Predicting Factuality of Reporting and Bias of News Media Sources

Baly, Ramy; Karadzhov, Georgi E.; Alexandrov, Dimitar; Glass, James; Nakov, Preslav

doi:10.48550/arxiv.1810.01765

Cited by 20 publications

(38 citation statements)

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“…We scraped the RSS feeds of each news source twice a day starting on 02/02/2018 using the Python libraries feedparser and goose. For source selection, we start with mainstream outlets (from both the U.S. and the U.K.) and alternative sources that are mentioned in other misinformation studies (Starbird 2017;Baly et al 2018). We then use the Google Search API to expand the number of sources in the collection.…”

Section: Datamentioning

confidence: 99%

“…This rise in low-quality and potentially malicious news producers has been the focus of many recent studies such as those focusing on detecting false content (Potthast et al 2017;Popat et al 2016;Singhania, Fernandez, and Rao 2017;Baly et al 2018). Some other studies have focused on the tactics used to spread low-quality news, such as the use of social bots (Shao et al 2017) and the structures of headlines to get higher attention and clicks (Horne and Adalı 2017;Chakraborty et al 2016).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media

Horne¹,

Nørregaard²,

Adalı³

2019

Preprint

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In this paper, we analyze content sharing between news sources in the alternative and mainstream media using a dataset of 713K articles and 194 sources. We find that content sharing happens in tightly formed communities, and these communities represent relatively homogeneous portions of the media landscape. Through a mix-method analysis, we find several primary content sharing behaviors. First, we find that the vast majority of shared articles are only shared with similar news sources (i.e. same community). Second, we find that despite these echo-chambers of sharing, specific sources, such as The Drudge Report, mix content from both mainstream and conspiracy communities. Third, we show that while these differing communities do not always share news articles, they do report on the same events, but often with competing and counter-narratives. Overall, we find that the news is homogeneous within communities and diverse in between, creating different spirals of sameness.

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Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media

Horne¹,

Nørregaard²,

Adalı³

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, to investigate news images, Jin et al [14] defined a set of visual features to predict fake news within a traditional statistical learning framework, and Wang et al [32] employed a deep neural network (VGG-19) to help extract the latent representation of news images. Baly et al [1] characterized fake news articles by their source websites, e.g., if they have a Wikipedia page, if their URLs contain digits or domain extensions such as .co, .com, .gov, and their Web traffic information. Nevertheless, few research efforts have focused on the authors who create and write the [true or fake] news, which we investigate in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…Earlier research has focused on assessing source credibility by looking at the URL associated with any news article [1], including features such as whether a website contains the https prefix, numbers, or .gov, .co, .com domain extensions. In our data, 354 news articles used the http prefix, 15 used the https prefix, and 37 had no URL.…”

Section: Source Credibilitymentioning

confidence: 99%

Credibility-based Fake News Detection

Sitaula¹,

Mohan²,

Grygiel³

et al. 2019

Preprint

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Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author's history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.

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“…Mihalcea and Strapparvva used tokenization and stemming for preprocessing the data and applied Naıve Bayes algorithm and Support Vector Machine (SVM) for the classification. In recent research the linguistic style [29], [2], [26] and source of the text are considered as the most critical factors to decide the genuineness of a fact or claim.…”

Section: Introductionmentioning

confidence: 99%

Claim Verification using a Multi-GAN based Model

Hatua¹,

Mukherjee²,

Verma³

2021

Preprint

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This article describes research on claim verification carried out using a multiple GAN-based model. The proposed model consists of three pairs of generators and discriminators. The generator and discriminator pairs are responsible for generating synthetic data for supported and refuted claims and claim labels. A theoretical discussion about the proposed model is provided to validate the equilibrium state of the model. The proposed model is applied to the FEVER dataset, and a pre-trained language model is used for the input text data. The synthetically generated data helps to gain information which helps the model to perform better than state of the art models and other standard classifiers.

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Predicting Factuality of Reporting and Bias of News Media Sources

Cited by 20 publications

References 0 publications

Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media

Different Spirals of Sameness: A Study of Content Sharing in Mainstream and Alternative Media

Credibility-based Fake News Detection

Claim Verification using a Multi-GAN based Model

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