Automatic detection of rumor on Sina Weibo

By Fan Yang, Yang Liu, Xiaohui Yu, Min Yang. MDS '12, 2012

663 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/96b17f09f3afcc9238c1789208eeb016bc2a84d8

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Citations in the corpus

Misinformation in Social Media: Definition, Manipulation, and Detection (2019, background): “SIGKDD Explorations Volume 21, Issue 1 Page 85 microblogging websites such as Twitter and Sina Weibo, and then train a text classifier based on the collected content and labels[56; 54; 58; 11].”
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites (2015, background): “LDA-based topic distribution of microblogs with 18 topics [10] Average length of microblogs [2] # of positive (negative) words in microblogs [2] Average sentiment score of microblogs [2, 10] % of microblogs with URL [2, 10, 11] % of microblogs with smiling (frowning) emoticons [2] % of positive (neg”
Detecting Rumors from Microblogs with Recurrent Neural Networks (2016, methodology): “In following works [Yang et al., 2012; Liu et al., 2015; Ma et al., 2015; Wu et al., 2015], different sets of hand-crafted features were proposed and incorporated to determine whether a claim about an event is credible.”
False Information on Web and Social Media: A Survey (2018, background): “[112] added propagation features such as number of replies and number of retweets to the standard set of features used for their classification tasks, such as content, client (device type), user, and location, to identify false information spread on the Sina Weibo network.”
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, background): “Most previous automatic approaches for rumor detection (Castillo et al., 2011; Yang et al., 2012; Liu et al., 2015) intended to learn a supervised classi-ﬁer by utilizing a wide range of features crafted from post contents, user proﬁles and propagation patterns.”
Combating Fake News (2019, background): “Variants of the above include other network-based features that are slightly extended or tailored to a particular context, such as the inclusion of temporal features [56] or geographic location and type of device (mobile or PC) from which the response was sent [122].”
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018, methodology): “Approaches like (Castillo et al., 2011; Qazvinian et al., 2011; Yang et al., 2012; Xu and Zhao, 2012; Gupta et al., 2013; Zhao et al., 2015; Volkova et al., 2017) propose supervised methods for detecting deceptive content in social media platforms like Twitter, Sina Weibo, etc. Similarly,…”
News Verification by Exploiting Conflicting Social Viewpoints in Microblogs (2016, background): “Subsequently, similar studies (Yang et al. 2012; Sun et al. 2013) are performed to detect rumors on Sina Weibo with several new features.”
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017, methodology): “Feature-based methods (Castillo et al., 2011; Yang et al., 2012; Ma et al., 2015) achieved certain success by employing large feature sets crafted from message contents, user profiles and holistic statistics of diffusion patterns (e.”
Novel Visual and Statistical Image Features for Microblogs News Verification (2017, background): “Apart from above features, some other features, such as type of software client, location from which a tweet is posted [4] are also presented in literature.”
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts (2015, methodology): “Much previous research has tried to develop classiﬁers for a more challenging problem than ours, automatically determining whether a meme that is spreading is true or false ([35, 4, 14, 17]).”
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, background): “Yang et al. (2012) extract account-based features, such as “is veriﬁed”, gender, hometown, and number of followers.”
Real-time Rumor Debunking on Twitter (2015, methodology): “We compared our proposed method with two state-of-the-art algorithms [1, 11].”
FakeBERT: Fake news detection in social media with a BERT-based deep learning approach (2021, background): “Further, Yang et al [49] have arrived with comparative resolutions for detecting false rumours.”
Detection and Resolution of Rumours in Social Media (2017, methodology): “The baselines achieve 85.4% [Castillo et al. 2011] and 77.2% accuracy [Yang et al. 2012].”