False rumors detection on Sina Weibo by propagation structures

By Ke Wu, Song Yang, Kenny Q. Zhu. IEEE International Conference on Data Engineering, 2015

579 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/d7a6083c5dcac0d03335766c8b99f0d67e69be5c

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In Statistical and Nonlinear Physics

Citations in the corpus

Combating Fake News (2019, background): “[120] similarly defined a random walk graph kernel [46] to calculate similarity between different propagation trees.”
False Information on Web and Social Media: A Survey (2018, background): “[110], who also studied 11,466 rumors on Sina Weibo by using propagation information.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, methodology): “2012], or indirectly through their posts [Dungs et al. 2018; Jin et al. 2014, 2016; Ma et al. 2018a,b; Wu et al. 2015] and interconnections [Shu et al.”
Detection and Resolution of Rumours in Social Media (2017, background): “It is also worthwhile noting that the authors, like [Wu et al. 2015], work with rumours that have at least a good volume of tweets, in this case 1,000 tweets.”
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, background): “Although UD-GCN has the ability to handle the global structural features of rumor dispersion, it does not consider the direction of the rumor propagation, which however has been shown to be an important clue for rumor detection (Wu, Yang, and Zhu 2015).”
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, background): “Visual features are important for news veriﬁ-cation (Jin et al., 2016; Shu et al., 2017), such as clarity score (Jin et al., 2016), the number of images (Wu et al., 2015; Jin et al., 2016).”
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, methodology): “Wu et al. (2015) proposed a hybrid SVM classiﬁer which combines a RBF kernel and a random-walk-based graph kernel to capture both ﬂat and propagation patterns for detecting rumors on Sina Weibo.”
A survey on fake news and rumour detection techniques (2019, methodology): “Moreover, many studies consider the age of the account on the platform, the presence of a clear description for the profile and of URLs linking to external resources [15, 17, 62, 64, 103, 105, 113].”
A Survey of Fake News (2020, methodology): “2018], or allow comparing the overall structure of a cascade with that of other cascades by computing similarities [Wu et al. 2015].”
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017, background): “Following that, various features corresponding to message contents, user profiles and statistics of propagation patterns were proposed in many studies (Yang et al., 2012; Wu et al., 2015; Sun et al., 2013; Liu et al., 2015).”
Real-time Rumor Debunking on Twitter (2015, methodology): “Thus, recent research on rumor detection using propagation networks in Sina Weibo [10] cannot be applied to Twitter.”
Detecting Rumors from Microblogs with Recurrent Neural Networks (2016, methodology): “…user characteristics, and diffusion patterns of the posts [Castillo et al. , 2011; Yang et al. , 2012; Kwon et al. , 2013; Liu et al. , 2015; Ma et al. , 2015; Wu et al. , 2015], or simply exploited patterns expressed using regular expressions to discover rumors in tweets [Zhao et al. , 2015].”
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (2018, background): “The basic features of attached images in the posts are explored in the work [12, 15, 24, 31].”
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017, background): “agation pa−erns of rumors were developed by Wu et al. through utilizing a message propagation tree where each node represents a text message to classify whether the root of the tree is a rumor or not [37]. In [18], a dynamic time series structure was proposed to capture the temporal features based o”
News Verification by Exploiting Conflicting Social Viewpoints in Microblogs (2016, methodology): “Kwon(2013) (Kwon et al. 2013; Wu, Yang, and Zhu 2015; Gupta et al. 2013): A classiﬁcation method at tweet level.”