Tanbih: Get To Know What You Are Reading

Zhang, Yifan; Martino, Giovanni Da San; Barrón-Cedeño, Alberto; Romeo, Salvatore; An, Jisun; Kwak, Haewoon; Staykovski, Todor; Jaradat, Israa; Karadzhov, Georgi; Baly, Ramy; Darwish, Kareem; Glass, James; Nakov, Preslav

doi:10.18653/v1/d19-3038

Cited by 16 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Framing refers to representing different salient aspects and perspectives for the purpose of conveying the latent meaning about an issue (Entman, 1993). Recent work on automatically identifying media frames includes developing coding schemes and semi-automated methods (Boydstun et al, 2013), datasets such as the Media Frames Corpus (Card et al, 2015), and systems to automatically detect media frames (Liu et al, 2019;Zhang et al, 2019), large-scale automatic analysis of news articles , and semi-supervised approaches (Cheeks et al, 2020).…”

Section: Framingmentioning

confidence: 99%

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

Nakov¹,

Alam²,

Shaar³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

Self Cite

View full text Add to dashboard Cite

While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer "fake news" may hinder efforts to recover from the first one. With this in mind, we performed an extensive analysis of Arabic and English tweets about COVID-19 vaccines, with focus on messages originating from Qatar. We found that Arabic tweets contain a lot of false information and rumors, while English tweets are mostly factual. However, English tweets are much more propagandistic than Arabic ones. In terms of propaganda techniques, about half of the Arabic tweets express doubt, and 1/5 use loaded language, while English tweets are abundant in loaded language, exaggeration, fear, name-calling, doubt, and flag-waving. Finally, in terms of framing, Arabic tweets adopt a health and safety perspective, while in English economic concerns dominate.

show abstract

Section: Framingmentioning

confidence: 99%

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

Nakov¹,

Alam²,

Shaar³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is important for news media as the same topics can be discussed from different perspectives, which can influence our understanding, beliefs, and attitudes regarding what is happening in our society. There has been recent work on automatically identifying media frames, which includes developing coding schemes and datasets such as the Media Frames Corpus (Card et al, 2015), developing systems to automatically detect media frames (Liu et al, 2019;Zhang et al, 2019), large scale automatic analysis of New York Times Articles (Kwak et al, 2020), and a semisupervised approach to detecting frames in online news sources (Cheeks et al, 2020).…”

Section: Framingmentioning

confidence: 99%

COVID-19 in Bulgarian Social Media: Factuality, Harmfulness, Propaganda, and Framing

Nakov¹,

Alam²,

Shaar³

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

Self Cite

View full text Add to dashboard Cite

With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic. Fighting this infodemic is currently ranked very high on the list of priorities of the World Health Organization, with dangers ranging from promoting fake cures, rumors, and conspiracy theories to spreading xenophobia and panic. With this in mind, we studied how COVID-19 is discussed in Bulgarian social media in terms of factuality, harmfulness, propaganda, and framing. We found that most Bulgarian tweets contain verifiable factual claims, are factually true, are of potential public interest, are not harmful, and are too trivial to fact-check; moreover, zooming into harmful tweets, we found that they spread not only rumors but also panic. We further analyzed articles shared in Bulgarian partisan pro/con-COVID-19 Facebook groups and found that propaganda is more prevalent in skeptical articles, which use doubt, flag waving, and slogans to convey their message; in contrast, concerned ones appeal to emotions, fear, and authority; moreover, skeptical articles frame the issue as one of quality of life, policy, legality, economy, and politics, while concerned articles focus on health & safety.

show abstract

“…The proliferation of false information has attracted a lot of research interest recently. This includes challenging the truthiness of news (Brill, 2001;Hardalov et al, 2016;Potthast et al, 2018), of news sources (Baly et al, 2018, and of social media posts (Canini et al, 2011;Castillo et al, 2011;Zubiaga et al, 2016), as well as studying credibility, influence, bias, and propaganda (Ba et al, 2016;Chen et al, 2013;Mihaylov et al, 2015;Kulkarni et al, 2018;Baly et al, 2018;Mihaylov et al, 2018;Barrón-Cedeño et al, 2019;Zhang et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

Vasileva¹,

Atanasova²,

Màrquez³

et al. 2019

Proceedings - Natural Language Processing in a Deep Learning World

Self Cite

View full text Add to dashboard Cite

We propose a multi-task deep-learning approach for estimating the checkworthiness of claims in political debates. Given a political debate, such as the 2016 US Presidential and Vice-Presidential ones, the task is to predict which statements in the debate should be prioritized for fact-checking. While different factchecking organizations would naturally make different choices when analyzing the same debate, we show that it pays to learn from multiple sources simultaneously (PolitiFact, FactCheck, ABC, CNN, NPR, NYT, Chicago Tribune, The Guardian, and Washington Post) in a multi-task learning setup, even when a particular source is chosen as a target to imitate. Our evaluation shows state-of-the-art results on a standard dataset for the task of check-worthiness prediction.

show abstract

Tanbih: Get To Know What You Are Reading

Cited by 16 publications

References 18 publications

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar

COVID-19 in Bulgarian Social Media: Factuality, Harmfulness, Propaganda, and Framing

It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction

Contact Info

Product

Resources

About