From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles
Peter Bourgonje,Julián Moreno Schneider,Georg Rehm +2 more
- pp 84-89
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TLDR
This work wants to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines.Abstract:
We present a system for the detection of the stance of headlines with regard to their corresponding article bodies The approach can be applied in fake news, especially clickbait detection scenarios The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 8959read more
Citations
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A Stylometric Inquiry into Hyperpartisan and Fake News
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A Stylometric Inquiry into Hyperpartisan and Fake News
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Fake News: A Survey of Research, Detection Methods, and Opportunities.
Xinyi Zhou,Reza Zafarani +1 more
TL;DR: This survey comprehensively and systematically reviews fake news research and identifies and specifies fundamental theories across various disciplines, e.g., psychology and social science, to facilitate and enhance the interdisciplinary research of fake news.
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Multiple features based approach for automatic fake news detection on social networks using deep learning
TL;DR: This paper introduces automatic fake news detection approach in chrome environment on which it can detect fake news on Facebook, and uses multiple features associated with Facebook account with some news content features to analyze the behavior of the account through deep learning.
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Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
TL;DR: A hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square, to determine the relative stance of a news article towards its headline.
References
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Social Media and Fake News in the 2016 Election
Hunt Allcott,Matthew Gentzkow +1 more
TL;DR: The authors found that people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks, and that the average American adult saw on the order of one or perhaps several fake news stories in the months around the 2016 U.S. presidential election, with just over half of those who recalled seeing them believing them.
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Twitter under crisis: can we trust what we RT?
TL;DR: The behavior of Twitter users under an emergency situation is explored and it is shown that it is posible to detect rumors by using aggregate analysis on tweets, and that the propagation of tweets that correspond to rumors differs from tweets that spread news.
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Automatic deception detection: methods for finding fake news
TL;DR: This research surveys the current state‐of‐the‐art technologies that are instrumental in the adoption and development of fake news detection, as well as various formats and genres.
Proceedings ArticleDOI
Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter.
TL;DR: It is found that amateur annotators are more likely than expert annotators to label items as hate speech, and that systems training on expert annotations outperform systems trained on amateur annotations.
Proceedings Article
Locate the hate: detecting tweets against blacks
Irene Kwok,Yuzhou Wang +1 more
TL;DR: A supervised machine learning approach is applied, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels "racist" and "nonracist", suggesting that with further improvements, this work can contribute data on the sources of anti-black hate speech.