Probabilistic Approaches to Controversy Detection

Jang, Myungha; Foley, John F.; Dori-Hacohen, Shiri; Allan, James

doi:10.1145/2983323.2983911

Cited by 36 publications

(28 citation statements)

References 15 publications

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“…Controversy Analysis on the Web: To identify controversial topics in Web documents, some work has demonstrated that identifying relevant Wikipedia pages as well as building a controversy language model is effective [3,9,11]. Several studies then have formally defined a model for controversy detection [10,21].…”

Section: Related Workmentioning

confidence: 99%

Explaining Controversy on Social Media via Stance Summarization

Jang

Allan

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. In this light, there has been significant work that studies how to identify and measure controversy online. However, we currently lack a tool for effectively understanding controversy in social media. For example, users have to manually examine postings to find the arguments of conflicting stances that make up the controversy.In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances. We focus on Twitter and treat stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic. We formalize the characteristics of a good stance summary and propose a ranking model accordingly. We first evaluate our methods on five controversial topics on Twitter. Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy.

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Section: Related Workmentioning

confidence: 99%

Explaining Controversy on Social Media via Stance Summarization

Jang

Allan

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…Identifying Quantifying Content Network Choi et al [8] Popescu and Pennacchiotti [45] Mejova et al [39] Klenner et al [33] Tsytsarau et al [49] Dori-Hacohen and Allan [14] Jang et al [29] Conover et al [10] Coletto et al [9] Akoglu [2] Amin et al [3] Guerra et al [27] Morales et al [40] Garimella et al [20] inside communities created by like-minded people, who reinforce and endorse the opinions of each other. This phenomenon has been quanti ed in many recent studies [4,18,26].…”

Section: Papermentioning

confidence: 99%

Quantifying Controversy on Social Media

Garimella

Morales

Gionis

et al. 2018

Trans. Soc. Comput.

242

123

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Which topics spark the most heated debates on social media? Identifying those topics is not only interesting from a societal point of view, but also allows the ltering and aggregation of social media content for disseminating news stories. In this paper, we perform a systematic methodological study of controversy detection by using the content and the network structure of social media.Unlike previous work, rather than study controversy in a single hand-picked topic and use domain-speci c knowledge, we take a general approach to study topics in any domain. Our approach to quantifying controversy is based on a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph.We perform an extensive comparison of controversy measures, di erent graph-building approaches, and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We nd that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.

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“…Garimella et al (2017) identified controversial topics using bipartite Twitter follower-graphs, while Dori-Hacohen and Allan (2015) proposed an incidentally supervised binary classification to predict controversial topics via Wikipedia tags. Jang et al (2016) used language modeling to predict controversial documents, based on earlier hypotheses by Cramer (2011): "that language in news discussions is a good indicator of controversy". Choi et al (2010) focused on using sentiment polarity indicators and subtopics, i.e.…”

Section: Related Researchmentioning

confidence: 99%

Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs

Rethmeier¹,

Hübner²,

Hennig³

2018

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

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Comments on web news contain controversies that manifest as inter-group agreementconflicts. Tracking such rapidly evolving controversy could ease conflict resolution or journalist-user interaction. However, this presupposes controversy online-prediction that scales to diverse domains using incidental supervision signals instead of manual labeling. To more deeply interpret commentcontroversy model decisions we frame prediction as binary classification and evaluate baselines and multi-task CNNs that use an auxiliary news-genre-encoder. Finally, we use ablation and interpretability methods to determine the impacts of topic, discourse and sentiment indicators, contextual vs. global word influence, as well as genre-keywords vs. per-genrecontroversy keywords-to find that the models learn plausible controversy features using only incidentally supervised signals.

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Probabilistic Approaches to Controversy Detection

Cited by 36 publications

References 15 publications

Explaining Controversy on Social Media via Stance Summarization

Explaining Controversy on Social Media via Stance Summarization

Quantifying Controversy on Social Media

Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs

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