Real-time Rumor Debunking on Twitter

By Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li, Rui Fang, Sameena Shah. International Conference on Information and Knowledge Management, 2015

419 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0fe9218d06078df0fdd9462c161c8256ad859bc5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jing Ma
2. Tian Bian
3. Wei Gao
4. Alessandro Bondielli
5. Shengsheng Qian
6. Kam-Fai Wong
7. Xi Xiao
8. Tong Chen

Citations in the corpus

• The spread of low-credibility content by social bots (2017, unknown): “The news verification capabilities of these systems range from completely automatic (TweetCred), to semi-automatic (TwitterTrails, RumorLens, News Tracer).”
• Stance Detection with Bidirectional Conditional Encoding (2016, background): “, target, or is rulebased (Liu et al., 2015) and thus potentially hard to generalise.”
• Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017, methodology): “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).”
• Rumor Detection on Twitter with Tree-structured Recursive Neural Networks (2018, methodology): “For automating rumor detection, most of the previous studies focused on text mining from sequential microblog streams using supervised models based on feature engineering (Castillo et al., 2011; Kwon et al., 2013; Liu et al., 2015; Ma et al., 2015), and more recently deep neural models (Ma et al.”
• Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, background): “Some studies apply more effective features, such as user comments (Giudice 2010), temporal-structural features (Wu, Yang, and Zhu 2015), and the emotional attitude of posts (Liu et al. 2015).”
• Applications of artificial intelligence for disaster management (2020, background): “2017) and rumors (Mendoza et al. 2010; Liu et al. 2015; Wu et al. 2015; Zubiaga et al. 2016, 2018), as well as track trends of”
• A survey on fake news and rumour detection techniques (2019, background): “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].”
• 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].”
• Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (2017, background): “Œey commonly cra‰ features manually from the content, sentiment [51], user pro€les [30, 31, 47], and di‚usion paŠerns of the posts [12, 16, 18, 34, 36, 37].”
• Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021, methodology): “Basically, existing studies on automatic fake news detection can be summarized into two categories: (1) The first one is traditional learning methods [2, 16, 18, 20], which design plenty of hand-crafted features from the media content of posts and the Session 1D: Social Aspects SIGIR ’21, July 11–15”