Identifying Controversial Issues and Their Sub-topics in News Articles

Choi, Yuni; Jung, Yuchul; Myaeng, Sung-Hyon

doi:10.1007/978-3-642-13601-6_16

Cited by 51 publications

(53 citation statements)

References 6 publications

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“…One approach that can apply to controversy is sentiment analysis, used to detect words that indicate high polarity and opinion [5,10]. However, unlike sentiment, "controversies are much more complex and opinions are often expressed in subtle forms, which makes determining pro/con polarities much more difficult than [...] prior work on opinion mining" [3, p. 523].…”

Section: Related Workmentioning

confidence: 99%

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Detecting controversy on the web

Dori-Hacohen

Allan

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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A useful feature to facilitate critical literacy would alert users when they are reading a controversial web page. This requires solving a binary classification problem: does a given web page discuss a controversial topic? We explore the feasibility of solving the problem by treating it as supervised k-nearest-neighbor classification. Our approach (1) maps a webpage to a set of neighboring Wikipedia articles which were labeled on a controversiality metric; (2) coalesces those labels into an estimate of the webpage's controversiality; and finally (3) converts the estimate to a binary value using a threshold. We demonstrate the applicability of our approach by validating it on a set of webpages drawn from seed queries. We show absolute gains of 22% in F0.5 on our test set over a sentiment-based approach, highlighting that detecting controversy is more complex than simply detecting opinions.

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Section: Related Workmentioning

confidence: 99%

“…Work on controversy outside Wikipedia has made progress on targeted domains, e.g. Twitter [10] and news [3,5]; they largely considered politics and politicians. In our case, we would like to approach all controversies, whether political, medical, or religious.…”

Section: Related Workmentioning

confidence: 99%

Detecting controversy on the web

Dori-Hacohen

Allan

2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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“…To quantify controversiality in online news, Choi, Jung, and Myaeng (2010) leveraged positive and/or negative sentiment words to compute the degree of controversiality. Mejova and colleagues (2014) report a high correlation between a) controversial issues and b) the use of negative affect and biased language.…”

Section: Controversiality Detection In Online Newsmentioning

confidence: 99%

Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics

Addawood¹,

Rezapour²,

Abdar³

et al. 2017

Proceedings of the Second Workshop on NLP and Computational Social Science

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In this paper, we evaluate the predictability of tweets associated with controversial versus non-controversial topics. As a first step, we crowd-sourced the scoring of a predefined set of topics on a Likert scale from non-controversial to controversial. Our feature set entails and goes beyond sentiment features, e.g., by leveraging empathic language and other features that have been previously used, but are new for this particular study. We find focusing on the structural characteristics of tweets to be beneficial for this task. Using a combination of emphatic, language-specific, and Twitter-specific features for supervised learning resulted in 87% accuracy (F1) for cross-validation of the training set and 63.4% accuracy when using the test set. Our analysis shows that features specific to Twitter or social media in general are more prevalent in tweets on controversial topics than in non-controversial ones. To test the premise of the paper, we conducted two additional sets of experiments, which led to mixed results. This finding will inform our future investigations into the relationship between language use on social media and the perceived controversiality of topics.

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“…Additionally, discourse markers and sentiment features have been found to assist with disagreement detection in collections of news documents on a particular topic, as well as reviews (Choi et al, 2010;Awadallah et al, 2012;Popescu et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure

Allen¹,

Carenini²,

Ng³

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Casual online forums such as Reddit, Slashdot and Digg, are continuing to increase in popularity as a means of communication. Detecting disagreement in this domain is a considerable challenge. Many topics are unique to the conversation on the forum, and the appearance of disagreement may be much more subtle than on political blogs or social media sites such as twitter. In this analysis we present a crowd-sourced annotated corpus for topic level disagreement detection in Slashdot, showing that disagreement detection in this domain is difficult even for humans. We then proceed to show that a new set of features determined from the rhetorical structure of the conversation significantly improves the performance on disagreement detection over a baseline consisting of unigram/bigram features, discourse markers, structural features and meta-post features.

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Identifying Controversial Issues and Their Sub-topics in News Articles

Cited by 51 publications

References 6 publications

Detecting controversy on the web

Detecting controversy on the web

Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics

Detecting Disagreement in Conversations using Pseudo-Monologic Rhetorical Structure

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