Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

Dinkov, Yoan; Ali, Ahmed; Koychev, Ivan; Nakov, Preslav

doi:10.21437/interspeech.2019-2965

Cited by 21 publications

(24 citation statements)

References 14 publications

(24 reference statements)

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“…In Tanbih, we model the factuality and the bias at the media level, learning from the Media Bias/Fact Check (MBFC) website, which covers over 2,800 news outlets. The model improves over our recent research (Baly et al, 2018(Baly et al, , 2019Dinkov et al, 2019), and combines information from articles published by the target medium, from their Wikipedia page accounts, from their social media accounts (Twitter, Facebook, Youtube) as well as from the social media accounts of the users who interact with the medium. We model factuality on a 3-point scale (low, mixed and high), with 80.1% accuracy (baseline 46.0%), and bias on a 7-point left-to-right scale, with 69% accuracy (baseline 24.7%), and also on a 3-point scale, with 81.9% accuracy (baseline 37.1%).…”

Section: Factuality Of Reporting and Leadingmentioning

confidence: 93%

Tanbih: Get To Know What You Are Reading

Zhang¹,

Martino²,

Barrón-Cedeño

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story. Our system displays news grouped into events and generates media profiles that show the general factuality of reporting, the degree of propagandistic content, hyper-partisanship, leading political ideology, general frame of reporting, and stance with respect to various claims and topics of a news outlet. In addition, we automatically analyse each article to detect whether it is propagandistic and to determine its stance with respect to a number of controversial topics.

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Section: Factuality Of Reporting and Leadingmentioning

confidence: 93%

Tanbih: Get To Know What You Are Reading

Zhang¹,

Martino²,

Barrón-Cedeño

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

Self Cite

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show abstract

“…While its credibility is sometimes questioned, it has been regarded as accurate enough to be used as ground-truth for e.g. media bias classifiers 39,41,42 , fake news studies [43][44][45] , and automatic fact-checking systems [46][47][48] .…”

Section: Methodsmentioning

confidence: 99%

“…MBFC groups sources into 8 categories: left (92 sources in our list), left-center (255), center (147), right-center (125), right (53), pro-science (27), fake-news (21), conspiracy (17), satire (2). We merged the last three categories to one-low-Q-news (41). Following the website, sources marked as left are "moderately to strongly biased toward liberal causes through story selection and/or political affiliation", left-center have "slight to moderate liberal bias", sources marked as right are "moderately to strongly biased toward conservative causes" and right-center "media sources are slightly to moderately conservative in bias".…”

Section: Methodsmentioning

confidence: 99%

A calibrated measure to compare fluctuations of different entities across timescales

Chołoniewski

Sienkiewicz

Dretnik

et al. 2020

Sci Rep

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A common way to learn about a system’s properties is to analyze temporal fluctuations in associated variables. However, conclusions based on fluctuations from a single entity can be misleading when used without proper reference to other comparable entities or when examined only on one timescale. Here we introduce a method that uses predictions from a fluctuation scaling law as a benchmark for the observed standard deviations. Differences from the benchmark (residuals) are aggregated across multiple timescales using Principal Component Analysis to reduce data dimensionality. The first component score is a calibrated measure of fluctuations—the reactivityRA of a given entity. We apply our method to activity records from the media industry using data from the Event Registry news aggregator—over 32M articles on selected topics published by over 8000 news outlets. Our approach distinguishes between different news outlet reporting styles: high reactivity points to activity fluctuations larger than expected, reflecting a bursty reporting style, whereas low reactivity suggests a relatively stable reporting style. Combining our method with the political bias detector Media Bias/Fact Check we quantify the relative reporting styles for different topics of mainly US media sources grouped by political orientation. The results suggest that news outlets with a liberal bias tended to be the least reactive while conservative news outlets were the most reactive.

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“…Thus, we use YouTube channels by modeling their textual and acoustic contents to predict the political bias and the factuality of reporting of the target news medium. This source of information is relatively underexplored, but it has demonstrated potential for modeling bias (Dinkov et al, 2019) and factuality (Kopev et al, 2019).…”

Section: Youtube Video Channelsmentioning

confidence: 99%

What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context

Baly¹,

Karadzhov²,

An³

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Predicting the political bias and the factuality of reporting of entire news outlets are critical elements of media profiling, which is an understudied but an increasingly important research direction. The present level of proliferation of fake, biased, and propagandistic content online, has made it impossible to fact-check every single suspicious claim, either manually or automatically. Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content. This approach makes it possible to detect likely "fake news" the moment they are published, by simply checking the reliability of their source.From a practical perspective, political bias and factuality of reporting have a linguistic aspect but also a social context. Here, we study the impact of both, namely (i) what was written (i.e., what was published by the target medium, and how it describes itself on Twitter) vs. (ii) who read it (i.e., analyzing the readers of the target medium on Facebook, Twitter, and YouTube). We further study (iii) what was written about the target medium on Wikipedia. The evaluation results show that what was written matters most, and that putting all information sources together yields huge improvements over the current state-of-the-art.

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Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information

Cited by 21 publications

References 14 publications

Tanbih: Get To Know What You Are Reading

Tanbih: Get To Know What You Are Reading

A calibrated measure to compare fluctuations of different entities across timescales

What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context

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