Rumor has it: Identifying Misinformation in Microblogs

By Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, Qiaozhu Mei. Conference on Empirical Methods in Natural Language Processing, 2011

888 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/acdc7037ac8efdbfdfa2feb94b01cd7de0470a4f

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Citations in the corpus

Fake News Detection on Social Media: A Data Mining Perspective (2017, methodology): “l toward, or against some target entity, event, or idea [53]. Previous stance classication methods mainly rely on hand-crafted linguistic or embedding features on individual posts to predict stances [53;64]. Topic model methods, such as latent dirichlet allocation (LDA) can be applied to learn laten”
The spreading of misinformation online (2016, background): “To counteract this trend, algorithmic-driven solutions have been proposed (24 – 29), e.g., Google (30) is developing a trustworthiness score to rank the results of queries.”
Stance Detection on Social Media: State of the Art and Trends (2020, background): “The Rumours dataset [Qazvinian et al. 2011] is a claim-based stance detection dataset designed for Rumours resolutions.”
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,…”
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, background): “Rumor is commonly deﬁned as information that emerge and spread among people whose truth value is unveriﬁed or intentionally false (Di-Fonzo and Bordia, 2007; Qazvinian et al., 2011).”
Rumor Cascades (2014, background): “…structural, and linguistic features of rumors on Twitter, Gupta et al. (2013) tried to predict whether images being transmitted on Twitter were real or fake; while Qazvinian et al. (2011) attempted to predict whether tweets were factual or not, while also identifying sources of misinformation.”
False rumors detection on Sina Weibo by propagation structures (2015, background): “Our problem is, given a message propagation tree T =〈V , E〉 as well as the meta data associated with V , return whether root(T ) is a false rumor or not.”
Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks (2018, background): “A straightforward approach is to detect fake news based on its text content (Castillo, Mendoza, and Poblete 2011; Qazvinian et al. 2011; Takahashi and Igata 2012; Gupta et al. 2014; Popat 2017).”
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017, unknown): “Similar setting was also considered in (Wu et al., 2015; Qazvinian et al., 2011).”
Misinformation in Social Media: Definition, Manipulation, and Detection (2019, methodology): “The methods either aim to find those suspicious instances [29; 59], or find credible clusters of discussions [6; 7; 52].”
The spread of true and false news online (2018, background): “Some work develops theoretical models of rumor diffusion [37, 38, 39, 40], or methods for rumor detection [41, 42, 43, 44], credibility evaluation [45] or interventions to curtail the spread of rumors [46, 47, 48].”
Combating Fake News (2019, background): “[88] compute probability distributions over users involved in fake and true posts from collected user engagements to model user behavior patterns.”
Real-time Rumor Debunking on Twitter (2015, methodology): “A rumor may be deﬁned as a statement whose truth value is unveriﬁed or deliberately false [7].”
Detection and Resolution of Rumours in Social Media (2017, methodology): “Other datasets, such as that by [Qazvinian et al. 2011], are not suitable for veracity classiﬁcation, as all the rumours are false.”
Comparing Person- and Process-centric Strategies for Obtaining Quality Data on Amazon Mechanical Turk (2015, background): “This task aligns with scholarly work done on credibility annotations in social media [4,25,31].”
A survey on fake news and rumour detection techniques (2019, methodology): “Syntactic features, such as the number of content words (nouns, verbs, adjective), and the presence and frequency of specific POS patterns, have been employed, for instance, in [75, 76, 113].”
Novel Visual and Statistical Image Features for Microblogs News Verification (2017, background): “Semantic features include sentiment scores [3], [19] of tweets, and opinion words [5] in tweet text.”
Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate (2018, methodology): “Following [30], we retrieve tweets related to fake news by compiling queries with a fact-checking website.”
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, unknown): “We believe FakeNewsNet would beneﬁt the research community for studying various topics such as: (early) fake news detection, fake news evolution, fake news mitigation, malicious account detection.”