A Convolutional Approach for Misinformation Identification

By Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tien-Ping Tan. International Joint Conference on Artificial Intelligence, 2017

508 citations recorded by the source corpus.

The fast expanding of social media fuels the spreading of misinformation which disrupts people's normal lives. It is urgent to achieve goals of misinformation identification and early detection in social media. In dynamic and complicated social media scenarios, some conventional methods mainly concentrate on feature engineering which fail to cover potential features in new scenarios and have difficulty in shaping elaborate high-level interactions among significant features. Moreover, a recent Recurrent Neural Network (RNN) based method suffers from deficiencies that it is not qualified for practical early detection of misinformation and poses a bias to the latest input. In this paper, we propose a novel method, Convolutional Approach for Misinformation Identification (CAMI) based on Convolutional Neural Network (CNN). CAMI can flexibly extract key features scattered among an input sequence and shape high-level interactions among significant features, which help effectively identify misinformation and achieve practical early detection. Experiment results on two large-scale datasets validate the effectiveness of CAMI model on both misinformation identification and early detection tasks.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/7e1f8e723b1d5dd43cff210d68568df8a7ec555c

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Citations in the corpus

Mining Dual Emotion for Fake News Detection (2019, background): “• Weibo-16: The dataset Weibo-16 is firstly proposed in [17] and has been as a benchmark dataset in Chinese [9, 23, 28].”
A survey on fake news and rumour detection techniques (2019, background): “CNNs and paragraph embeddings [59] have been exploited in [106].”
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, background): “Qi et al. (2019) design a CNN-based model to capture image patterns, but their model only works in the case of large samples.”
Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021, methodology): “In addition, the performance of TextGCN is better than that of CNN and SVM-TS on the three datasets.”
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, background): “Thus, some studies have tried to involve the information from the structures of rumor dispersion by invoking Convolutional Neural Network (CNN) based methods (Yu et al. 2017; Yu et al. 2019).”