An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harness...

In this paper, we build a generic opinion-fact classifier to detect opinions and facts from online news articles and social media datasets such as Youtube comments and idiom hashtags. We further use this classification model to compare opinionatedness of various news article sections. The proposed classifier produces better results than the existing methods over four different datasets, and the...

In this paper, an online news recommender system for the popular social network, Facebook, is described. This system provides daily newsletters for communities on Facebook. The system fetches the news articles and filters them based on the community description to prepare the daily news digest. Explicit survey feedback from the users show that most users found the application useful and easy to...

This paper investigates the effectiveness of online temporal language model adaptation when applied to a Thai broadcast news transcription task. Our adaptation scheme works as follow: first an initial language model is trained with broadcast news transcription available during the development period. Then the language model is adapted over time with more recent broadcast news transcription and ...

News recommendation and user interaction are important features in many Web-based news services. The former helps users identify the most relevant news for further information. The latter enables collaborated information sharing among users with their comments following news postings. This research is intended to marry these two features together for an adaptive recommender system that utilizes...

We describe a system for recommending news articles, called NewsPer, which learns news-reading preferences of its users and suggests recently published articles that may be of interest to specific readers based on their interest profiles. The underlying algorithm is based on representing articles by bags of words and named entities, and applying support vector machines to this representation. W...

In this paper, we address the problem of mining transliterations of Named Entities (NEs) from large comparable corpora. We leverage the empirical fact that multilingual news articles with similar news content are rich in Named Entity Transliteration Equivalents (NETEs). Our mining algorithm, MINT, uses a cross-language document similarity model to align multilingual news articles and then mines...

In recent years, rumor news has been generated by humans as well as robots in order to attract readership, in uence opinions, and increase internet click revenue. Its detrimental e ects have become a worldwide phenomenon, leading to confusion over facts and causing mistrust about media reports. However, evaluating the veracity of news stories can be a complex and cumbersome task, even for exper...

BACKGROUND Debates persist around the world over the development and use of genetically modified organisms (GMO). News media has been shown to both reflect and influence public perceptions of health and science related debates, as well as policy development. To better understand the news coverage of GMOs in China, we analyzed the content of articles in two Chinese newspapers that relate to the ...

Abstract. Geographical processes might well impact online engagement in big countries like the USA. Upon a random sample of 200K news articles and corresponding 41M comments posted on the Yahoo! News in that country, we show that nearby individuals tend to comment and engage with similar news articles more than distant individuals do. Interestingly, at state level, topics one reads about are as...