Topic-Specific Sentiment Analysis Can Help Identify Political Ideology

Bhatia, Sumit; Deepak, P

doi:10.18653/v1/w18-6212

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…Computational approaches to media bias focus on factuality (Baly et al, 2018), political ideology (Iyyer et al, 2014), and information quality (Rashkin et al, 2017). Bias detection has been done at different granularity levels: single sentences (Bhatia and Deepak, 2018), articles (Kulkarni et al, 2018), and media sources (Baly et al, 2019). Recently, the authors of this paper studied how the two granularity levels "sentence" and "discourse" affect each other.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

Chen¹,

Khatib²,

Wachsmuth³

et al. 2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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Media organizations bear great reponsibility because of their considerable influence on shaping beliefs and positions of our society. Any form of media can contain overly biased content, e.g., by reporting on political events in a selective or incomplete manner. A relevant question hence is whether and how such form of imbalanced news coverage can be exposed. The research presented in this paper addresses not only the automatic detection of bias but goes one step further in that it explores how political bias and unfairness are manifested linguistically. In this regard we utilize a new corpus of 6964 news articles with labels derived from adfontesmedia.com and develop a neural model for bias assessment. By analyzing this model on article excerpts, we find insightful bias patterns at different levels of text granularity, from single words to the whole article discourse.

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Section: Related Workmentioning

confidence: 99%

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

Chen¹,

Khatib²,

Wachsmuth³

et al. 2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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“…Inferring the political ideology of various types of text including news articles, political speeches and social media has been vastly studied in NLP (Lin et al, 2008;Gerrish and Blei, 2011;Sim et al, 2013;Iyyer et al, 2014;Preot ¸iuc-Pietro et al, 2017;Kulkarni et al, 2018;Stefanov et al, 2020). Bhatia and P (2018) exploit topic-specific sentiment analysis for ideology detection (i.e. conservative, liberal) in speeches from the U.S. Congress.…”

Section: Political Ideology Predictionmentioning

confidence: 99%

Analyzing Online Political Advertisements

Villegas¹,

Mokaram²,

Αλέτρας³

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Online political advertising is a central aspect of modern election campaigning for influencing public opinion. Computational analysis of political ads is of utmost importance in political science to understand the characteristics of digital campaigning. It is also important in computational linguistics to study features of political discourse and communication on a large scale. In this work, we present the first computational study on online political ads with the aim to (1) infer the political ideology of an ad sponsor; and (2) identify whether the sponsor is an official political party or a third-party organization. We develop two new large datasets for the two tasks consisting of ads from the U.S.. Evaluation results show that our approach that combines textual and visual information from pre-trained neural models outperforms a state-of-the-art method for generic commercial ad classification. Finally, we provide an in-depth analysis of the limitations of our best-performing models and linguistic analysis to study the characteristics of political ads discourse. 1

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“…Balahur et al (2009) combine polarity with party classification, a task that we consider to be a form of ideology detection, but which they name "source classification". Indeed, this is another task that suffers from a lack of clarity over terminology, with some studies considering party affiliation to be a proxy for ideology (Diermeier et al, 2012;Jensen et al, 2012;Kapočiūtė-Dzikienė & Krupavičius, 2014;Taddy, 2013), while others do not make this connection, extracting information about speakers' ideologies from their sentiment towards different topics (Bhatia & P, 2018;Chen et al, 2017;Nguyen et al, 2013), or training a model on examples that have been explicitly labelled by ideology, and not party membership (Iyyer et al, 2014). Yet others perform party classification, making no mention of the relationship between party membership and ideology (Balahur et al, 2009;Burfoot, 2008;Lapponi et al, 2018;Lefait & Kechadi, 2010).…”

Section: Tasksmentioning

confidence: 99%

Sentiment and position-taking analysis of parliamentary debates: a systematic literature review

Abercrombie

Batista-Navarro

2020

J Comput Soc Sc

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Parliamentary and legislative debate transcripts provide access to information concerning the opinions, positions and policy preferences of elected politicians. They attract attention from researchers from a wide variety of backgrounds, from political and social sciences to computer science. As a result, the problem of automatic sentiment and position-taking analysis has been tackled from different perspectives, using varying approaches and methods, and with relatively little collaboration or cross-pollination of ideas. The existing research is scattered across publications from various fields and venues. In this article we present the results of a systematic literature review of 61 studies, all of which address the automatic analysis of the sentiment and opinions expressed and positions taken by speakers in parliamentary (and other legislative) debates. In this review, we discuss the available research with regard to the aims and objectives of the researchers who work on these problems, the automatic analysis tasks they undertake, and the approaches and methods they use.We conclude by summarizing their findings, discussing the challenges of applying computational analysis to parliamentary debates, and suggesting possible avenues for further research.

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Topic-Specific Sentiment Analysis Can Help Identify Political Ideology

Cited by 12 publications

References 10 publications

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity

Analyzing Online Political Advertisements

Sentiment and position-taking analysis of parliamentary debates: a systematic literature review

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