Over the past decade, there has been a proliferation of online news articles. News articles can contain rich content and contextual information pertaining to groups in societies, such as senior citizens, child rights groups, religious minority groups, or environmentalist groups. In addition, news articles contain different object types, such as people, organization names, statistical (numerical) information, country names, author names, or events. Thus, it is possible to create a complex heterogeneous graph containing multi-type objects (vertices) and multi-type linkages (edges) among the objects, such as common keywords found between two news articles. We call such a graph a Heterogeneous News Graph (HNG). Currently, it is possible to extract rich information and knowledge from an HNG. It is our belief that one could use an HNG to resolve the bias and visibility issues found in many news sources, also capturing important news articles. First, due to the amount of news feeds currently available in this digital age, readers want a filtered view of relevant news articles, allowing them to focus on important (breaking) news that contain rich contextual information for their particular societal group. For example, senior citizen groups might want to know new safety measures taken by police for elderly people. Second, visibility is another problem in the world of journalism, where there are multiple objects in the news articles, such as author names and organization names. In this case, for example, readers might need to know who are the relevant authors, or experts, for particular topics, such as Libya, Afghanistan, and climate change. Third, there is an inherent bias in the narrative of the news by the authors, whereby they try to align their opinion to a perceived general public sentiment in order to increase readership. For example, news agencies often tend to link minorities to the rise in crime, and terrorism to specific religious groups. To address the issues of determining importance, visibility of objects, and bias in news narrative, we propose novel graph-based approaches using HNGs that will (1) rank the expertness of an article’s author on a specific topic, (2) identify articles of particular interest and value, and (3) classify articles as unbiased. In summary, our approach will use a novel graph-based, streaming approach for determining context and content in big data so that more personalized recommendations can be realized.
|Commitee:||Kosa, Martha, Mills, Allan, Siraj, Ambareen, Talbert, Doug|
|School:||Tennessee Technological University|
|School Location:||United States -- Tennessee|
|Source:||DAI-B 79/01(E), Dissertation Abstracts International|
|Keywords:||Context mining, News streams|
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