses were higher for the rational appeals for both groups. As an afterword for Experiment 1, which have proven that age and time horizon perspective moderate responses to emotional and rational appeals to older and young adults. The results compiled from Experiment 1 differ from results of previous research (e.g. Fung and Carstensen 2003), which had inadequate evidence.(Williams Drolet, 2005, pp.345) In Experiment 2, Williams and Drolet (2005) examine how differences in age and time horizon perspective influence consumers attitudes toward and recall of emotional appeals that focus on the avoidance of negative emotional experiences. Participants were instructed to read either a positively framed or negatively framed emotional appeal of one of two emotional products. After reading the appeal, participants were required to answer questions about their attitudes toward products. After that, participants were required to do manipulation checks and answered product use and demographic questions. Lastly, participants were asked to recall all they could about the appeal that they have read earlier (Williams Drolet, 2005, pp.349 50). Williams and Drolet (2005) tested for differences by using two emotional products (greeting cards and flowers). The analysis found no significant differences in results ( ps 1 .30). Hence, Experiment 2 have shown indications that aging and time horizon perspective impact and preferences for emotional versus rational appeals, but also preferences for different types of emotional appeals. Specifically, that avoidance of negative emotional outcomes is more preferable and has higher memory retention among both groups of older and younger participants in limited time horizon view. On the contrary, younger and older participants who had an expansive time horizon view generated were preferably higher on positive emotions and are more memorable (Williams Drolet, 2005, pp.351). Gunter, et el (2005) have preliminary evidence that can lead advertisers to believe that effectiveness of advertisements on consumers retention and comprehension of messages relies on the placement of television programs, positioning of ads in print materials or radio airtime. The nature of the advertising environment can affect memory for embedded advertising as a result of cognitive interference effects when and where the advertisement formats are congruent semantically (Furnham, Bergland, Gunter, 2002;Furnham, Gunter, Richardson, 1999) or in terms of format (Gunter, Baluch, Duffy, Furnham, 2001); or as a function of program-induced moods (Goldberg Corn, 1987; Kamins, Marks Skinner, 1991; Schumann, 1986). Arousal (Mundorf, Zillman, Drew, 199 1; Pavelchak, Antil, Munch, 1988), or excitement (Singh, Churchill, Hitchon, 1987). While unpleasant arousal or interference can impede memory for embedded advertisements, the degree to which any advertisement format involves or appeals also can affect memory (Gunter, et al 2005, pp. 1680) 4.0 Objective of Proposed Research The objective of the research is to provide advertisers and advertising agencies to have insights to create ethical, effective and efficient advertisements to publics. The collection and analysis of consumers personal information from various electronic media and tools with the advancements and improvements in the new age of technologies and research methods, advertisers are able to analyse consumers information, perception and behaviours. 4.01 Methods This study aims to investigate which element in advertising precedes primary in the minds of the consumers, by taking into account the possible role of attention, puffery and motivational messages in advertising. The use of focus group interviews allows researchers to generate information that can be used to design effective, ethical and efficient messages in advertising. Focus group interviews can provide researchers with relevant perceptions and attitudes of selected participants (Frey, et al 2000, pp.221). In addition for more insight and higher success of the interviews, four facilitators will be acquired to guide and lead the focus group interviews. The facilitators will introduce the topics; encourage participations and probes for more information. The participants will be exposed to advertisements of different materials (e.g., television commercials, radio commercials, magazines ads, or newspapers ads). The participants will be divided into four focus groups that will be videotaped and recorded with written consents given by the participants. Every participant will be asked to provide demographic information including age, gender, race, ethnicity, marital status, and religion. The members of the research team were present to greet and support the focus group, by playing the roles of complete participant, participant observer, observer participant and complete observer via listening to the discussions, and record field notes (Frey, et al 2000, pp.269). Male and female participants will be assigned randomly to 4 treatment conditions, ensuring equal numbers of 5 each gender per condition: Group 1- television commercials and magazine print ads; Group 2- radio commercials and newspaper ads; Group 3- television commercials and radio commercials; and Group 4 magazine print ads and newspaper ads. Each group will spend 30 minutes on the different advertising formats that will be played in a small theatre room that will be fully equipped with a large screen, enhanced audio systems, desks and refreshments. After observing the different formats of advertising, each group will be lead into discussions by the facilitators, where participants will be encouraged to express themselves freely about their experiences, opinions and perceptions. Before finalising the focus group sessions, participants will be given three set of questionnaires to answer. Commercials rating questionnaire. On the program rating questionnaire, participants will use a 10-point scale to rate the advertisements, which they have watch, heard or seen in the focus group session on 12 evaluative scales (absorbing, hostile, arousing, disturbing, engaging, entertaining, enjoyable, exciting, happy, violent, interesting, and involving). Each scale ranged from 1 (not at an> to 10 (extremely). Free-recall questionnaire. A free-recall questionnaire will ask participants to write everything they could remember about the advertisements that they saw. They will be required to write down the name of the product and the brand advertised, and any details of the advertising message. Such details could include specific product-related information, such as price, promotional appeals, specific strengths or benefits, presence of celebrity endorser, and other idiosyncratic features of the advertisement. Brand recognition questionnaire. A brand recognition questionnaire will test participants memory for the brands advertised in the duration of the focus group. Participants will be asked to indicate as many brands as they could remember that appeared during the focus group. Each correct answer was scored 1 point, while incorrect choices were given 0 points. 4.02 Data Collection and Analysis All the members of the research team who will engage in a series of meetings to review and compare the four focus groups coding schemes The meetings will audio-recorded, and then the selected portions of the recordings were transcribed to review dialogue through which concepts will be refined. Metaphor analysis and fantasy theme analysis can best complement the data collected from the focus groups interviews. Metaphor analysis will allow researchers to investigate into participants figures of speech in a word or phrase that denotes one object to another, while fantasy theme analysis allows participants to interact between one another and share stories and experiences (Frey, et al 2000, pp.285). The questionnaires will be content-analysed and compared against a pretested list of salient points that had been identified for each advertisement. The research will be compiled into an informal structure report written by the researchers in first-person singular voice, which signifies rhetorical assumption of naturalistic paradigm (Frey, et al 2000, pp.20). Every participant will be treated as a unit of analysis in analytic strategy to consider the participants behaviours, attitudes, perception and cognitive process. 5.0 Timeline The proposed timeline of research is as below: Week 1 Selecting Respondents Or Target Participants Week 2 Setting the environment for focus groups Week 3 Conducting Focus Group Interviews Week 4 Conducting Focus Group Interviews Week 5 Collection of Data Week 6 Compiling Of Data and Transfer Data Into Transcripts Week 7 Analysis Of Data Week 8 Compilation of Report Week 9 Compilation Of Report 6.0 Conclusion The proposed study has important social implications that can provide advertisers and advertising agencies with more concrete and overwhelming findings to help overcome the issues that are threatening the effects and impacts of advertising on individuals. Hence, the study can result in advertisers creating ethical, efficient and effective advertisements that can influence and persuade individuals with motivational messages that affect emotional appeals positively. 7.0 References Belch, G. E. Belch, M. A. 2004, Advertising and Promotion: An Integrated Marketing Communication Perspective, 6th edn, McGraw Hill, Singapore. Frey, L., Botan, C. Kreps, G. 2000, Investigating Communication: An Introduction to Research Methods, 2nd edn, Allyn Bacon, Needham Heights, MA. Gunter, B., Furnham, A. Pappa, E. 2005, Effects of television violence on memory for violent and nonviolent advertising, Journal of Applied Social Psychology, vol 35, no. 8, pp. 1680 97. Halliwell, E., Dittmar, H. Howe, J. 2005, The impact of advertisements featuring ultra-thin or average-size models on women with a history of eating disorders, Journal of Community Applied Social Psychology, vol 15, pp. 406 13. Jacoby, J. Hoyer, H. W. 2002, Viewer miscomprehension of televised communication: Selected findings, Advertising Social Review, viewed 16 October 2009,http://muse.jhu.edu.ezproxy.lib.uts.edu.au/journals/advertising_and_society_review/v001/1.1jacoby.html Rosbergen, E., Pieters, R. Wedel, M. 1997, Visual attention to advertising: A segment level analysis, Journal of Consumer Research, vol 24, pp. 305 -15. Wells, W., Burnett, J. Moriarty, S. 2003, Advertising: Principles and Practice, 6th edn, Prentice Hall, New Jersey. Williams, P. Drolet, A. 2005, ‘Age related differences in responses to emotional advertisements, Journal of Consumer Research, vol.32, pp. 343 55.

Solutions to Domestic Violence

Solutions to Domestic Violence Problem Solution Every now and then, people have been known to say, What can they do to help someone they know that is being abused? There are many different solutions that people can do to help those in need that are involved in a domestic violence relationship. According to the Michigan State Police there are various solutions involved in assisting someone. Knowing what one is talking about by having some background on domestic violence. Always let them know that your ears are open at anytime they need to talk. Help them as much as possible by being respectful, patient and supportive in learning about their safety. Lastly, never let them think it is their fault, keep addressing that as much as possible. There is always ways to get help when someone needs it. They can do so by calling the confidential National Domestic Hotline (DMVH) at 1-800-799-SAFE (7233), they are available to the United States, 24 hours, 7 days a week, and open 365 days a year. The DMVH have counselors to support them in getting them information and referrals for themselves, their children, shelter, and legal assistance (Michigan State Police). There are many other things that you could do to help a victim or even a victim herself can do. It may be just as easy as picking up a phone book to find out what organizations in your community help out with such as employment or even child care. There are other different things that can be done such as seeking counseling or even support groups. While you are seeking counseling, make sure you identify weather the counselor is for the abused and has had experience of working with the abused. Most of all stay active as much as possible to help your self esteem, self confidence, and getting you independence back (Women Web, Getting Help). Domestic violence shelter, often called a womens shelter is a building or set of apartments where victims of abuse can seek shelter. These shelter locations are kept confidential so these womens abusers are unable to find them. Shelters are known to provide those abused and their children with shelter, food and childcare. Since there are very limited times for residing at a shelter, many shelters assist in placement of permanent homes and jobs (Help guide). There are different things that can be done after leaving a shelter so their abuser doesnt find them. The top 3 things that you can do are: 1.) get and unlisted number, 2) use a P.O. Box, 3.) open new bank accounts and credit cards (Help guide). After discovering how serious Domestic violence actually was, the Domestic Violence Bill, 2006 was passed. The Domestic Violence Bill was intended to allow more help and relief of the abuse. The purpose of this Bill was to allow those involved in domestic violence the maximum protection that the laws can provide. These laws have made it mandatory for all police stations to have specific departments to deal with and give legal duty on the officers to assist in a complaint domestic violence. Under the provision of this bill, police officers are suppose to advise the victim of their rights under, help in obtaining shelter, offer medical treatment, and to lodge a criminal complaint. The Domestic Violence Act also gives police officers the power to arrest the accused perpetrator, without a warrant who is reasonably suspected to have committed or who is threatening to commit an act of domestic violence on a victim. If any person is arrested they are to be brought before a magistrate within forty-eight hours (Government Gazette) Under the Michigan Constitution, (Art. I, Sec. 24; eff. Dec. 24, 1988) and the Crime Victims Rights Act, (1985 PA 87; MCL 780.751) have given crime victims the right to be treated with dignity and respect. Making sure all is handled in a timely manner following an arrest. The victim also has the right to receive emergency and medical services. Receive an explanation of all court proceedings. This act allows them to be protected of being free of any threats, acts, and/or discharge from your employer. The name of the Prosecutors who is handling the case. Any scheduled court proceedings, including sentencing, the defendants release on bond or escape from custody while awaiting trial. The probation departments address and telephone number. Attend the court trial and make an oral statement to a pre-sentence investigator, and to write an impact statement which will be included in the pre-sentence report. Victims are also allowed to receive information regarding the conviction, sentence, im prisonment, and release of the accused. (Michigan Prosecuting Attorney Associations). There are many different effects of the abuse. If someone had physical abuse they may suffer from long term health complications. Abused women often have anxiety, tension, low energy, depression, insomnia, loss of appetite, or even headaches. They may believe that they failed the relationship. They have also been known to be ashamed and not allowing others to know exactly what had happened to them. In just about every case of Domestic Violence, women have stated that they have lost their self esteem and lack of confidence. Women have also been known to have anger and fear towards themselves and their abuser. They are also known to isolate themselves from other such as family and friends (Womens Web, The Effect of Abuse). According to Direnfeld (2007), the aftermath of emotional and psychological can last for several years or even lifetime. It not only affects the victim but also other family members and later relationships. In many cases, children have the trauma of the violence. These children grow to become bullies in their own right whose behavior the violated parent cannot control and whose behavior is reinforced by the perpetrator. There are many things that a victim needs to remember, its very hard to do it all at once. Between the police, and shelters, the victim has other they can talk to; rather it is a counselor or even someone that has been through it. There main thing they need to know what are their rights? References Direnfeld, G. MSW, RSW (2007). Alumbo, the Long Arm Of Domestic Violence. Retrieved July 24, 2009 from http://www.alumbo.com/article/32544-The-Long-Arm-Of-Domestic-Violence.html Government Gazett (2006). Domestic Violence Bill, 2006. Retrieved July 22, 2009 from http://www.kubatana.net/docs/legisl/dom_viol_bill_060630.pdf Help guide (2008). Domestic Violence and Abuse: Help, Treatment, Intervention, and Prevention Retrieved July 24, 2009 from http://www.alumbo.com/article/32544-The-Long-Arm-Of-Domestic-Violence.html Michigan Prosecuting Attorney Associations (2008). Victim Rights. Retrieved July 20, 2009 from http://www.michiganprosecutor.org/Victim.htm Michigan State Police (2009). Domestic Violence Awareness. Retrieved July 20, 2009 from http://www.michigan.gov/msp/0,1607,7-123-1589_1711_4577,00.html Women Web (2009). Domestic Violence, The Effect of Abuse. Retrieved July 25, 2009 from http://www.womensweb.ca/violence/dv/effects.php Women Web (2009). Domestic Violence, Getting Help. Retrieved July 25, 2009 from http://www.womensweb.ca/violence/dv/help.php

A History of Video Game Development Essay -- the last of us, Neil Druc

Video games are an ever-growing franchise that is constantly undergoing change. Ever since the dawn of video games, new consoles, games, developers, and teams have come together, fallen apart, triumphed, and failed. What is it that has allowed some to thrive where others failed? Several different factors have changed and influenced the world of gaming, including the history that is continuously being written, the people who have built the games behind the scenes, and, of course, the actual video games themselves. Numerous video games have been more successful than others, but identifying what components set the successful apart from the unsuccessful is something definitely worth observing. To find an answer to this statement, one must first delve back into the roots of where video games began. In 1996, Ralph Baer, an employee of Sanders Associates, envisioned the idea of a television gaming apparatus. Ideally, the contraption could be hooked up to a television and would be complete with a chase game and a visual tennis game. By 1970, Ralph’s idea became a reality as the very first home video game system, which he dubbed, the Odyssey. Though the Odyssey, by modern day standards, might be considered boring, at the time it was made it was an enjoyable and satisfying system. Although, as with all originals, it could easily be improved upon, fixed, and made more entertaining altogether. From 1972 and on, several establishments, corporations, and teams formed in an attempt to improve upon the foundation that Ralph Baer had laid out before them. Although, most of these organizations ended up falling apart due to not being able to make enough money or due to losi ng out to another group. Some of the less fortunate institutio... ...is not needed, a person can successfully construct an exceptional video game. Works Cited Caoili, E. (2013). The Last of Us wins an armful of E3 Game Critics Awards. Gamasutra. Retrieved from www.gamasutra.com/view/news/173052/ Karmali, L. (2013). The Last of Us Sells 3.4 Million Copies in Three Weeks. IGN. Retrieved from www.ign.com/articles/2013/07/09/the-last-of-us-sells-34-million-copies-in-three-weeks Miller, M. (2005, April 1). A History of Home Video Game Consoles. Informit.com/articles. Retrieved November 11, 2013, from http://www.informit.com/articles/article.aspx?p=378141 Smith, E. (2013a). The Last of Us, Neil Druckmann and Less Being More. International Business Times. Retrieve from www.ibtimes.co.uk Smith, E. (2013b). The Last of Us Review [VIDEO]. International Business Times. Review of The Last of Us. Retrieved from www.ibtimes.co.uk

Parental Conflict In Turtle Mo :: essays research papers fc

The Parental Conflict in Turtle Moon   Ã‚  Ã‚  Ã‚  Ã‚  For the average person, occasional inter-personal conflicts are a fact of life. Nowhere do these conflicts manifest themselves with greater tension than in the parent-adolescent relationship. Through their works, writers of fiction illuminate the sources of strain common to parent-child interactions. In the novel Turtle Moon, Alice Hoffman exemplifies this conflict in the relationship between Keith Rosen and his mother Lucy. There are several factors that contribute to this conflict and the work as a whole. The strife between Keith and his mother results from Keith’s desire to live in New York with his father, the lack of parental involvement, and the lack of communication between Keith and his mother.   Ã‚  Ã‚  Ã‚  Ã‚  The discord between Keith and his mother results from his preference to live with his father in New York. Keith has no choice in the decision and now he lives in Verity, a town he hates. This situation lies at the root of his rebellion against his mother. When he lives in New York he is never particularly well behaved, â€Å"but after eight months in Florida, he is horrid†(5). Through his rebellious actions Keith generates grief and worry in his mother Lucy. His backpack must be checked â€Å"for contraband everyday†(31), and he and his mother fight constantly. Because he is forced to live with his mother, Keith resents her. Keith is angry with Lucy because he feels as if he is trapped in Verity. â€Å"He wanted to live with his father, but who asked him?†(6). Keith deliberately disobeys Lucy and has no respect for her. He counts down the days until he can go back to New York and this ignites many arguments between them. Keith’s rebelli ous actions advance the novel’s theme of searching for identity and independence. McBane In addition to living in Verity, another source of the conflict between Keith and Lucy is her lack of parental involvement. Lucy and Keith grow more and more distant from each other because Lucy stays out of Keith’s life. In the same way Keith avoids his mother at every available opportunity. â€Å"He waits in bed until he’s sure she’s left, so he won’t have to see her and pretend to be normal or cheerful or whatever it is she wants him to be†(6). Because Lucy does not involve herself in Keith’s life she wonders what he is doing and tends to assume the worst about him.

Exposing American Myths Today Essays -- essays research papers

The United States of America has been blessed with the grace with God, it is the land of spacious skies, amber waves of grain, endless possibilities, and freedom for all. It is superior to all other nations and when faced with moral dilemmas. It is firmly believed by citizens that God sides with them. Though these are the ideals and the purposes of which the United States was founded, they are still myths and legends that are not necessarily truthful today. They were partially created by facts but mostly by the government and the people. Myths are dreams that take one from reality and place them in a comfort zone that feels much more at ease than dealing with what is reality and truth. There are so many myths that are meant to placate the frazzled American who is just looking for some reassurance from what is stressfully everyday life. However, some myths can be offensive and overlooked as many are not recognized as being false at first glance. Some assumptions of certain cultural gr oups, religious affiliations, political parties, and many others face the abuse daily. Since myths in the United States are often misperceived, the way society sees myths is bias. They can be seen as delusions of the ways people are to distinguish between what is reality and what is not. Myths in the United States began when the first pilgrims set foot on Plymouth Rock, it is unlikely they knew the importance of what they had begun but they knew the principles on which they had founded the new land with the notion that they were now able to begin the second journey of their lives free from British persecution and hardship. A common myth one will find about the new settlers and the Native Americans that resided there can be found in an ordinary kind... ...no myth bold enough to state that bliss has ever resolved anything. Works Cited Churchill, Ward. â€Å"Crimes Against Humanity.† The Presence of Others. Eds. Andrea Lunsford and John J. Ruszkiewicz. Boston: Bedford, 2005. 536-543. Douglass, Frederick. â€Å"What to the Slave Is the Fourth of July?† The Presence of Others. Eds. Andrea Lunsford and John J. Ruszkiewicz. Boston: Bedford, 2005. 522-533. Jefferson, Thomas. â€Å"Declaration of Independence.† The Presence of Others. Eds. Andrea Lunsford and John J. Ruszkiewicz. Boston: Bedford, 2005. 517-520. Shindle, Kate. â€Å"Miss America: More Than a Beauty Queen?† The Presence of Others. Eds. Andrea Lunsford and John J. Ruszkiewicz. Boston: Bedford, 2005. 563-566. Postman, Neil. â€Å"The Great Symbol Drain.† The Presence of Others. Eds.Andrea Lunsford and John J. Ruszkiewicz. Boston: Bedford. 2005. 546-555.

Open Domain Event Extraction from Twitter

Open Domain Event Extraction from Twitter Alan Ritter University of Washington Computer Sci. & Eng. Seattle, WA [email  protected] washington. edu Mausam University of Washington Computer Sci. & Eng. Seattle, WA [email  protected] washington. edu Oren Etzioni University of Washington Computer Sci. & Eng. Seattle, WA [email  protected] washington. edu Sam Clark? Decide, Inc. Seattle, WA sclark. [email  protected] com ABSTRACT Tweets are the most up-to-date and inclusive stream of information and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events.Previous work on extracting structured representations of events has focused largely on newswire text; Twitter’s unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal— the ? rst open-domain event-extraction and categorization system for Twitt er. We demonstrate that accurately extracting an open-domain calendar of signi? cant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models.By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar. com; Our NLP tools are available at http://github. com/aritter/ twitter_nlp. Entity Steve Jobs iPhone GOP Amanda Knox Event Phrase died announcement debate verdict Date 10/6/11 10/4/11 9/7/11 10/3/11 Type Death ProductLaunch PoliticalEvent Trial Table 1: Examples of events extracted by TwiCal. vents. Yet the number of tweets posted daily has recently exceeded two-hundred million, many of which are either redundant [57], or of limited interest, leading to information overload. 1 Clearly, we can bene? t from more structured representations of events that are synthesized from individual tweets. Previous work in event extraction [21, 1, 54, 18, 43, 11, 7] has focused largely on news articles, as historically this genre of text has been the best source of information on current events. Read also Twitter Case StudyIn the meantime, social networking sites such as Facebook and Twitter have become an important complementary source of such information. While status messages contain a wealth of useful information, they are very disorganized motivating the need for automatic extraction, aggregation and categorization. Although there has been much interest in tracking trends or memes in social media [26, 29], little work has addressed the challenges arising from extracting structured representations of events from short or informal texts.Extracting useful structured representations of events from this disorganized corpus of noisy text is a challenging problem. On the other hand, individual tweets are short and self-contained and are therefore not composed of complex discourse structure as is the case for texts containing narratives. In this paper we demonstrate that open-domain event extraction from Twitter is indeed feasible, for example our highest-con? dence extracted f uture events are 90% accurate as demonstrated in  §8.Twitter has several characteristics which present unique challenges and opportunities for the task of open-domain event extraction. Challenges: Twitter users frequently mention mundane events in their daily lives (such as what they ate for lunch) which are only of interest to their immediate social network. In contrast, if an event is mentioned in newswire text, it 1 http://blog. twitter. com/2011/06/ 200-million-tweets-per-day. html Categories and Subject Descriptors I. 2. 7 [Natural Language Processing]: Language parsing and understanding; H. 2. [Database Management]: Database applications—data mining General Terms Algorithms, Experimentation 1. INTRODUCTION Social networking sites such as Facebook and Twitter present the most up-to-date information and buzz about current ? This work was conducted at the University of Washington Permission to make digital or hard copies of all or part of this work for personal or classr oom use is granted without fee provided that copies are not made or distributed for pro? t or commercial advantage and that copies bear this notice and the full citation on the ? rst page.To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speci? c permission and/or a fee. KDD’12, August 12–16, 2012, Beijing, China. Copyright 2012 ACM 978-1-4503-1462-6 /12/08 †¦ $10. 00. is safe to assume it is of general importance. Individual tweets are also very terse, often lacking su? cient context to categorize them into topics of interest (e. g. Sports, Politics, ProductRelease etc†¦ ). Further because Twitter users can talk about whatever they choose, it is unclear in advance which set of event types are appropriate.Finally, tweets are written in an informal style causing NLP tools designed for edited texts to perform extremely poorly. Opportunities: The short and self-contained nature of tweets means they have very simple d iscourse and pragmatic structure, issues which still challenge state-of-the-art NLP systems. For example in newswire, complex reasoning about relations between events (e. g. before and after ) is often required to accurately relate events to temporal expressions [32, 8]. The volume of Tweets is also much larger than the volume of news articles, so redundancy of information can be exploited more easily.To address Twitter’s noisy style, we follow recent work on NLP in noisy text [46, 31, 19], annotating a corpus of Tweets with events, which is then used as training data for sequence-labeling models to identify event mentions in millions of messages. Because of the terse, sometimes mundane, but highly redundant nature of tweets, we were motivated to focus on extracting an aggregate representation of events which provides additional context for tasks such as event categorization, and also ? lters out mundane events by exploiting redundancy of information.We propose identifying im portant events as those whose mentions are strongly associated with references to a unique date as opposed to dates which are evenly distributed across the calendar. Twitter users discuss a wide variety of topics, making it unclear in advance what set of event types are appropriate for categorization. To address the diversity of events discussed on Twitter, we introduce a novel approach to discovering important event types and categorizing aggregate events within a new domain. Supervised or semi-supervised approaches to event categorization would require ? st designing annotation guidelines (including selecting an appropriate set of types to annotate), then annotating a large corpus of events found in Twitter. This approach has several drawbacks, as it is apriori unclear what set of types should be annotated; a large amount of e? ort would be required to manually annotate a corpus of events while simultaneously re? ning annotation standards. We propose an approach to open-domain eve nt categorization based on latent variable models that uncovers an appropriate set of types which match the data.The automatically discovered types are subsequently inspected to ? lter out any which are incoherent and the rest are annotated with informative labels;2 examples of types discovered using our approach are listed in ? gure 3. The resulting set of types are then applied to categorize hundreds of millions of extracted events without the use of any manually annotated examples. By leveraging large quantities of unlabeled data, our approach results in a 14% improvement in F1 score over a supervised baseline which uses the same set of types. Stanford NER T-seg P 0. 62 0. 73 R 0. 5 0. 61 F1 0. 44 0. 67 F1 inc. 52% Table 2: By training on in-domain data, we obtain a 52% improvement in F1 score over the Stanford Named Entity Recognizer at segmenting entities in Tweets [46]. 2. SYSTEM OVERVIEW TwiCal extracts a 4-tuple representation of events which includes a named entity, event p hrase, calendar date, and event type (see Table 1). This representation was chosen to closely match the way important events are typically mentioned in Twitter. An overview of the various components of our system for extracting events from Twitter is presented in Figure 1.Given a raw stream of tweets, our system extracts named entities in association with event phrases and unambiguous dates which are involved in signi? cant events. First the tweets are POS tagged, then named entities and event phrases are extracted, temporal expressions resolved, and the extracted events are categorized into types. Finally we measure the strength of association between each named entity and date based on the number of tweets they co-occur in, in order to determine whether an event is signi? cant.NLP tools, such as named entity segmenters and part of speech taggers which were designed to process edited texts (e. g. news articles) perform very poorly when applied to Twitter text due to its noisy and u nique style. To address these issues, we utilize a named entity tagger and part of speech tagger trained on in-domain Twitter data presented in previous work [46]. We also develop an event tagger trained on in-domain annotated data as described in  §4. 3. NAMED ENTITY SEGMENTATION NLP tools, such as named entity segmenters and part of speech taggers which were designed to process edited texts (e. g. ews articles) perform very poorly when applied to Twitter text due to its noisy and unique style. For instance, capitalization is a key feature for named entity extraction within news, but this feature is highly unreliable in tweets; words are often capitalized simply for emphasis, and named entities are often left all lowercase. In addition, tweets contain a higher proportion of out-ofvocabulary words, due to Twitter’s 140 character limit and the creative spelling of its users. To address these issues, we utilize a named entity tagger trained on in-domain Twitter data presented in previous work [46]. Training on tweets vastly improves performance at segmenting Named Entities. For example, performance compared against the state-of-the-art news-trained Stanford Named Entity Recognizer [17] is presented in Table 2. Our system obtains a 52% increase in F1 score over the Stanford Tagger at segmenting named entities. 4. EXTRACTING EVENT MENTIONS This annotation and ? ltering takes minimal e? ort. One of the authors spent roughly 30 minutes inspecting and annotating the automatically discovered event types. 2 In order to extract event mentions from Twitter’s noisy text, we ? st annotate a corpus of tweets, which is then 3 Available at http://github. com/aritter/twitter_nlp. Temporal Resolution S M T W T F S Tweets POS Tag NER Signi? cance Ranking Calendar Entries Event Tagger Event Classi? cation Figure 1: Processing pipeline for extracting events from Twitter. New components developed as part of this work are shaded in grey. used to train sequence models to extract events. While we apply an established approach to sequence-labeling tasks in noisy text [46, 31, 19], this is the ? rst work to extract eventreferring phrases in Twitter.Event phrases can consist of many di? erent parts of speech as illustrated in the following examples: †¢ Verbs: Apple to Announce iPhone 5 on October 4th?! YES! †¢ Nouns: iPhone 5 announcement coming Oct 4th †¢ Adjectives: WOOOHOO NEW IPHONE TODAY! CAN’T WAIT! These phrases provide important context, for example extracting the entity, Steve Jobs and the event phrase died in connection with October 5th, is much more informative than simply extracting Steve Jobs. In addition, event mentions are helpful in upstream tasks such as categorizing events into types, as described in  §6.In order to build a tagger for recognizing events, we annotated 1,000 tweets (19,484 tokens) with event phrases, following annotation guidelines similar to those developed for the Event tags in Timebank [43] . We treat the problem of recognizing event triggers as a sequence labeling task, using Conditional Random Fields for learning and inference [24]. Linear Chain CRFs model dependencies between the predicted labels of adjacent words, which is bene? cial for extracting multi-word event phrases.We use contextual, dictionary, and orthographic features, and also include features based on our Twitter-tuned POS tagger [46], and dictionaries of event terms gathered from WordNet by Sauri et al. [50]. The precision and recall at segmenting event phrases are reported in Table 3. Our classi? er, TwiCal-Event, obtains an F-score of 0. 64. To demonstrate the need for in-domain training data, we compare against a baseline of training our system on the Timebank corpus. precision 0. 56 0. 48 0. 24 recall 0. 74 0. 70 0. 11 F1 0. 64 0. 57 0. 15 TwiCal-Event No POS TimebankTable 3: Precision and recall at event phrase extraction. All results are reported using 4-fold cross validation over the 1,000 manu ally annotated tweets (about 19K tokens). We compare against a system which doesn’t make use of features generated based on our Twitter trained POS Tagger, in addition to a system trained on the Timebank corpus which uses the same set of features. as input a reference date, some text, and parts of speech (from our Twitter-trained POS tagger) and marks temporal expressions with unambiguous calendar references. Although this mostly rule-based system was designed for use on newswire text, we ? d its precision on Tweets (94% estimated over as sample of 268 extractions) is su? ciently high to be useful for our purposes. TempEx’s high precision on Tweets can be explained by the fact that some temporal expressions are relatively unambiguous. Although there appears to be room for improving the recall of temporal extraction on Twitter by handling noisy temporal expressions (for example see Ritter et. al. [46] for a list of over 50 spelling variations on the word â€Å"tomorrow †), we leave adapting temporal extraction to Twitter as potential future work. . CLASSIFICATION OF EVENT TYPES To categorize the extracted events into types we propose an approach based on latent variable models which infers an appropriate set of event types to match our data, and also classi? es events into types by leveraging large amounts of unlabeled data. Supervised or semi-supervised classi? cation of event categories is problematic for a number of reasons. First, it is a priori unclear which categories are appropriate for Twitter. Secondly, a large amount of manual e? ort is required to annotate tweets with event types.Third, the set of important categories (and entities) is likely to shift over time, or within a focused user demographic. Finally many important categories are relatively infrequent, so even a large annotated dataset may contain just a few examples of these categories, making classi? cation di? cult. For these reasons we were motivated to investigate un- 5. EXTRACTING AND RESOLVING TEMPORAL EXPRESSIONS In addition to extracting events and related named entities, we also need to extract when they occur. In general there are many di? rent ways users can refer to the same calendar date, for example â€Å"next Friday†, â€Å"August 12th†, â€Å"tomorrow† or â€Å"yesterday† could all refer to the same day, depending on when the tweet was written. To resolve temporal expressions we make use of TempEx [33], which takes Sports Party TV Politics Celebrity Music Movie Food Concert Performance Fitness Interview ProductRelease Meeting Fashion Finance School AlbumRelease Religion 7. 45% 3. 66% 3. 04% 2. 92% 2. 38% 1. 96% 1. 92% 1. 87% 1. 53% 1. 42% 1. 11% 1. 01% 0. 95% 0. 88% 0. 87% 0. 85% 0. 85% 0. 78% 0. 71% Con? ct Prize Legal Death Sale VideoGameRelease Graduation Racing Fundraiser/Drive Exhibit Celebration Books Film Opening/Closing Wedding Holiday Medical Wrestling OTHER 0. 69% 0. 68% 0. 67% 0. 66% 0. 66% 0. 65 % 0. 63% 0. 61% 0. 60% 0. 60% 0. 60% 0. 58% 0. 50% 0. 49% 0. 46% 0. 45% 0. 42% 0. 41% 53. 45% Label Sports Concert Perform TV Movie Sports Politics Figure 2: Complete list of automatically discovered event types with percentage of data covered. Interpretable types representing signi? cant events cover roughly half of the data. supervised approaches that will automatically induce event types which match the data.We adopt an approach based on latent variable models inspired by recent work on modeling selectional preferences [47, 39, 22, 52, 48], and unsupervised information extraction [4, 55, 7]. Each event indicator phrase in our data, e, is modeled as a mixture of types. For example the event phrase â€Å"cheered† might appear as part of either a PoliticalEvent, or a SportsEvent. Each type corresponds to a distribution over named entities n involved in speci? c instances of the type, in addition to a distribution over dates d on which events of the type occur. Including calen dar dates in our model has the e? ct of encouraging (though not requiring) events which occur on the same date to be assigned the same type. This is helpful in guiding inference, because distinct references to the same event should also have the same type. The generative story for our data is based on LinkLDA [15], and is presented as Algorithm 1. This approach has the advantage that information about an event phrase’s type distribution is shared across it’s mentions, while ambiguity is also naturally preserved. In addition, because the approach is based on generative a probabilistic model, it is straightforward to perform many di? rent probabilistic queries about the data. This is useful for example when categorizing aggregate events. For inference we use collapsed Gibbs Sampling [20] where each hidden variable, zi , is sampled in turn, and parameters are integrated out. Example types are displayed in Figure 3. To estimate the distribution over types for a given event , a sample of the corresponding hidden variables is taken from the Gibbs markov chain after su? cient burn in. Prediction for new data is performed using a streaming approach to inference [56]. TV Product MeetingTop 5 Event Phrases tailgate – scrimmage tailgating – homecoming – regular season concert – presale – performs – concerts – tickets matinee – musical priscilla – seeing wicked new season – season ? nale – ? nished season episodes – new episode watch love – dialogue theme – inception – hall pass – movie inning – innings pitched – homered homer presidential debate osama – presidential candidate – republican debate – debate performance network news broadcast – airing – primetime drama – channel stream unveils – unveiled – announces – launches wraps o? shows trading – hall mtg – zoning – brie? g stocks – tumbled – trading report – opened higher – tumbles maths – english test exam – revise – physics in stores – album out debut album – drops on – hits stores voted o? – idol – scotty – idol season – dividendpaying sermon – preaching preached – worship preach declared war – war shelling – opened ? re wounded senate – legislation – repeal – budget – election winners – lotto results enter – winner – contest bail plea – murder trial – sentenced – plea – convicted ? lm festival – screening starring – ? lm – gosling live forever – passed away – sad news – condolences – burried add into – 50% o? up shipping – save up donate – tornado relief disaster relief – donated – raise mone y Top 5 Entities espn – ncaa – tigers – eagles – varsity taylor swift – toronto britney spears – rihanna – rock shrek – les mis – lee evans – wicked – broadway jersey shore – true blood – glee – dvr – hbo net? ix – black swan – insidious – tron – scott pilgrim mlb – red sox – yankees – twins – dl obama president obama – gop – cnn america nbc – espn – abc – fox mtv apple – google – microsoft – uk – sony town hall – city hall club – commerce – white house reuters – new york – u. . – china – euro english – maths – german – bio – twitter itunes – ep – uk – amazon – cd lady gaga – american idol – america – beyonce – glee church – jesus – pastor faith – god libya – afghanistan #syria – syria – nato senate – house – congress – obama – gop ipad – award – facebook – good luck – winners casey anthony – court – india – new delhi supreme court hollywood – nyc – la – los angeles – new york michael jackson afghanistan john lennon – young – peace groupon – early bird facebook – @etsy – etsy japan – red cross – joplin – june – africaFinance School Album TV Religion Con? ict Politics Prize Legal Movie Death Sale Drive 6. 1 Evaluation To evaluate the ability of our model to classify signi? cant events, we gathered 65 million extracted events of the form Figure 3: Example event types discovered by our model. For each type t, we list the top 5 entities which have highest probability given t, and the 5 event phrases which as sign highest probability to t. Algorithm 1 Generative story for our data involving event types as hidden variables.Bayesian Inference techniques are applied to invert the generative process and infer an appropriate set of types to describe the observed events. for each event type t = 1 . . . T do n Generate ? t according to symmetric Dirichlet distribution Dir(? n ). d Generate ? t according to symmetric Dirichlet distribution Dir(? d ). end for for each unique event phrase e = 1 . . . |E| do Generate ? e according to Dirichlet distribution Dir(? ). for each entity which co-occurs with e, i = 1 . . . Ne do n Generate ze,i from Multinomial(? e ). Generate the entity ne,i from Multinomial(? n ). e,i TwiCal-Classify Supervised Baseline Precision 0. 85 0. 61 Recall 0. 55 0. 57 F1 0. 67 0. 59 Table 4: Precision and recall of event type categorization at the point of maximum F1 score. d,i end for end for 0. 6 end for for each date which co-occurs with e, i = 1 . . . Nd do d Generate ze,i from Multinomial(? e ). Generate the date de,i from Multinomial(? zn ). Precision 0. 8 1. 0 listed in Figure 1 (not including the type). We then ran Gibbs Sampling with 100 types for 1,000 iterations of burnin, keeping the hidden variable assignments found in the last sample. One of the authors manually inspected the resulting types and assigned them labels such as Sports, Politics, MusicRelease and so on, based on their distribution over entities, and the event words which assign highest probability to that type. Out of the 100 types, we found 52 to correspond to coherent event types which referred to signi? cant events;5 the other types were either incoherent, or covered types of events which are not of general interest, for example there was a cluster of phrases such as applied, call, contact, job interview, etc†¦ hich correspond to users discussing events related to searching for a job. Such event types which do not correspond to signi? cant events of general interest were simply marked as OTHER. A complete list of labels used to annotate the automatically discovered event types along with the coverage of each type is listed in ? gure 2. Note that this assignment of labels to types only needs to be done once and produces a labeling for an arbitrarily large number of event instances. Additionally the same set of types can easily be used to lassify new event instances using streaming inference techniques [56]. One interesting direction for future work is automatic labeling and coherence evaluation of automatically discovered event types analogous to recent work on topic models [38, 25]. In order to evaluate the ability of our model to classify aggregate events, we grouped together all (entity,date) pairs which occur 20 or more times the data, then annotated the 500 with highest association (see  §7) using the event types discovered by our model. To help demonstrate the bene? s of leveraging large quantities of unlabeled data for event classi? cation, we compare against a supervised Maximum Entropy baseline which makes use of the 500 annotated events using 10-fold cross validation. For features, we treat the set of event phrases To scale up to larger datasets, we performed inference in parallel on 40 cores using an approximation to the Gibbs Sampling procedure analogous to that presented by Newmann et. al. [37]. 5 After labeling some types were combined resulting in 37 distinct labels. 4 0. 4 Supervised Baseline TwiCal? Classify 0. 0 0. 2 0. 4 Recall 0. 0. 8 Figure 4: types. Precision and recall predicting event that co-occur with each (entity, date) pair as a bag-of-words, and also include the associated entity. Because many event categories are infrequent, there are often few or no training examples for a category, leading to low performance. Figure 4 compares the performance of our unsupervised approach to the supervised baseline, via a precision-recall curve obtained by varying the threshold on the probability of the most lik ely type. In addition table 4 compares precision and recall at the point of maximum F-score.Our unsupervised approach to event categorization achieves a 14% increase in maximum F1 score over the supervised baseline. Figure 5 plots the maximum F1 score as the amount of training data used by the baseline is varied. It seems likely that with more data, performance will reach that of our approach which does not make use of any annotated events, however our approach both automatically discovers an appropriate set of event types and provides an initial classi? er with minimal e? ort, making it useful as a ? rst step in situations where annotated data is not immediately available. . RANKING EVENTS Simply using frequency to determine which events are signi? cant is insu? cient, because many tweets refer to common events in user’s daily lives. As an example, users often mention what they are eating for lunch, therefore entities such as McDonalds occur relatively frequently in associat ion with references to most calendar days. Important events can be distinguished as those which have strong association with a unique date as opposed to being spread evenly across days on the calendar. To extract signi? ant events of general interest from Twitter, we thus need some way to measure the strength of association between an entity and a date. In order to measure the association strength between an 0. 8 0. 2 Supervised Baseline TwiCal? Classify 100 200 300 400 tweets. We then added the extracted triples to the dataset used for inferring event types described in  §6, and performed 50 iterations of Gibbs sampling for predicting event types on the new data, holding the hidden variables in the original data constant. This streaming approach to inference is similar to that presented by Yao et al. 56]. We then ranked the extracted events as described in  §7, and randomly sampled 50 events from the top ranked 100, 500, and 1,000. We annotated the events with 4 separate criter ia: 1. Is there a signi? cant event involving the extracted entity which will take place on the extracted date? 2. Is the most frequently extracted event phrase informative? 3. Is the event’s type correctly classi? ed? 4. Are each of (1-3) correct? That is, does the event contain a correct entity, date, event phrase, and type? Note that if (1) is marked as incorrect for a speci? event, subsequent criteria are always marked incorrect. Max F1 0. 4 0. 6 # Training Examples Figure 5: Maximum F1 score of the supervised baseline as the amount of training data is varied. entity and a speci? c date, we utilize the G log likelihood ratio statistic. G2 has been argued to be more appropriate for text analysis tasks than ? 2 [12]. Although Fisher’s Exact test would produce more accurate p-values [34], given the amount of data with which we are working (sample size greater than 1011 ), it proves di? cult to compute Fisher’s Exact Test Statistic, which results in ? ating poin t over? ow even when using 64-bit operations. The G2 test works su? ciently well in our setting, however, as computing association between entities and dates produces less sparse contingency tables than when working with pairs of entities (or words). The G2 test is based on the likelihood ratio between a model in which the entity is conditioned on the date, and a model of independence between entities and date references. For a given entity e and date d this statistic can be computed as follows: G2 = x? {e, ¬e},y? {d, ¬d} 2 8. 2 BaselineTo demonstrate the importance of natural language processing and information extraction techniques in extracting informative events, we compare against a simple baseline which does not make use of the Ritter et. al. named entity recognizer or our event recognizer; instead, it considers all 1-4 grams in each tweet as candidate calendar entries, relying on the G2 test to ? lter out phrases which have low association with each date. 8. 3 Results The results of the evaluation are displayed in table 5. The table shows the precision of the systems at di? rent yield levels (number of aggregate events). These are obtained by varying the thresholds in the G2 statistic. Note that the baseline is only comparable to the third column, i. e. , the precision of (entity, date) pairs, since the baseline is not performing event identi? cation and classi? cation. Although in some cases ngrams do correspond to informative calendar entries, the precision of the ngram baseline is extremely low compared with our system. In many cases the ngrams don’t correspond to salient entities related to events; they often consist of single words which are di? ult to interpret, for example â€Å"Breaking† which is part of the movie â€Å"Twilight: Breaking Dawn† released on November 18. Although the word â€Å"Breaking† has a strong association with November 18, by itself it is not very informative to present to a user. 7 Our high- con? dence calendar entries are surprisingly high quality. If we limit the data to the 100 highest ranked calendar entries over a two-week date range in the future, the precision of extracted (entity, date) pairs is quite good (90%) – an 80% increase over the ngram baseline.As expected precision drops as more calendar entries are displayed, but 7 In addition, we notice that the ngram baseline tends to produce many near-duplicate calendar entries, for example: â€Å"Twilight Breaking†, â€Å"Breaking Dawn†, and â€Å"Twilight Breaking Dawn†. While each of these entries was annotated as correct, it would be problematic to show this many entries describing the same event to a user. Ox,y ? ln Ox,y Ex,y Where Oe,d is the observed fraction of tweets containing both e and d, Oe, ¬d is the observed fraction of tweets containing e, but not d, and so on.Similarly Ee,d is the expected fraction of tweets containing both e and d assuming a model of independence. 8. EXPERIMENTS To estimate the quality of the calendar entries generated using our approach we manually evaluated a sample of the top 100, 500 and 1,000 calendar entries occurring within a 2-week future window of November 3rd. 8. 1 Data For evaluation purposes, we gathered roughly the 100 million most recent tweets on November 3rd 2011 (collected using the Twitter Streaming API6 , and tracking a broad set of temporal keywords, including â€Å"today†, â€Å"tomorrow†, names of weekdays, months, etc. ).We extracted named entities in addition to event phrases, and temporal expressions from the text of each of the 100M 6 https://dev. twitter. com/docs/streaming-api Mon Nov 7 Justin meet Other Motorola Pro+ kick Product Release Nook Color 2 launch Product Release Eid-ul-Azha celebrated Performance MW3 midnight release Other Tue Nov 8 Paris love Other iPhone holding Product Release Election Day vote Political Event Blue Slide Park listening Music Release Hedley album Music Rele ase Wed Nov 9 EAS test Other The Feds cut o? Other Toca Rivera promoted Performance Alert System test Other Max Day give OtherNovember 2011 Thu Nov 10 Fri Nov 11 Robert Pattinson iPhone show debut Performance Product Release James Murdoch Remembrance Day give evidence open Other Performance RTL-TVI France post play TV Event Other Gotti Live Veterans Day work closed Other Other Bambi Awards Skyrim perform arrives Performance Product Release Sat Nov 12 Sydney perform Other Pullman Ballroom promoted Other Fox ? ght Other Plaza party Party Red Carpet invited Party Sun Nov 13 Playstation answers Product Release Samsung Galaxy Tab launch Product Release Sony answers Product Release Chibi Chibi Burger other Jiexpo Kemayoran promoted TV EventFigure 6: Example future calendar entries extracted by our system for the week of November 7th. Data was collected up to November 5th. For each day, we list the top 5 events including the entity, event phrase, and event type. While there are several err ors, the majority of calendar entries are informative, for example: the Muslim holiday eid-ul-azha, the release of several videogames: Modern Warfare 3 (MW3) and Skyrim, in addition to the release of the new playstation 3D display on Nov 13th, and the new iPhone 4S in Hong Kong on Nov 11th. # calendar entries 100 500 1,000 ngram baseline 0. 50 0. 6 0. 44 entity + date 0. 90 0. 66 0. 52 precision event phrase event 0. 86 0. 56 0. 42 type 0. 72 0. 54 0. 40 entity + date + event + type 0. 70 0. 42 0. 32 Table 5: Evaluation of precision at di? erent recall levels (generated by varying the threshold of the G2 statistic). We evaluate the top 100, 500 and 1,000 (entity, date) pairs. In addition we evaluate the precision of the most frequently extracted event phrase, and the predicted event type in association with these calendar entries. Also listed is the fraction of cases where all predictions (â€Å"entity + date + event + type†) are correct.We also compare against the precision of a simple ngram baseline which does not make use of our NLP tools. Note that the ngram baseline is only comparable to the entity+date precision (column 3) since it does not include event phrases or types. remains high enough to display to users (in a ranked list). In addition to being less likely to come from extraction errors, highly ranked entity/date pairs are more likely to relate to popular or important events, and are therefore of greater interest to users. In addition we present a sample of extracted future events on a calendar in ? ure 6 in order to give an example of how they might be presented to a user. We present the top 5 entities associated with each date, in addition to the most frequently extracted event phrase, and highest probability event type. 9. RELATED WORK While we are the ? rst to study open domain event extraction within Twitter, there are two key related strands of research: extracting speci? c types of events from Twitter, and extracting open-domain even ts from news [43]. Recently there has been much interest in information extraction and event identi? cation within Twitter. Benson et al. 5] use distant supervision to train a relation extractor which identi? es artists and venues mentioned within tweets of users who list their location as New York City. Sakaki et al. [49] train a classi? er to recognize tweets reporting earthquakes in Japan; they demonstrate their system is capable of recognizing almost all earthquakes reported by the Japan Meteorological Agency. Additionally there is recent work on detecting events or tracking topics [29] in Twitter which does not extract structured representations, but has the advantage that it is not limited to a narrow domain. Petrovi? t al. investigate a streaming approach to identic fying Tweets which are the ? rst to report a breaking news story using Locally Sensitive Hash Functions [40]. Becker et al. [3], Popescu et al. [42, 41] and Lin et al. [28] investigate discovering clusters of rela ted words or tweets which correspond to events in progress. In contrast to previous work on Twitter event identi? cation, our approach is independent of event type or domain and is thus more widely applicable. Additionally, our work focuses on extracting a calendar of events (including those occurring in the future), extract- . 4 Error Analysis We found 2 main causes for why entity/date pairs were uninformative for display on a calendar, which occur in roughly equal proportion: Segmentation Errors Some extracted â€Å"entities† or ngrams don’t correspond to named entities or are generally uninformative because they are mis-segmented. Examples include â€Å"RSVP†, â€Å"Breaking† and â€Å"Yikes†. Weak Association between Entity and Date In some cases, entities are properly segmented, but are uninformative because they are not strongly associated with a speci? c event on the associated date, or are involved in many di? rent events which happen to oc cur on that day. Examples include locations such as â€Å"New York†, and frequently mentioned entities, such as â€Å"Twitter†. ing event-referring expressions and categorizing events into types. Also relevant is work on identifying events [23, 10, 6], and extracting timelines [30] from news articles. 8 Twitter status messages present both unique challenges and opportunities when compared with news articles. Twitter’s noisy text presents serious challenges for NLP tools. On the other hand, it contains a higher proportion of references to present and future dates.Tweets do not require complex reasoning about relations between events in order to place them on a timeline as is typically necessary in long texts containing narratives [51]. Additionally, unlike News, Tweets often discus mundane events which are not of general interest, so it is crucial to exploit redundancy of information to assess whether an event is signi? cant. Previous work on open-domain informat ion extraction [2, 53, 16] has mostly focused on extracting relations (as opposed to events) from web corpora and has also extracted relations based on verbs.In contrast, this work extracts events, using tools adapted to Twitter’s noisy text, and extracts event phrases which are often adjectives or nouns, for example: Super Bowl Party on Feb 5th. Finally we note that there has recently been increasing interest in applying NLP techniques to short informal messages such as those found on Twitter. 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