Predicting the Topical Stance and Political Leaning of Media using Tweets

Stefanov, Peter; Darwish, Kareem; Atanasov, Atanas; Nakov, Preslav

doi:10.18653/v1/2020.acl-main.50

Cited by 66 publications

(56 citation statements)

References 37 publications

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“…A large body of related work in NLP focuses on detecting stance, ideology, or political leaning (Baly et al, 2020;Bamman and Smith, 2015;Iyyer et al, 2014;Johnson et al, 2017;Preoţiuc-Pietro et al, 2017;Luo et al, 2020a;Stefanov et al, 2020). While we show a relationship between framing and political leaning, we argue that frames are often more subtle than overt expressions of stance, and cognitively more salient than other stylistic differences in the language of political actors, thus more challenging to be measured.…”

Section: Related Workmentioning

confidence: 54%

To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence

Ziems¹,

Yang²

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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Framing has significant but subtle effects on public opinion and policy. We propose an NLP framework to measure entity-centric frames. We use it to understand media coverage on police violence in the United States in a new POLICE VIOLENCE FRAME CORPUS of 82k news articles spanning 7k police killings. Our work uncovers more than a dozen framing devices and reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. Conservative sources emphasize when the victim is armed or attacking an officer and are more likely to mention the victim's criminal record. Liberal sources focus more on the underlying systemic injustice, highlighting the victim's race and that they were unarmed. We discover temporary spikes in these injustice frames near highprofile shooting events, and finally, we show protest volume correlates with and precedes media framing decisions. 1

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Section: Related Workmentioning

confidence: 54%

To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence

Ziems¹,

Yang²

2021

Findings of the Association for Computational Linguistics: EMNLP 2021

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“…Preoţiuc-Pietro et al [165] used Word2vec to assist political ideology prediction. Hierarchical LSTM and FastText are also applied to detect political perspective [191] and stance [192] . As to political relation extraction, the topic model is mainly used to look at the relationship between republican legislators [193] or extract events between political actors from news corpora [194] .…”

Section: A12 Embedding-based Representationmentioning

confidence: 99%

“…In the domain of politics, Stefanov et al [192] applied node2vec [34] to a user-to-hashtag graph and a user-tomention graph to learn users' embeddings, which can help better predict the stance and political leaning of media. Li and Goldwasser [191] employed GCN [36] to embed the social information graph, as well as text features, for identifying the political perspective of news media.…”

Section: A22 Embedding-based Representationmentioning

confidence: 99%

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

et al. 2021

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Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS?To explore the answer, we give a thorough review of data representations in CSS for both text and network.Specifically, we summarize existing representations into two schemes, namely symbol-based and embeddingbased representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.

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“…Baly et al (2019) estimate the trustworthiness and political ideology (left/right bias) of news sources as a multi-task problem. Stefanov et al (2020) develop methods to predict the overall political leaning (left, center or right) of online media and popular Twitter users. Political ideology and communicative intents have also been studied in computer vision.…”

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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Predicting the Topical Stance and Political Leaning of Media using Tweets

Cited by 66 publications

References 37 publications

To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence

To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

Analyzing Online Political Advertisements

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