Explosion Of The Digital Era Public Relations

Explosion Of The Digital Era Public Relations Public Engagement is a modern paradigm in the PR context and sets out how professionals should be listening and engaging with their key stakeholders in the 21st Century. Marshall Manson, Director of digital strategy at Edelman UK, describes the terminology as advancing shared interest moving from an influence pyramid to a world of cross-influence (pg 5). According to Edelman there are four attributes of Public Engagement; it aims to be democratic and decentralized, inform the conversation, call for engagement with stakeholders and finally make it clear how reputation is important. Public Engagement has to work in all four areas (Richard Edelman, 2008). Trust building both internally and externally, reputation management and transparency are the hallmarks of successful PR initiatives besides positioning the CEO. Jean-Jacques Rousseau, John Stuart Mill and G D H Cole (1968) came up with the term Participatory Democracy, which was the forerunner of Public Engagement. Mills supported a representative government with some form of public participation but on a limited scale. Wholesale participation could endanger political decision making and hence democracy. (Pateman1970) Engagement in politics had been regarded as reactive but it is now perceived as proactive. Dialogue and engagement with all parties is important to prevent loss of public trust (Edward Andersson, Simon Burall Emily Fennell, Involve 2010). The Big Society introduced by David Cameron 19 July 2010 sought public dialogue and involvement in decision making. Business and government rely on PR to establish trust and dialogue. Public engagement, like any conversation, is all about listening. Our world can be likened to a conversation and in order to be successful we need to listen ( Richard Edelman 2008). New technology has presented the public with effective tools for engaging in these conversations at a level never experienced before. A white paper published by Arthur W Page Society (2007) is a good example of how Public Relations practitioners and company leaders must change their strategies and business goals to be more authentic in the 21st century. The study looked at how CEOs saw their roles change with the culture of the environment. Environment is changing because of the emergence of all things digital, a global economy and a new breed of stakeholders. However, what it fails to do is to provide a way that can be used to enhance the role of the communication department in the future. According to the paper it believes that communication departments have lost control. In order to move forward they must adapt to new technologies, audiences and engagement models. Transparency is a key part of performing well in the 21st century, it encourages trust on behalf of employees and the public and is in demand more than ever (Michael Smith, Richard Hunter, Ken McGee, Gartner 2010). Public opinion surveys have confirmed the need for transparency in business and managing trust (Grunig, J.E 2009). For organisations to succeed in Public Engagement, they need to be informed, transparent, prospective and adaptive to their key stakeholders (Royal Commission on Environmental Pollution, 2008). Prior to current publications on Public Engagement, the term was mainly connected to the science world. A report written by Demos (2009) stated that the UK was now seen as a leader in public engagement within the science world. Public Engagement, it argued would only work when interest groups were included. Matthew C. Nisbet Dietram A. Scheufele (2007) argued that communication must be accessible to all sections of society. Advocacy, a term used by PR company Weber Shandwick, is defined as Public relations caught the first wave, the adoption of new technology to spread information But that first wave, sharing information with more segmented audiences, is cresting. A new one, a fundamental transformation of communication from information to advocacy, is rising (Jack Leslie, Chairman, Weber Shandwick Worldwide). It argues that personnel engagement is needed in order to be able to communicate with their audiences. It is evident that universities are becoming involved with the term Public Engagement. The National Co-ordinating Centre for Public Engagement (NCCPE) is part of the Beacon for Public Engagement project. Their aim is to encourage a change in how universities engage with the public and their stakeholders. ___________________________________________________________________________ Social Media Traditional PR skills are still helpful in bringing transparency to communications with stakeholders (Schlesinger 2010) but the introduction of Web 2.0 technologies has changed the world of communications for PR (Pavlik 2008) and Philips and Young (2009). Grunig, J.E (2009) noted that digital media has the potential to make the profession more global, strategic, two-way, interactive and socially responsible (pg 1 Paradigms of Global Public Relations in an Age of Digitalisation). However, he comments that this new media is being used ineffectively by practitioners. Some are using it, as they did with the old media, as a place to write messages rather than using it to interact with the public strategically. Grunig (2009) concludes that it can only be used effectively if social media is used to its full capacity. Public engagement has created new networks of influences and special online forums leading to a world of communication through Blog, Twitter and Facebook among many, impacting all walks of life (Edelman, 2009.) In 2009, 625 million people worldwide had access to the internet (McCann 2009). Sharing of social networking sites has created huge paradigms for PR. It has increased challenges for organisations for the crucial role played by transparency, quickness and clarity in response (Pavlik 2008). The rise in digital interaction and the surge in social media present the practitioners with enormous opportunities. According to McCanns Wave 4 report (2009), social media platforms are becoming the norm to create and share. In his study he noted how they presented unique opportunities to listen and observe. One way messaging is now outdated and anyone understanding this new communication world will succeed (Weber Shandwick). It is important to recognize that social media is also important within organizations, encouraging employee engagement. Enterprise 2.0 is a form of web 2.0 which is used for businesses only. They provide services such as Yammer, a corporate version of Twitter, and Chatter- a social-networking service (Economist 2010). According to Mashable (2010) the top five engaged brands in social media are Starbucks, Coca- Cola, Oreo, Skittles and Redbull. Starbucks have created a digital platform for the participation of public through My Starbucks Idea for communicating with customers by just listening to customers suggestions. Companies are benefitting from listening and improving their services for all stakeholders including customers. Richard Sambrook (the former head of BBC news) believes that every company should be a media company he says. Big companies are going directly to the consumer to engage them now, rather than through display or spot ads and the traditional means of trying to reach consumers. You cant just be out there shouting at people about your brand, youve got to engage with them quite carefully PR is there to help organisations to be honest and engaged with the Public in this new age of media and should not spin stories to the press (Independent 2010) A Stakeholder can be defined as anyone who is affected by the actions arising from any organisation, whether a public or private entity. When engaged, stakeholders can provide organisations with valuable feedback on society expectations leading to the generation of creative solutions and earning the organisation valuable stakeholder support (Lawrence Weber 2008). With the advent of technologically enabled tools there has been a democratisation and socialisation of the media leading to a greater involvement by all participants. Breaking news is today likely to be captured first on Twitter, or the like, rather than a news agency (WrightHinson 2009). This in turn has necessitated greater transparency and in turn enhanced the role of PR professional teams in the management of organisations day to day operations. The status of employees and customers has now been placed on an equal footing with shareholders and other governing participants. (Authentic Enterprise 2007). Evolving through p ublic engagement, the corporate communication function is set to play the role of catalyst in this new environment and the PR practitioner can help empower corporate culture and stakeholder confidence (Miller 2010). At a local level internal communication has become more significant in shaping the management and image of organisations (Authentic Enterprise 2007). Trust is a valuable commodity which needs to be nurtured; in times of crisis the trust relationship will play a significant role in shaping the outcome of adverse events. Encouraging a listening culture within management will enhance the perception of trust (Mazzei and Ravazzani 2010). This is a function that PR needs to develop to accommodate changing communication platforms and models. Strategies need to consider the social and behavioural changes brought about by the internet. There have been many communication models put forward over the past 60 years the principal ones being the Shannon and Weaver model 1949, Shramms Interactive Model 1954 and Grunigs and Hunts 4 Models (1984). It is important to look at communication models to see whether Public engagement is a new paradigm or simply an extension of these. Shannon and Wavers (1949) Mathematical theory of Communication is accepted as one of the most important models from which communication studies has grown ( Johnson and Klare 1961), albeit biased towards the technical aspects of communication research (John Fiske 1982). In contrast to the above models Schramm went on to create his model which emphasised two-way communication (1954) and introduced the concept of feedback. In problem solving scenarios open ended questions need to be asked in order to gain feedback and results. The two-way symmetric model, Grunig and Hunt (1984) is probably the most popular and widely used in todays PR industry. This model is described as being one that can build relationships and resolve conflicts. It is where the stakeholders have a say in what an organisation does and can have some power over policies. This model employs research, listening, and dialogue as tools to cultivate relationships with both internal and external strategic parties. Pieczka (1996) criticised the model as she felt that the study was biased to the two way symmetrical model and questioned the validity of his model. According to Phillips and Young( 2009),social media is causing a new communication paradigm. They believe that communication is shifting from the traditional hierarchical arrangement to an audience model encouraging horizontal discourse. This is confirmed by Edelman (2009). A further study by Grunig developed a model for excellent public relations ( Grunig 2002 ,Grunig Dozier ) It was found that the most successful PR departments took part or contributed to the strategic decisions of the company. Once stakeholders had been identified the study showed that successful PR departments developed programmes to communicate with them. According to Grunig (2002.) Excellence is seen as the balance between an organisation and the public. It was seen as the management of communications on a level that aided the strategic management role whilst maintaining the attention and cooperation of the public. As such the PR role was enhanced and perceived in commercial terms whereby long term relationships could be forged between strategic parties. Philip and Young (2009 ) argued that the new PR challenges the Excellence model. LEtang (2006) was also critical stating that the power imbalance between organisation and public was a flaw in the theory of two-way communication. However Grunig insisted that the model was revised over the years. There appears to be a gap in communication models in regards to Public Engagement although Grunigs fourth model is possibly the closest to it. It is evident that a new model of PR is needed. Whether it is to be called Public Engagement is something which has to be decided.

Milk Brands In Mauritius

Milk Brands In Mauritius Literature review is a critical and an evaluative summary of the themes, issues, and arguments of a specific clearly defined research topic. The aim of this chapter is to review the points of findings about the title how milk brands affect the purchasing decisions of consumers. 2.1 Brief History on Milk Brands in Mauritius Mauritius imports milk powders from Australia and New Zealand. The milk powders have different brand names such as Farmland, Twin cow and others all depend the countries, which it come from. Once it reaches the home country, it is being channeled to two or more industries in Mauritius for its packaging. Then, it is being sold in bulk to shops, super and hypermarkets. Companies such as Innodis Ltd, ISO Pack Ltd, Eden vale Distributors Ltd, and other pack the different milk brands and distribute them. However, Mauritius imports milk from other countries because its consumption has rapidly been increased. In addition, importation of milk is becoming necessary for Mauritius due to increase in demand of branded milk by Mauritian clients. Regardless to the price and quantity, quality of the product has always been the main factor for customers in their product selection. Branding plays a key role in the recognition of the product. The annual consumption of milk in Mauritius is approximately nine millions litres which is equivalent to 12% of the total consumption of different brand names (Hulman et al., 1990). There is a rise in the demand for milk in Mauritius. In 2013, the Mauritian customers are moving towards mostly Farmland due to its quality and advertisement on T.V and radio. In our island, the brand names for milk have substitutes due to the different brand names available on the market. Thus, if we are not satisfied with a particular milk brand, we always have the choice to opt for another brand. According to AGA Webmaster FOA (2013), the aim is to ensure milk brand marketed in Mauritius are affordable and the emphasis on Human Resources Development (H.R.D) and provision of inputs and services to enhance production and milk brand processing to facilitate marketing in Mauritius. In the year 1971, the Milk and Meat Project Food and Agriculture Organization (FAO) interpreted that, there is a lack of supplement limited milk production. Furthermore, it was not determined whether it was energy or protein in the supplement that was important, and the basal diet of cane tops and grasses was not evaluated. The FAO has proved that milk yield could be increased significantly by better feeding and management. 2.2 Elements That Influences Consumers Towards Milk Brand Through advertising, design and media commentary milk brands have made its place in the market in Mauritius. This leads branding to give an image of the product to consumers to make a purchasing decision. As per Pearce (2013), the elements that influence consumers are as follows: NAME LOGO TASTE FONTS COLOR SCHEME PACKAGE GRAPHICS SHAPES These points above shared an advance information about implicit values, ideas, benefits and as well as it developed the personality of the consumers while buying the product. Consumers face purchasing decisions nearly every day. Hence, they established a willingness to purchase brand products. Consumers purchasing decisions of brand milk have created certain attributes and interactions in recent years using several methods by means of both qualitative and quantitative (Anon, n.d). 2.3 The Importance Of Branding When Consumers Take Purchasing Decision According to Kotler (1994), Branding is the best way to establish the authority, niche and credibility and authority of individual and business. In other words branding is not only convenient for business or for repeated customer but also easier for others to filter out the countless generic items. The bargaining power of Mauritius is limited. Recently, in an article published on the website businessmega, it was found that about 10,000 metric tons of milk powder is imported on a yearly basis depending on demand and consumption, which is less in quantity in comparison to other countries. The Executive director of La Trobe ltd, Mr. L. Wong and the supplier of Snowy milk stated that local distributors have the entire influence over the prices (Anon, 2011). The importance of branding is as follows: Branding communicates information about the business. Share all types of information about the product to the market. It establishes an identity in order for consumers to recognize the product very well. Branding gives consumers the assurance that the entire products they buy are trustful and enable positive responses from the latter. Branding gives a strategic position in the market and through this, company eventually leads to increased profits. Branding shows an advance details for example: Cost of the products Packaging Marketing and advertising strategies Distribution channels and so on. Branding is the powerful factor in marketing, which helps consumers in taking purchasing decision. When the client is satisfied with a given branded product, they tend to revert to the same brand supplier, (Badgujar (Roll No.04)). 2.4 Brand Awareness To Consumers Donald (2010) defined brand awareness as the customers ability to recall and recognize the brand under different conditions and link to the brand name, logo, and so on to certain associations in memory. That is brand awareness encompasses both brand recognition and brand recall. It helps the customers to understand to which product or service category the particular brand belongs and what products and services are sold under the brand name. It also ensures that customers know which of their needs are satisfied by the brand through its products. Brand awareness is of critical importance since customers will not consider a particular brand if they are not aware of it (Donald, 2010). However, Epstein (1977) argued that human and brand personality traits share the same conceptualization but they differentiate in terms of how they are created. There are three types of brand awareness, which are as follows: Top-of-Mind Awareness occurs when the companys brand is what pops into a consumers mind when asked to name brands in a product category. For example, when someone is asked to name a type of facial tissue, the common answer is Kleenex, which is a top-of-mind brand. Aided Awareness occurs when a consumer reads a list of brands, and expresses familiarity with companys brand only after they hear or see it as a type of memory aide. Strategic Awareness occurs when the companys brand is not only top-of-mind to consumers, but also has distinctive qualities that stick out to consumers as making it better than the other brands in your market. These three types of awareness above inform consumers about some objectives that a good brand of milk product will achieve include: Motivates the buyers Concretes user loyalty Delivers the message clearly Connects the consumers target prospects emotionally Confirms the consumers credibility In Mauritius, almost everyone consumes branded milk such as Anchor, Farmland, Snowy, Red Cow, and others on a daily basis. 2.5 Milk Brand Production And Milk Pricing Branding can result in higher sales of other types and varieties of product associated with a specific branded product. Branding should also analyze by more than the difference between the actual cost of a product and its selling price and they represent the sum of all valuable qualities of a product to the consumer. The branding concept here is taking into account complexity of human behavior and benefit of consumers. Our country import milk from various countries that increase the consumption and the productivity of dairy product in Mauritius. Other countries such as Europe, Australia, and New Zealand and so on are also the exporters for Mauritius. Europe produces milk, New Zealand produces Red Cow and Australia produces Snowy milk, Farmland, Dolly and Anchor. Furthermore, the price hike of imported milk is forcing people to diminish consumption. Multiple actors show the situation will not improve and local production is the only way out. 80% of the milk consumed in Mauritius comes from Australia and New Zealand by Olivier Masson, 2 July 2007 Port Louis. According to Lake (n.d), branding is ones identity in the marketplace. She stated that, it is crucial to realize that packaging always either has a negative or positive influence on the buyer. A negative impression can detour a potential client, just like a positive reaction can influence a customer to buy. She also said that one should pay special attention to packaging when a new brand is launch. This is because many people often do not pay close attention to the packaging if it is a popular brand. How can you package your brand so that it is an integral part of your business and represents a strong identity? Keep in mind that we are not speaking that packaging has only a box, which contains a product, but as a medium, that reflects your companys brand and image. The following common business tools represent packaging: Business cards and stationery Web site Answering system Email address Food packaging is packaging for food. It requires protection, tampering resistance, and special physical, chemical, or biological needs. Milk plays an important role in peoples goal of eating healthily and having an active lifestyle. Milk manufacturers therefore are riding this trend by introducing milk brands that are aimed at health-conscious people. Extracted from the Inspiration Hive, Daily Inspiration on July 9, 2012. With the statement, packaging is a way of formulating a feasible marketing strategy for milk brand. The products presentation leads to a competitive pricing policy and a well-planned advertising campaign will convince consumers to purchase the branded milk. 2.5.2 Preservation In Mauritius, almost everyone consumes branded milk such as Anchor, Farmland, Snowy, Red Cow, and others on a daily basis. Thus, branded milk imported in bulk from other countries and conservation plays is a major factor during these periods. The process of Ultra-high temperature takes place. That is, milk preserved by UHT processing does not need to refrigerate before opening and has a longer shelf life than milk in ordinary packaging. It is sold unrefrigerated in the UK, Europe, Latin America, and Australia. This process helps trade to take place and fulfill the choice, needs, and wants of consumers. 2.6 The Objectives Of Brand In Purchasing Decision Branding is a way to communicate to consumers about the types of products available in the markets. This gives rise to some objectives that should be taken into consideration while dealing with consumers, (Kotler et al. 2001). The objectives that branding should achieve include: Delivers the message clearly Confirms consumers credibility Connects consumers target prospects emotionally Motivates the buyer Concretes user Loyalty Therefore, to succeed in branding, companies must understand the needs and wants of customers and prospects stated by Kotler and Armstrong, 2008. Therefore, by integrating the companys brand strategies through the company at every point of public contact will lead to an increase in demands of products stated by Laura Lake. Taking into consideration the objectives of brand milk in purchasing decisions, marketers also focus in marketing strategies which are; promotion and promotion mix, tools of promotion, advertising and gaining the market share. 2.6.1 Promotion And Promotion Mix Based on Kotler, 1994, promotion is the fourth marketing mix tool. It is the different activities that companies undertake to communicate and promote its products to the target market. Nowadays, companies hire advertising agencies to develop effective advertisements, sales promotion specialist to design buying-incentive programs, direct marketing specialists to build database and interact with consumers and prospect by mail and telephone, public relation firms to supply product publicity and finally develop corporate image of the brand. Furthermore, the promotion mix consists of five major tools referring to Kotler, 1994, are as follows: Advertising Direct marketing Sales Promotion Public Relation and Publicity Personal Selling McCharty 1982, p.37, stated that promotion is concerned with telling the target market about the right product. Promotion includes personal selling, mass selling, and sales promotion. Through the statement of Kotler and McCharty, the aim of purchasing brand milk will lead to an effective measure to make better purchasing decisions. 2.6.2 TOOLS Of PROMOTION The four main tools of promotion are advertising, public relation, direct marketing and sales promotion which convey the message to the consumers. Advertising includes any paid forms of non-personal presentation and promotion of ideas, goods, or services by an identified sponsor. In contrast, public relations focus on building good relations with the company by obtaining favorable unpaid publicity. Direct marketing is any form of personal presentation by the firms sales force for the purposes of making sales and building customer relationships. Firms use sales promotion to provide short-time incentives to encourage the purchase or sale of products or services, stated by Armstrong and Kilter, 1999. Tools of promotion encourage and motivate consumers to purchase brand milk along the benefits providing to them. 2.6.3 Advertising According to Armstrong and Kotler (1999), regardless to the budget size, advertising can succeed only if commercials gain attention and communicate well. Good advertising messages are especially important in todays costly and cluttered advertising environment. Two steps are involved: Firstly, creating effective advertising strategies begins with identifying customer benefits that can be use as advertising appeals. Secondly, to develop a compelling creative concept that will bring the message to life in a distinctive and memorable way. Advertising is also the integral part of our social and economic system. In other words, it is a co commitment of modern marketing, which helps the consumers at least in three ways to purchase brand milk, which are as follows: It acts as driving force in decision making. It ensures better quality products as reasonable prices. It saves good deal of time. 2.6.4 Gaining The Market Share Referring to Kilter, 1988, gaining market share is a key factor in reaching a leadership or number one position in any industry. However, gaining significant share requires careful planning, thoughtful well-executed market strategies, and specific account-by-account practical plans. Gaining market share is an extremely important component in the promotion of marketing strategies. Thus, it is difficult for a company to obtain loyalty without gaining high market share. Kilter (1988) also suggested that the five major strategies, which consist of price, new product, service, strength, and quality of marketing, advertising, and sales promotion, are important for a company. 2.7 Factors Of Brand Positioning Positioning is how a product appears in relation to other products in the market. It is one of the main factors that affect consumers perceptions of the milk brand. Brand positioning can help make or break a product depending on the effectiveness of its execution, (subtracted from brand by social). The factors of brand positioning are as follows: 1. Brand Attributes What the brand delivers through features and benefits to consumers? 2. Consumer Expectations What consumers expect to receive from the brand? 3. Competitor attributes What the other brands in the market offer through features and benefits to consumers? 4. Price Price is an easy quantifiable factor and as well as the prices to compete with other companies. 5. Consumer perceptions The perceived quality and value of the companys brand in consumers minds (i.e., does the companys brand offer the cheap solution, the good value for the money solution, the high-end, high-price tag solution and so on ), mentioned by Aaker and Keller 1990, Aaker and Keller 1992, Sunde and Brodie 1993, Dacin and Smith 1994, Bottomley and Dolye 1996. So, this take some time to create a thorough picture of the current market and how the companys brand fits in that market to determine the companys brands current position. If that is not the position you want for the companys brand, take the necessary steps to change it based on the gaps defined when the company analyzed the five factors above. 2.8 Milk Production In Mauritius Currently, in Mauritius we are producing 12% of our local milk production that is 12million liters per year. The government of Mauritius has been encouraging farmers to engage in milk production by providing loans facilities of up to Rs 50,000 per head (with 5% interest). Through this initiative, local milk production has increased slightly over the past years (local milk production was 2% in 2003). However, there are still several challenges faced by breeders and entrepreneurs in the diary industry: the main challenge is the high cost of production, mention in 2013 Nawsheens World, templates: Monday, October 31, 2011. Mauritius produces a few milk brands, which are as follows: Island Dairy Ole Twin Cows Candia Maurilait Over the past years, the cost of production has been continuously increasing mainly because of the price of animal feeds, hence reducing profitability in the business. Milk production will mainly depend on: Management of feeding programs Calf and heifer management Reproductive management of dairy cows Milking management Other husbandry practices related to animal health and welfare, housing, environment and bio-security measures, (2013 Nawsheens World, templates: Monday, October 31, 2011). However, as long as local suppliers cannot sustain our domestic market, no other stuff than milk is more linked with the drawbacks of globalization. 80% of the milk consumed in Mauritius comes from New Zealand and Australia, explains Jean-Cyril Monty, the officer in charge of the diversification desk at the Mauritius Chamber of Agriculture. The latter also suggested that, now that the price has risen by 40% since the beginning of the year 2007, people are diminishing their consumption. This rise in price, which he believes will continue with another 30% by the end of the year, is related to multiple factors outside our control. 2.8.1 Findings On Production Of Milk In Mauritius The quantity of milk produced by the village cows in this experiment (9.2 kg/d during 300 days) is higher than the average production of between 3.5 and 9.2 kg/d reported for the Government stations where cow feed is fed at the rate of 0.5 kg/kg milk. It is also relevant to compare it with the milk production of unsupplemented village cows (4 to 5 kg/d for a lactation period of around 225 days). Although there were only 23 (about 25%) Creole cows in the study their milk production potential appeared to be equal to the more exotic genotypes with a daily mean of 9.6 and 8.3 kg per head for a 301- day lactation in the Vacoas and Mapou areas respectively. This indicates that under these village conditions the Creole breed has a similar performance to the imported Friesians or their crosses. Mauritius milk production can sum up in the following ways: The village cattle breeders own about 11000 females over one year of age. This represents about 65% of the female national herd in this age group. The cattle are a side-line activity of the family. There are normally one to two cows per farm No forage is specifically cultivated for the cows. Forage sources include sugar cane tops, and shrubs and grasses, which are found on roadsides and on common land. The housing and shelter of the tethered animals is often rudimentary. Milk is sold to neighbours or to small scale (20 25 l/d) milk sellers who then distribute the milk. There is little or no use of concentrate feeds as supplements to the hand-collected forage. The cattle are a mixture of the local Creole breed and Friesians. Artificial insemination is subsidised by the Government and is widely used. Milk production is generally low (1200 1500 liters per lactation). There are generally relatively short lactations (about 225 250 days) and long calving intervals (15 18 months). On the basis that the small breeders make a major contribution to national production of fresh milk the work reported here was designed to investigate the extent to which productivity in this sector could be improved. As far back as 1956, Bennie reported that the local Creole cattle could double their milk production with improved feeding. In 1971, an FAO project on Milk and Meat Production suggested that the most important factor limiting milk production was the supply of a protein concentrate to the cow. This FAO project also demonstrated that milk yield could be increased considerably by better feeding and management. (By A A Boodoo, R Ramjee, B Hulman, F Dolberg* and J B Rome*). More recently, Dolberg and Rowe (1984), in reviewing experimental work done by the Mauritian Ministry of Agriculture on milk production, concluded that under local conditions greatest responses in milk production would be expected from protein supplementation. They referred to the work of Mapoon et al (1977) which showed that ground nut cake was more efficient then either a balanced concentrate feed, or a molasses/urea mixture, as a supplement for milk production; and to that of Gaya et al (1982) who reported that supplementation with cottonseed cake increased milk production more effectively than the formulated concentrate [emailprotected] In fact, similar increases in milk production were achieved with about half the level of cotton seed cake as commercial concentrate. A second advantage of cottonseed cake as a supplementary feed is that it requires no mixing. The project described here was designed to investigate and compare the effect of two types of supplements: the commercial concentrate [emailprotected] and the protein-rich cotton seed cake. In addition to the measurements of milk production the study provided the opportunity to investigate, the nutritive value of the most commonly used feed resources (see Boodoo et al 1990). Factors Influencing Brand Preference Kotler (1988) identified affective means of increasing market share as a primary means of achieving competitive advantage in both existing and new customers and stagnant markets. Brand preference is also known as brand adoption. Lalit S. Badgujar (Roll No.04) stated that, people begin to develop preferences at early ages. Brand preference represents which brands are preferred under assumptions of equality in price and availability. Cooper (1993) noted that most innovations come with high risks as most of them failed in the marketplace creating the need for marketers to have a clear understanding of success factors in brand adoption. Theories of adoption have often been used to explain how consumers form references for various goods and services (Rogers, 1995; Tornasky and Klein, 1982; Mason, 1990; Charlotte, 1999). Generally, these theories emphasize on the importance of complexity, compatibility, observability, triability, relative advantage, risk, cost, communicability, divisibility, profitability, social approval, and product characteristics in brand preference (Wee, 2003). The relative importance of each factor depends on the nature of industry under consideration, location, and social characteristics of the consumers of the different brands. In this study, we have focused on four main factors, which the customer depends upon while selecting the brand, which are as follows: 2.9.1 Price Price is a factor, which the consumer may depend while selecting a brand in any kind of product. Most of the consumers may give first preference to quality than the other factors. However, price can have an influence for the final decision of the consumer. If there are two or more brands, for a particular product, the manufacturers will reduce the price in order to attract the consumer but still the consumer must see the quality provided. 2.9.2 Quality In addition, quality of product is another key factor during product selection. Especially in the case of milk, we should depend upon than any other factors the quality. High quality will give us a good health. Pasteurization will also conduct in order to maintain the quality of the milk. The quality of the milk should be good to attract the customers, Kilter (1988). 2.9.3 Services Service is an important factor, which the customer is influenced in the modern market. Placing the orders in the right time at the right place is the part of good servicing. 2.9.4 Advertisement Advertising as a powerful technique of sales promotion has been doing wonders in the domain distribution because it is quite capable of influencing the course of consumption, affecting the process of production, enlarging the exchange and diversify the distribution. That is why it is said that advertising is the arch median lever that motivates the world of commerce and industry. It has the pride of the place in framework of dynamic marketing. The role of advertising in the modern business world can be analyzed from five distinct angles namely manufacturer, intermediaries, sales force, consumers and the society. The ultimate aim of all marketing efforts is to satisfy the needs of the consumers by transforming the benefits of productive efficiency to the final users. 2.10 Conclusion To conclude, this chapter is mainly about branding that is, its importance, concepts, positioning and so on. According to Shimp (2007), consumers represent the starting point for all marketing activities. Therefore, in this part of the project, it is viewed how to communicate with customers and send them feedback about the types of products available in the market. In the literature review, different parts have described in details to make a good analysis and choice for milk brand selection.

Analysis of The Stronger by August Strindberg Essay -- Performance Art

The Stronger by August Strindberg is a play that is filled with irony. One of the first things noticed in this play is that the characters have no names, nor are they labeled by any type of status. Rather than having names like most plays, the two characters are differentiated by the letters "X" and "Y." Another ironic thing about this play, is how it is written; the dialogue of the play is not evenly spoken. Instead of the two characters conversing between one another, the play is written almost like a monologue where only Mrs. X speaks. Because Mrs. X is the only speaker, one would think that she is "the stronger," but ironically, she is not. One reason Mrs. X is not thought to be the stronger is that she goes back to her husband after she concludes that an affair had existed ironically thinking that the affair will not disable her marriage. The play implies that Mrs. X believes that the affair has and will somehow continue to make her marriage stronger. She says, "that only gave me a stronger hold on my husband," but actually her knowledge of the affair will eventually weaken the relationship. Knowing that her trust has been abused will normally cause her to question her husband's devotion: Were there other affairs? Is he cheating now? If so, is she someone I know? If not, will he cheat again? These are possible questions that will remain unanswered because Mrs. X has no intention of confronting her husband. This is a fact because in the last line of the play Mrs. X says, "Now I am going home - to love him." This quote also makes it seem like she is going home to live her normal life as if the affair never occurred, but making herself believe that it has disappeared will not solve anything. She believes th... ...would one put oneself in the situation to be vulnerable to such false mentality? It is because emotionally Mrs. X is weak, so to protect herself from any pain, she thinks of a way to logically persuade her mind and her emotions that she is the stronger, but she is not. Mrs. X can not be the stronger because Miss Y clearly shows more strength by saying nothing. Miss Y shows this strength by simply sitting there enduring Mrs. X's accusations and abuse. She sat there and faced it all when she could have easily matched Mrs. X's actions. Miss Y could have refused to listen to Mrs. X's accusations, or she could have made a scene by responding to Mrs. X's abuse. Instead of showing signs weakness, Miss Y chose to say nothing because there really was nothing that could be said to make the situation any better. By choosing to do so, Miss Y proves that she is the stronger.

Johan Sebastian Bach Essay -- Biography Biographies essays research pa

  Ã‚  Ã‚  Ã‚  Ã‚  On March 21, 1685, Johan Sebastian Bach was born in Eisenach, Germany. His parents’ names were Johan Ambrosias Bach and Elizabeth Lammerhirt Bach. His Family earned its living as musicians. His mom died when Bach was 9 then his dad died the next year so he moved to live with his oldest brother in Ohrdruf. In Ohrdruf Bach Learned Latin and sung as a soprano in the school choir. His brother didn’t have enough money so Bach moved to Luneburg in March of 1700. Bach could go to school for free because he sang in the choir. (Geringer 433, www.let.rug.nl ) Bach left Luneburg in March of 1702 when he was 18, to go to Arnstadt to get his first real job. His job was to be the organist of the Neue Kirche Church. Bach got a four-week vacation from playing at the church to go to Lubeck and listen to the music of Dietrich Buxtehude. He walked 200 miles to get there, and then instead of staying for the four weeks the church had planed, he stayed for four months. (www.let.rug.nl) Bach got a different job as an organist in Muhlhausen at St. Blasius Church in 1707. That year his uncle died and Bach inherited some money. That gave Bach enough money to marry Maria Barbara, his second cousin. They got married in April of 1707. One year later in 1708 Wilhelm Ernst hired Bach as an organist and as a member of the orchestra so Bach and Maria moved to Weimar. Bach’s salary was double what he got paid at his last job. (Geringer 433, Lloyd 30 www.let.rug.nl) Bach and Maria were financially st...

Environmentally Conscious Propoganda :: social issues

Environmentally Conscious Propoganda WRIT 140 September 11, 2000 Environmentally Conscious Propaganda Almost every single original concept today has become mainstream or shows a general trend towards becoming so. Propagandists realize this and often exploit these ideas, tainting their flavor of originality and creating a new generation of gullible â€Å"wannabes† who can partly adhere to any philosophy, but do not allow themselves to be inconveniences by certain doctrines. Anything that might elicit followers or have the potential to, has drawn the attention of these solicitor, yet one of the fastest growing target audiences today seem to be â€Å"nature lovers.† Three examples extracted from various sources reveal that advertisers are targeting a presumed cultural attitude that people today wish not only to save the environment, but also view nature as the idealistic existence in contrast with the mundane city life most people have accustomed themselves to. Next one must question, â€Å"What is the idealistic existence?† All three advertisements point to one notion or another but more often than not seem more different than similar. The fist two automotives ads for Toyota and Honda respectively both deal with the natural environment. However Toyota appeals directly toward nature lovers and those who share an outdoorsman spirit by asking the question of whether one would blend in with nature, or with traffic. The advertisement crosses both a machine and the environment, suggesting that a balance can be achieved between man and nature. The subliminal hint seem to be that the advertised 4-Runner will somehow help one tune into nature. The stance from the Honda Insight, however, is far from Toyota’s. Reaching for the environmentalist, the ad immediately grabs at people who are to some degree conscious of environmental concerns such as pollution. While not an â€Å"environmental movement all by itself† the insight does pollute considerably less than normal 4 cylinder and 6 cylinder vehicles. While not designed to adhere to â€Å"hardcore† environmentalists, it does appeal to the average person co ncerned with it. Car promoters also presume that while people today are concerned with the environment, most people wouldn’t lift a finger if they had to go out of their way. The average Joe would rather pass a piece of trash on the street than pick it up, because they would inconvenience themselves in the process. The Insight is accordingly partially battery powered, never needs to be plugged in, and is more of a convenience.

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. For example, recent work has explored Part of Speech tagging [19], geographical variation in language found on Twitter [13, 14], modeling informal conversations [44, 45, 9], and also applying NLP techniques to help crisis workers with the ? ood of information following natural disasters [35, 27, 36]. 1. ACKNOWLEDGEMENTS The authors would like to thank Luke Zettlemoyer and the anonymous reviewers for helpful feedback on a previous draft. This research was supported in part by NSF grant IIS-0803481 and ONR grant N00014-08-1-0431 and carried out at the University of Washington’s Turing Center. 12. REFERENCES [1] J. Allan, R. Papka, and V. Lavrenko. On-line new event detection and tracking. In SIGIR, 1998. [2] M. Banko, M. J. Cafarella, S. Soderl, M. Broadhead, and O. Etzioni. Open information extraction from the web. In In IJCAI, 2007. [3] H. Becker, M. Naaman, and L. Gravano. Beyond trending topics: Real-world event identi? ation on twitter. In ICWSM, 2011. [4] C. Bejan, M. 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Falsifiability of the Big Bang Theory Essay

In one of Karl Raimund Popper’s works, he discussed the demarcation that differentiate the sciences from the non-sciences or those that are merely subjects of faith and pseudo-sciences. Popper believes that sciences are falsifiable. If something can be falsified it can be considered as a science. He argued that unlike the work of Einstein which is â€Å"capable of conflicting with possible, or conceivable, observations†, the works of Freud, Marx and Adler proves every event as compatible to their theories which in Popper’s argument was not scientific. No matter what the situation is, Freud, Marx and Adler would explain it in terms of their theory which is somehow a subjective way of explaining or looking into things. For instance, a selfish capitalist could be analyzed as someone who was fixated to a certain Freudian psychosexual stage or was suffering from Adler’s concept of inferiority. Marx would analyze the man from a class-struggle perspective. Popper believes that although there are evidences and observable facts that could prove the three theories through experimentation, these experiments are not falsifiable and are therefore merely based on faith and subjective judgments. In the case of the Big Bang theory, it argues that the universe is expanding. The theory stated that the universe had started from an initial bang or explosion of a very dense material. The impact of the explosion, according to the theory, is still observable today. Evidences shows that the space was expanding as quasars and galaxies are perceived to shift in their perceivable wavelengths. Hubble assumed, with respect to his observations, that either the universe is moving away from a center were an explosion had originated or that the universe was in constant expansion. Unlike the three theories mentioned above, the Big Bang theory left space for debate and possible changes. The three previous theories would always hold true in past and future circumstances and would always have an explanation about the phenomena that they are concerned with (personality or human nature). On the other hand, the Big Bang theory may be false whenever a new discovery proves that a Big Bang had not occurred. Big Bang theory passed the falsifiable criterion set by Popper. References: Balashov, Y. & Rosenberg, (2002). A. Philosophy of Science Contemporary Readings. Routledge. Pages 294-300. Edwards, R. E. (2001). What Caused the Big Bang? New York; Rodopi B. V.