Hotspots of news articles: Joint mining of news text &amp; social media to discover controversial points in news

Lourentzou, Ismini; Dyer, Graham; Sharma, Abhishek; Zhai, ChengXiang

doi:10.1109/bigdata.2015.7364132

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…No, whether a topic (or entity/hashtag/word) has been controversial [a distinction also made by Addawood et al (2017)] (Popescu and Pennacchiotti, 2010;Choi et al, 2010;Cao et al, 2015;Lourentzou et al, 2015;Addawood et al, 2017;Al-Ayyoub et al, 2017;Garimella et al, 2018) No, whether a conversation contained disagreement (Mishne and Glance, 2006;Yin et al, 2012;Allen et al, 2014;Wang and Cardie, 2014) or mapping the disagreements (Awadallah et al, 2012;Marres, 2015;Borra et al, 2015;Liu et al, 2018) No, the task is, for the given textual item, predict antisocial behavior in the ensuing discussion (Zhang et al, 2018b,a), or subsequent comment volume/popularity/structure (Szabo and Huberman, 2010;Kim et al, 2011;Tatar et al, 2011;Backstrom et al, 2013;He et al, 2014;Zhang et al, 2018b), or eventual post article score (Rangwala and Jamali, 2010;Szabo and Huberman, 2010),; but all where, like us, the paradigm is early detection No, only info available at the item's creation (Dori-Hacohen and Allan, 2013;Mejova et al, 2014;Klenner et al, 2014;Addawood et al, 2017;Timmermans et al, 2017;Rethmeier et al, 2018;Kaplun et al, 2018) or the entire ensuing revision/discussion history (Rad and Barbosa, 2012;. N.B.…”

Section: Introductionmentioning

confidence: 99%

Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features

Hessel¹,

Lee²

2019

Proceedings of the 2019 Conference of the North

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Controversial posts are those that split the preferences of a community, receiving both significant positive and significant negative feedback. Our inclusion of the word "community" here is deliberate: what is controversial to some audiences may not be so to others. Using data from several different communities on reddit.com, we predict the ultimate controversiality of posts, leveraging features drawn from both the textual content and the tree structure of the early comments that initiate the discussion. We find that even when only a handful of comments are available, e.g., the first 5 comments made within 15 minutes of the original post, discussion features often add predictive capacity to strong content-andrate only baselines. Additional experiments on domain transfer suggest that conversationstructure features often generalize to other communities better than conversation-content features do.

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Section: Introductionmentioning

confidence: 99%

Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features

Hessel¹,

Lee²

2019

Proceedings of the 2019 Conference of the North

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“…Event detection has been studied in the long-running Topic Detection and Tracking (TDT) [44] research programme. Some of the event detection papers propose methods for finding events using news articles [45–53], others focus on event detection directly from tweets [54–63] and a third group uses both news articles and tweets [64–68]. Event detection methods can be categorised into two approaches: document-pivot approach [52,69–71] and feature-pivot approach [72–74].…”

Section: Related Workmentioning

confidence: 99%

Online news media website ranking using user-generated content

Karimi

Shakery

Verma

2020

Journal of Information Science

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News media websites are important online resources that have drawn great attention of text mining researchers. The main aim of this study is to propose a framework for ranking online news websites from different viewpoints. The ranking of news websites is useful information, which can benefit many news-related tasks such as news retrieval and news recommendation. In the proposed framework, the ranking of news websites is obtained by calculating three measures introduced in the paper and based on user-generated content. Each proposed measure is concerned with the performance of news websites from a particular viewpoint including the completeness of news reports, the diversity of events being covered by the website and its speed. The use of user-generated content in this framework, as a partly-unbiased, real-time and low cost content on the web distinguishes the proposed news website ranking framework from the literature. The results obtained for three prominent news websites, BBC, CNN, NYTimes, show that BBC has the best performance in terms of news completeness and speed, and NYTimes has the best diversity in comparison with the other two websites.

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“…Mejova et al (2014) analyzed language use in controversial news articles and found that a writer may choose to highlight the negative aspects of the opposing view rather than emphasizing the positive aspects of ones view. Lourentzou et al (2015) utilize the sentiments expressed in social media comments to identify controversial portions of news articles. Given a news article and its associated comments on social media, the paper links comments with each sentence of the article (by using a sentence as a query and retrieving comments using BM25 score).…”

Section: Sentiment Analysis For Controversy Detectionmentioning

confidence: 99%

Topic-Specific Sentiment Analysis Can Help Identify Political Ideology

Bhatia

Deepak²

2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Ideological leanings of an individual can often be gauged by the sentiment one expresses about different issues. We propose a simple framework that represents a political ideology as a distribution of sentiment polarities towards a set of topics. This representation can then be used to detect ideological leanings of documents (speeches, news articles, etc.) based on the sentiments expressed towards different topics. Experiments performed using a widely used dataset show the promise of our proposed approach that achieves comparable performance to other methods despite being much simpler and more interpretable.

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Hotspots of news articles: Joint mining of news text & social media to discover controversial points in news

Cited by 10 publications

References 8 publications

Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features

Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features

Online news media website ranking using user-generated content

Topic-Specific Sentiment Analysis Can Help Identify Political Ideology

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