Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts

By Zhe Zhao, Paul Resnick, Qiaozhu Mei. The Web Conference, 2015

645 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/cd09f6688bd0ab248e3499fe87864d1992f353ac

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Citations in the corpus

Spread of Hate Speech in Online Social Media (2018, background): “However, there are several works that looks into diffusion of fake news [26, 37, 41], Linkedin [3], retweet cascade [8, 9, 18, 37], rumours [11, 17, 20, 22, 45] and Tumblr [1, 2, 7, 42].”
Detecting Rumors from Microblogs with Recurrent Neural Networks (2016, background): “For example, two time series plots in Figure 1 depict the typical shallow patterns of rumor signals from [Zhao et al., 2015].”
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, background): “…studies (Allport and Postman, 1946; Berin-sky, 2017), management science (DiFonzo et al., 1994; Kimmel, 2004) and recently computer science and artiﬁcial intelligence (Qazvinian et al., 2011; Ratkiewicz et al., 2011; Castillo et al., 2011; Hannak et al., 2014; Zhao et al., 2015; Ma et al., 2015).”
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017, methodology): “ures should be extracted from contexts. However, as shown in Fig.1 (b), users’ posts exhibit high duplication in their textual phrases due to the repeated forwarding, reviews, and/or inquiry behavior [49]. „is poses a challenge on e†ciently distilling distinct information from duplication, and …exib”
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, background): “The rapid growth of social media has created fertile soil for the emergence and fast spread of fake news (Zhao et al., 2015), resulting in serious consequences.”
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, background): “Zhao et al. (2015) ﬁnd the enquiry phrases from user responses are useful, and Ma et al. (2016) use recurrent neural networks to learn better representations of user responses.”
Contextual Outlier Interpretation (2017, background): “Typical examples include notable human behaviors in static environment [52], online spam detection [31, 43, 55], public disease outbreaks [51], and dramatic changes in temporal signals [32, 54].”
Emergent: a novel data-set for stance classification (2016, methodology): “The task deﬁned by the Emergent dataset differs from recent work in stance classiﬁcation (Qazvinian et al., 2011; Lukasik et al., 2015; Zhao et al., 2015) not only in the number of claims from which the article headlines are derived, but also in that correct prediction requires considering…”
Novel Visual and Statistical Image Features for Microblogs News Verification (2017, background): “According to a report in China, more than 1/3 trending events on microblogs contain fake information [2].”
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].”
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].”
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, background): “…work for rumor detection mainly focuses on extracting rumor features from the text contents, user proﬁles and propagation structures to learn a classiﬁer from labeled data (Castillo, Men-doza, and Poblete 2011; Yang et al. 2012; Kwon et al. 2013; Liu et al. 2015; Zhao, Resnick, and Mei 2015).”
Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection (2018, background): “The analysis of subjective language on OSN has been deeply studied and applied on different fields varying from sentiment analysis [10]–[12] to sarcasm detection [6], [7] or detection of rumors [13] etc.”
The Limitations of Stylometry for Detecting Machine-Generated Fake News (2019, background): “…such as how content is propagated, by which users, its originating URL, and other metadata (Castillo, Mendoza, and Poblete 2011; Gupta et al. 2014; Zhao, Resnick, and Mei 2015; Kochkina, Liakata, and Zubiaga 2018; Liu and Wu 2018), as well as incorporating users’ explicit feedback, such as abuse…”
A survey on fake news and rumour detection techniques (2019, methodology): “Enquiring tweets were exploited in [109] in order to determine clusters of potential rumours.”