Threading stories and generating topic structures in news videos across different sources

Ubiquitous access to internet has resulted in more and more people going online to get their daily dose of news. In a 2010 survey conducted by the Pew Project for Excellence in Journalism, 41% of the respondents said they get most of their news online, 10% more than those who said they got most of their news from a newspaper. A lot of socio-technical factors have contributed to this phenomenal rise in adoption of online news in recent years. One of the biggest reasons why people are increasingly reading news online is because it facilitates discussion with peers (Nguyen 2010), offering different viewpoints which aid in forming a rounded personal opinion about the news story. The Pew survey found that 37% of online news users (and 51% of 18-29 year olds) think that commenting on news stories is an important feature to have. A lot of people tend to shape their opinion by reading discussion comments, reflective articles, blogs and even tweets about the news. Hence, an increasing number of people rely on online sources of news – be it news websites or news aggregator services like Digg, Reddit, Google Reader, Flipboard, Pulse etc. The problem with these news websites and aggregators is that the only way people can gather public opinion is by actively searching through the endless stream of comments and feeds, filtering out spam (which is a growing problem) and then reading the relevant posts. A top trending story on Twitter will typically see multiple tweets per second, and keeping up with the rapid flow of incoming tweets is quite cumbersome and cognitively taxing. Hence it becomes increasingly difficult and time consuming for someone who wants to get the pulse of the people affected by a news story. Furthermore, in certain scenarios people might want to look at more fine grained opinions. Currently, there is no elegant way to extract geographic and demographic impact of a news story. What is the public sentiment in Indonesia about the Arab Spring? How did the public opinion about the Wikileaks disclosures change as the story unfolded during the course of a year? It is very difficult and tedious to observe such patterns using the currently available news providers. This work attempts to solve these problems by proposing a news aggregator platform which pulls news stories from various sources and also aggregates public responses, reflections, opinions and sentiments associated with those stories. This data is presented in ways that are easily understandable so readers can make better sense of the stories unfolding across the globe. Such a news aggregator platform that gathers and display public opinion and sentiments about a story, must deal with various challenges – 1. Opinions are very subjective. Different people feel about a story in different ways. With such an enormous amount of diverse opinions and subjectivity, how can we possibly aggregate the responses into something that makes sense as a whole? ________________________________ Acknowledgements: Prof. Yardi, S. for the guidance Sethi, P. (2013). Public opinion aggregation by annotation and tagging of online news stories. iConference 2013 Proceedings (pp. 891-894). doi:10.9776/13453 Copyright is held by the author. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 892 2. There isn’t really a unified web standard for expressing opinion (in textual form). Some people tweet in 140 characters, while others write elaborate blog posts. Some websites employ tags which a reader can use to define and classify their public opinion, while others rely on threaded comments and comment ranking systems. How can a platform be flexible enough to adapt to all these varied standards so that it can extract valuable data from various sources? Perhaps the platform can create a new standard of expression on the web which is flexible and comprehensive enough to be used to express diverse views about every news story in the world. 3. How to filter out spam while extracting public opinion? 4. Once the platform has access to the data it needs, how should it be displayed to the reader in a way that makes sense? What forms of visualizations, illustrations and graphical representations can be employed to give the reader a holistic view of how people feel about a story? 5. How can the platform determine and convey effects of geographical, demographic and temporal variations as the story unfolds? These are just a few out of possibly many issues which must be dealt with. Previous research on similar public opinion aggregation services has greatly focused on natural language processing, data mining and text categorization and clustering. Xiaojun (2010) proposed a framework for crawling the web for comments and applying various data mining algorithms on the data to extract relevant information. Diakopoulos and Shamma (2010) used tweets posted in conjunction with the live presidential debate between Barack Obama and John McCain to gauge public opinion. Brody and Diakopoulos (2011) studied the use of word lengthening to detect sentiment in microblogs. This research proposes a solution – The Opinionated Reader, which relies on sentiment tags and annotations associated with a news story. The essential idea is to create a commenting, discussion and sharing plug-in which can be used by news websites and aggregators as a commenting solution for their news pages. Users wanting to share or comment on a news story through the plug-in are asked to tag the news story with sentiment tags and annotate the story with their reaction (happy/positive or sad/negative). These tags and annotations are stored, aggregated and linked to each news story. A mobile application provides the front-end interface for users to access the news stories and the aggregated sentiment associated with each story. The basic architecture is explained as follows: The Opinionated Reader – Mobile/Tablet App The app fetches news articles from various web sources, based on the interests and preferences configured by the user. In every news article, a portion of the screen real estate is reserved for Opinions which shows graphical visualizations and illustrations of the public opinion surrounding the news story. These visualizations include: A Sentiment Graph indicating the popular tags associated with the story (E.g.: “Shocking”, “Inspiring”, “Amusing” etc.). See Figure 1 for example visualization. A Positivity Graph which plots the level of positivity associated with the story on a time scale from when the news broke. See Figure 2 for example visualization. Figure 1. Example Sentiment Graph Figure 2. Example Positivity Graph iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 893 The user can choose to see these visualizations for a particular time period in the evolution history of the news story, or for a specific country. The app also facilitates people to tag and annotate news articles from within its interface. The Opinionated Reader – Commenting and Sharing Web Plug-in These days, a common way of adding discussion and commenting functionality to news websites is by using 3 rd party services (like DISQUS). The Opinionated Reader is a similar service which can be embedded into the news articles of various news websites to enable commenting and sharing. When someone wishes to comment on an article, the comment is directed through this plug-in, which allows the users to annotate the article with the sentiment tags and reactions along with their comments. The Opinionated Reader saves this information along with the commenter’s location and date of comment (See Figure 3). Figure 3. The Opinionated Reader Web Plug-in 'Add Comment' dialog mockup The Opinionated Reader – Back-end The Back-end maintains a database of news items extracted from RSS feeds of various news websites. Each news article is linked with the sentiment tags and reaction/positivity annotations extracted from the comments and annotations gathered by the commenting plug-in. This data is used by the mobile/tablet app to generate visualizations (Sentiment Graph and Positivity Graph). The back-end also performs data mining on the tags and annotations for geographies and tracks the opinions across time. The back end system responds to queries received from the mobile app with the news story and associated tags and annotations, which are then rendered by the mobile app for the user. iConference 2013 February 12-15, 2013 Fort Worth, TX, USA 894 Discussion and Conclusion This design idea is still in a nascent state and has long hours of research, brainstorming, designing and development to go before it can be realized into something tangible. Twitter has grown exponentially in importance as a news source and it would be vastly valuable to integrate Twitter with The Opinionated Reader. Possibilities include use of special hash tags and natural language processing of tweets to extract public sentiment. The current design supports only two reaction annotations – positive and negative. Not every news story fits this annotation paradigm. Further research about human reactions to news stories might unveil interesting insights which would help zero in on a more robust annotation rubric. Lastly, since this service is envisioned to be non-curated and non-moderated, the value served by the app depends on the users themselves. Greater adoption will lead to more annotations and tags, which translates into a more accurate public opinion as presented to the user.

This experiment investigated the effects of television news frames on (1) audience interpretations of a political issue, (2) the salience of news frames versus other information in the story, and (3) support for future policy. A sample of 145 adults watched an experimental television news bulletin produced in cooperation with reporters and editors at a national television news program about the enlargement of the European Union. A news story was manipulated to reflect a conflict frame or an economic consequences frame. The two frames provide direction to the audience's thoughts about the issue but do not yield different levels of policy support. Frames in the news are as important as core facts in a news story when citizens conceive of a political issue.

Existing research on news video analysis mainly concentrates on structure analysis, semantic concept detection, annotation and search. However, little work has been contributed to news video people community analysis, which is helpful for users to understand the event. In this paper, we propose a novel approach to classify the people appearing in the news video into different communities. In our approach, the people appearing in the news video are first identified by associating their faces with names. The faces are detected from the video frames, and the names are obtained from the text. Then, the people belonging to the same organization are clustered. After that, the relationships between these organizations are determined using sentiment analysis. The sentiment words are diverse in each news story and contain both positive and negative ones. However, we have news title, which is the summary of the story and the sentiment of which is clear, to help us to mine the relationships between the organizations. At last, social networks are built to classify those people/organizations into different classes, and the people/organizations are ranked in each community according to their influence. The main contributions of the paper are two folds: 1) we propose a novel approach to present the news video event according to communities; 2) we propose to use the sentiment analysis and social network to classify the news people/organizations. The experimental results on the selected news topics demonstrate that the proposed approach is effective.

A survey of a Midwestern community supported a public health model of crime reporting and found respondents saying media pay too much attention to crime. Respondents provided modest support for cultivation theory, with no differences found for effects of newspapers and television. Violence is popular in America. It is ubiquitous. It leads to boffo box office. It is a popular topic in political campaigns. It is a cheap and easy way to sell newspapers or bump up ratings. This paper seeks to address sociological effects of violence in media, to discuss how violence is generally covered by news media on a level, to assess feelings of residents of a Midwestern university town regarding news coverage and analyze survey results from both cultivation effects and public health reporting perspectives. Violence in media Violence in media is a longstanding issue. In early research, George Gerbner noted violence in 66 percent of approximately 3,000 American films produced from 1950 to 1961. He also observed, in 1950s, that more than 75 percent of television drama contained acts of violence and it predominated in 56 percent of programs. In 1967 and 1968, he recorded that 80 percent of television programming contained violent portrayals.(1) A three-year study sponsored by National Cable Television Association found that violence continues to be prevalent. Prime time broadcast programs containing violent content increased by 14 percent between 1994 and 1998 and similar content increased by 10 percent on cable stations during same time frame(1). The percentage of prime time broadcast and cable programs with violence ranged from 58 percent in year one to 61 percent in years two and three. Researchers found that a minimum of six violent incidents occur in each hour of typical violent program.(2) But violent content is not limited to entertainment programming. also is a dominant topic of news broadcasts. Paul Klite, Robert Bardwell and Jason Salzman found that crime stories consumed about a third of total time devoted to news. They found that violent crimes are most frequently aired and that though infrequent among crime types, is most commonly covered crime on television newscasts. On 56 stations, more than half crime coverage relates to murder, they wrote.(3) John McManus was interested to learn whether, in making news decisions, television news would be treated as a commodity, following an economic model, or a public service, following a journalistic model. He found that television news, in large part, was simply a regurgitation of newspaper content. This observation provides evidence that newspapers play an influential role in news agenda-setting process not only of their own organizations, but in that of broadcast news outlets as well. McManus found economic model dominated three stations he observed. There was little attention paid to innovation in news gathering.(4) Crime news is a staple of journalism, and reporting it has been considered a public duty of press. Nevertheless, Walter Jaehnig and his colleagues found newspapers emphasize relatively infrequent violent crimes.(5) A 1994 study by Center for Media and Public Affairs showed that news stories regarding crimes more than tripled between 1990 and 1994. Gerbner reported that local TV news crime reports [doubled] in past two years [1995-96].(6) Phyllis Kaniss notes that today's journalists identify strongly with power structures-city and county officers, police and fire departments, housing authorities and so forth. The news that emerges from these sources is often bad news - news of crime, indigence, health problems and so forth.(7) One study showed that newspapers in St. Louis greatly distorted reality of criminal activity. E.T. Jones found that the typical murder had slightly more than 90 times as much attention as average major crime. …

An empirical analysis of 3,334 state and local television news stories in 10 cities clarifies the reluctance of commercial television newscasters to cover state government. Contrary to what many television news directors assert, television's minimal attention to state government cannot be attributed to an alleged negative relationship between state government and newscast ratings. Rather, it appears to stem from the high cost of obtaining filmed coverage of state government in a timely fashion.

This study challenges whether the television (TV) news story has changed due to the advancement in technologies and adaption of production methods from a Fordist to a post-Fordist routine. Textual analysis of 40 years of producing the “news package” appears to reveal minimal changes to the length and framework; however, there seems to be significant changes to the “genetic” construction. While at first, the changes appear to be driven by technology, it may be reasonable to question whether or not technological advancement is responsible. This essay looks at the construction of a TV news story from a microlevel of production as well as examines possible causes for the progressive alterations.

This paper focus on TV news programs and design a content-based news video browsing and retrieval system, NVRS, which is convenient for users to fast browsing and retrieving news video by different categories such as political, finance, amusement, etc. Combining audiovisual features and caption text information, the system automatically segments a complete news program into separate news stories. NVRS supports keyword-based news story retrieval, category-based news story browsing and generates key-frame-based video abstract for each story. Experiments show that the method of story segmentation is effective and the retrieval is also efficient.

In this paper, we present a schema for content-based analysis of broadcast news video. First, we separate commercials from news using audiovisual features. Then, we automatically organize news programs into a content hierarchy at various levels of abstraction via effective integration of video, audio, and text data available from the news programs. Based on these news video structure and content analysis technologies, a TV news video Library is generated, from which users can retrieve definite news story according to their demands.

This paper is concerned with representations of the human genome and medical applications of modern biotechnology in Finnish television news. The main focus is on the way that news stories are framed by various linguistic and visual means and on how scientists appearing in the news are positioned. The qualitative analysis shows that in the national frame, biotechnology was treated as a field with great future promise, weighing the prospects of Finnish scientists to succeed in international competition. In the disease frame, the focus was on the achievements of genetic research in the treatment of serious diseases. In the breakthrough frame, it was predicted that genetics will revolutionise the treatment of diseases in ‘the near future’. Finally, news stories presented in the threat frame were mainly concerned to predict and avert problems arising from the potential abuse of genetic research. The analysis suggests that these frames implied certain speaking positions to the scientists appearing in the news stories.

How sensationalist features affect television news preferences and recognition among young viewers

This study compared embedded and nonembedded (unilateral) television news coverage during the invasion and the occupation of Iraq. Content analysis was conducted of ABC, CBS, NBC, and CNN news during the invasion and during the occupation examining whether embedded and nonembedded news reports were different and, if so, how. The results revealed that compared to nonembedded reports, embedded network television news stories were more favorable in overall tone toward the military, more favorable in depictions of military personnel, and featured greater use of episodic frames which, as a result, elicited somewhat more positive relational cues. In addition, the results indicated that compared to network news coverage of the occupation, news stories of the invasion were more positive in tone and employed more episodic framing.

The Christian men's group, Promise Keepers, received glowing television news coverage for its 1997 rally in Washington, B.C., which defied typical coverage of religious organizations. Scholars have attributed the positive stories to the lack of airtime for the group's feminist critics. I argue, however, that the television news reports favored Promise Keepers because of the feminists' presence in the news stories. The news media characterized both groups in contrast to each other, portraying Promise Keepers as 'feminine" and the feminists as "masculine." In contrasting genders, the media endorsed Promise Keepers' sincerity and dismissed the feminists' concerns as paranoid. This study helps to explain Promise Keepers' rise to fame and subsequent fall, and it modifies theories about media gendering and framing.

Based on a simple temporal structural model of a news program, the paper presents a practical solution to automatic news story segmentation by integrating syntactic and semantic methods. First, a syntactic segmentation method is used to detect the shot boundaries in order to partition video frames into video shots. Then, a semantic segmentation method based on graph-theoretical cluster analysis is developed to classify the video shots into anchorperson shots and news footage shots. Finally, a structural model of news video is used to complete the news-story segmentation. The proposed method obtains a precision of 90.45% and a recall of 95.83% in the segmentation experiment of 168 news stories from two Hong Kong news stations.

Personal News Retrieval System is a client-server application that delivers news segments on demand in a variety of information networks. At the server side, the news stories are segmented out from the digitized TV broadcast then classified and filtered based on consumers' preferences. At the client side, the user can access the preferred video news through the Web and watch stored video news in preferred order. Browsing preferences can be set based on anchorperson, broadcaster, category, location, top- stories and keywords. This system can be used to set up a news service run by content providers or independent media distribution companies. However, in the news era of enhanced PC/TV appliances, it is foreseeable that the whole system can run in the living room on a personal device. This paper describes the chosen server architecture, limitation of the system and solutions that can be implemented in the future.

This study investigated the effect of five types of topical structure on the item difficulty of reading comprehension questions. A topical structure includes the initial sentence element which is the initially placed discourse material in a sentence; a mood (surface structure) subject; and a topical subject which is a surface subject relating to the discourse topic of a text. The results indicated that there was a difference in the item difficulty of the questions according to the type of topical structure on which the questions were based. The questions derived from sentences in which given information occurred before new information and from sentences in which topical information occurred in subject position tended to be easier than questions derived from sentences manifesting other types of topical structure. The article concludes with a suggestion that topical structure types be included as a variable in future models of ESL reading comprehension difficulty.