Detecting Rumors from Microblogs with Recurrent Neural Networks

By Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard Jim Jansen, Kam-Fai Wong, Meeyoung Cha. International Joint Conference on Artificial Intelligence, 2016

1,331 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d9595f9acec303c7ad06eeae8a054743cbaf21ce

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Citations in the corpus

CSI: A Hybrid Deep Model for Fake News Detection (2017, methodology): “In particular, the model has on the order of 52K parameters, whereas GRU-2 has 621K parameter.”
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, methodology): “Figure 4 shows the performances of our Bi-GCN method versus RvNN, SVM-TS, SVM-RBF and DTC at various deadlines for the Weibo and Twitter datasets.”
Mining Dual Emotion for Fake News Detection (2019, methodology): “• Weibo-16: The dataset Weibo-16 is firstly proposed in [17] and has been as a benchmark dataset in Chinese [9, 23, 28].”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, methodology): “They observed distinctive engagement patterns when social users face fake versus factual news [17, 25].”
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums (2019, methodology): “, there has been work on checking the factuality/credibility of a claim, of a news article, or of an information source (Ba et al., 2016; Zubiaga et al., 2016; Ma et al., 2016; Castillo et al., 2011; Baly et al., 2018).”
Fake News Identification on Twitter with Hybrid CNN and RNN Models (2018, methodology): “ and real images posted about the event[12]. Neural networks are a form of machine learning method that have been found to exhibit high accuracy and precision in clustering and classification of text [13]. They also prove effective in the prompt detection of spatio-temporal trends in content propaga”
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs (2017, methodology): “[21] explore the possibility to represent tweets in an event with deep neural networks.”
Fake News Detection on Social Media: A Data Mining Perspective (2017, methodology): “Unsupervised embedding methods, such as recurrent neural network (RNN), are utilized to capture the changes in posts over time [69; 48].”
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (2018, background): “[21] propose a deep learning model to identify fake news.”
Combating Fake News (2019, background): “re": A New Benchmark Dataset for Fake News Detection. [118] https://www.cs.ucsb.edu/ÌČwilliam/data/liar_dataset.zip 7.1.2 Twitter.Detecting Rumors from Microblogs with Recurrent Neural Networks. [61] http: //alt.qcri.org/~wgao/data/rumdect.zip 7.1.3 Weibo. Detecting Rumors from Microblogs with ”
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, background): “(3) mGRU (Ma et al., 2016): a modified gated recurrent unit model for rumor detection, which learns temporal patterns from retweet user profile, along with the source’s features.”
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017, methodology): “GRU: The RNN-based rumor detection model proposed by Ma et al. (2016) with gated recurrent unit for representation learning of high-level features from relevant posts over time.”
A survey on fake news and rumour detection techniques (2019, methodology): “In the context of fake news and rumours, the earliest adoption of RNNs for rumour detection is reported in [63].”
Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021, methodology): “[19] employ Recurrent Neural Networks (RNNs) to learn the hidden features from posts.”
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018, unknown): “…et al., 2015; Volkova et al., 2017) propose supervised methods for detecting deceptive content in social media platforms like Twitter, Sina Weibo, etc. Similarly, approaches like Ma et al. (2016); Ruchansky et al. (2017) use neural network methods to identify fake news and rumors on social media.”