News Verification by Exploiting Conflicting Social Viewpoints in Microblogs
By Zhiwei Jin, Juan Cao, Yongdong Zhang, Jiebo Luo. AAAI Conference on Artificial Intelligence, 2016
396 citations recorded by the source corpus.
Fake news spreading in social media severely jeopardizes the veracity of online content. Fortunately, with the interactive and open features of microblogs, skeptical and opposing voices against fake news always arise along with it. The conflicting information, ignored by existing studies, is crucial for news verification. In this paper, we take advantage of this "wisdom of crowds" information to improve news verification by mining conflicting viewpoints in microblogs. First, we discover conflicting viewpoints in news tweets with a topic model method. Based on identified tweets' viewpoints, we then build a credibility propagation network of tweets linked with supporting or opposing relations. Finally, with iterative deduction, the credibility propagation on the network generates the final evaluation result for news. Experiments conducted on a real-world data set show that the news verification performance of our approach significantly outperforms those of the baseline approaches.
Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/db318f7e8024421bc5948aa6b21e97132220a192
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Citations in the corpus
- Mining Dual Emotion for Fake News Detection (2019, background): “Beyond news content, social contexts such as texts of comments and reposts, viewpoints and stances of the crowd, and user credibility are emphasized as well [9, 11, 13, 23, 25].”
- dEFEND: Explainable Fake News Detection (2019, methodology): “Post-based features represent users’ social response in term of stances [43], topics [13], or credibility [18].”
- Fake news, disinformation and misinformation in social media: a review (2023, background): “…Sharma et al. 2019; Zhou and Zafarani 2020; Zhang and Ghorbani 2020; Conroy et al. 2015; Celliers and Hattingh 2020; Nakov 2020; Shu et al. 2020c; Jin et al. 2016; Rubin et al. 2016; Balmas 2014; Brewer et al. 2013; Egelhofer and Lecheler 2019; Mustafaraj and Metaxas 2017; Klein and Wueller…”
- FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, methodology): “These features are important signals to study fake news and disinformation in general.(17,18) We perform sentiment analysis on the replies of user posts that spread fake news and real news using one of the state-of-the-art unsupervised sentiment prediction tool called VADER.”
- Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, methodology): “…are news-related posts by users, and edges represent supporting (+) or opposing (-) relations among each pair of posts, e.g., the similarity between each pair of tweets that can be calculated using a distance measure such as Jensen-Shannon [Jin et al. 2016] or Jaccard distance [Jin et al. 2014].”
- A Survey of Fake News (2020, methodology): “…posts by users and edges represent supporting (+) or opposing (–) relations among each pair of posts, such as the similarity between each pair of posts that can be calculated using a distance measure such as Jensen-Shannon divergence [Jin et al. 2016] or Jaccard distance [Jin et al. 2014].”
- A Convolutional Approach for Misinformation Identification (2017, background): “Some other works adopt more effective handcrafted features, such as conflict viewpoints [Jin et al., 2016], temporal properties [Kwon et al., 2013; Ma et al., 2015], users’ feedback [Giudice, 2010; Rieh et al., 2014] and signals tweets containing skepticism [Zhao et al., 2015].”
- Beyond News Contents: The Role of Social Context for Fake News Detection (2017, methodology): “Propagation-based models assume that the credibility of news is highly related to the credibilities of relevant social media posts, which several propagation methods can be applied [10].”
- Novel Visual and Statistical Image Features for Microblogs News Verification (2017, methodology): “With these features, classification-based methods [3]–[8] or graph-based optimization methods [9], [10], [11] are deployed to identify news as real or fake.”
- A survey on fake news and rumour detection techniques (2019, background): “[50], following [92], has exploited topic models [7] to identify conflicting viewpoints in microblogs, and has built a credibility network to determine the veracity of social media posts.”
- FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, background): “ecent work has taken another turn and has explored contextual features of the news dissemination process. They observed distinctive engagement patterns when social users face fake versus factual news [17, 25]. For example, the fake news shown in Table 1 had many engagements shortly after its publica”
- Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs (2017, methodology): “Features based on topic model, such as LDA are also employed to represent high-level abstract semantics in the rumor detection task [14, 31].”
- Understanding User Profiles on Social Media for Fake News Detection (2018, methodology): “Post-based features represent users’ social responses such as stances [5], topics [6].”