Detect Rumors Using Time Series of Social Context Information on Microblogging Websites

By Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, Kam-Fai Wong. International Conference on Information and Knowledge Management, 2015

672 citations recorded by the source corpus.

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/308fb9872f256dd043e580930169a7fb851cd888

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Citations in the corpus

Beyond News Contents: The Role of Social Context for Fake News Detection (2017, background): “Post-based features represent users’ social response in term of stance [10], topics [16], or credibility [4, 36].”
Fake News Identification on Twitter with Hybrid CNN and RNN Models (2018, neutral): “in time and sequence based predictions [25].”
CSI: A Hybrid Deep Model for Fake News Detection (2017, methodology): “In the main set of experiments, we use two real-world datasets, Twitter and Weibo, to compare the proposed CSI model with ve state-of-the-art models that have been used for similar classi cation tasks and were discussed in Section 2: SVM-TS [25] , DT-Rank [45], DTC [6] , LSTM-1 [24], and GRU-2 [24].”
Understanding User Profiles on Social Media for Fake News Detection (2018, background): “Post-based features represent users’ social responses such as stances [5], topics [6].”
Dynamics of information diffusion and its applications on complex networks (2016, methodology): “It is generally known that rumor detection is a critical problem in information diffusion on social networks, and Kwon and Cha found that the temporal patterns were very different between rumor and no-rumor information [105], which is also an important method to detect the rumors where rumor-like me”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, background): “Recent work has highlighted the importance of incorporating temporality not only for fake news detection [26, 35], but also for modeling online information dissemination [14].”
Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021, methodology): “• SVM-TS [20]: SVM-TS is a method for detecting fake news, which utilizes heuristic rules and a linear SVM classifier.”
Detecting Rumors from Microblogs with Recurrent Neural Networks (2016, methodology): “, [Ma et al., 2015] proposed the linear SVM classifier that uses time-series structures to model the variation of social context features.”
Combating Fake News (2019, methodology): “[62] observe reasonable performance improvements over methods that do not consider”
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017, methodology): “LK-RBF and SVM-TS achieve better results, indicating the ability of feature engineering to help classifiers detect rumors better.”
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, methodology): “- SVM-TS : A linear SVM classiﬁer that uses time-series to model the variation of handcrafted social context features (Ma et al., 2015).”
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.”
Overview of the Special Issue on Trust and Veracity of Information in Social Media (2016, background): “…the credibility of shared content [Gupta et al. 2014], spotting fake content [Martinez-Romo and Araujo 2013; Boididou et al. 2014], and detecting rumors spreading in social media [Qazvinian et al. 2011; Ratkiewicz et al. 2011; Seo et al. 2012; Liu et al. 2015; Ma et al. 2015; Zubiaga et al. 2015].”
Predicting Factuality of Reporting and Bias of News Media Sources (2018, methodology): “…rumor detection have been primarily addressed using information extracted from social media, i.e., based on how users comment on the target claim (Canini et al., 2011; Castillo et al., 2011; Ma et al., 2015, 2016; Zubiaga et al., 2016; Ma et al., 2017; Dungs et al., 2018; Kochkina et al., 2018).”
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, methodology): “(2) SVM-TS (Ma et al., 2015): a linear support vector machine classifier that utilizes the source tweet and the sequence of retweet users’ profiles.”