Exploiting Microblog Conversation Structures to Detect Rumors

Li, Jiawen; Sujana, Yudianto; Kao, Hung-Yu

doi:10.18653/v1/2020.coling-main.473

Cited by 24 publications

(13 citation statements)

References 25 publications

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“…Lu and Li (2020) [26] developed graph-aware co-attention networks (GCAN) to detect fake news, which generated an explanation by highlighting the evidence on suspicious re-tweeters and the words of concern. [27] built a conversation structure from source tweet and user comments, and used GNN to encode it. Li et al (2020) [28] crawled user-follower information and built a friendly network based on the followfollowers relationship.…”

Section: Structure-based Methodsmentioning

confidence: 99%

MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

Kao

2021

IEEE Access

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Fake news on social media is a widespread and serious problem in today's society. Existing fake news detection methods focus on finding clues from Long text content, such as original news articles and user comments. This paper solves the problem of fake news detection in more realistic scenarios. Only source shot-text tweet and its retweet users are provided without user comments. We develop a novel neural network based model, Multi-View Attention Networks (MVAN) to detect fake news and provide explanations on social media. The MVAN model includes text semantic attention and propagation structure attention, which ensures that our model can capture information and clues both of source tweet content and propagation structure. In addition, the two attention mechanisms in the model can find key clue words in fake news texts and suspicious users in the propagation structure. We conduct experiments on two real-world datasets, and the results demonstrate that MVAN can significantly outperform state-of-the-art methods by 2.5% in accuracy on average, and produce a reasonable explanation.INDEX TERMS Fake news detection, graph attention networks, attention, deep learning, social media.

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Section: Structure-based Methodsmentioning

confidence: 99%

MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

Kao

2021

IEEE Access

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“…• CSRD: a state-of-the-art model that detect rumors by modeling conversation structure (Li et al, 2020b).…”

Section: Methodsmentioning

confidence: 99%

“…Lu and Li (2020) classified rumor by extracting user's features from their profiles and social interactions. Li et al (2020b) used Graph-SEGA to encode the conversation structure. Li et al (2020a) crawled user-follower information and built a friendly network based on the followfollowers relationship.…”

Section: Rumor Detectionmentioning

confidence: 99%

Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection

Li¹,

Kao

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Existing rumor detection strategies typically provide detection labels while ignoring their explanation. Nonetheless, providing pieces of evidence to explain why a suspicious tweet is rumor is essential. As such, a novel model, LOSIRD, was proposed in this paper. First, LOSIRD mines appropriate evidence sentences and classifies them by automatically checking the veracity of the relationship of the given claim and its evidence from about 5 million Wikipedia documents. LOSIRD then automatically constructs two heterogeneous graph objects to simulate the propagation layout of the tweets and code the relationship of evidence. Finally, a graphSAGE processing component is used in LOSIRD to provide the label and evidence. To the best of our knowledge, we are the first one who combines objective facts and subjective views to verify rumor. The experimental results on two real-world Twitter datasets showed that our model exhibited the best performance in the early rumor detection task and its rumor detection performance outperformed other baseline and state-of-the-art models. Moreover, we confirmed that both objective information and subjective information are fundamental clues for rumor detection.

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“…We define the task as a binary classification task, where y ∈ {T rue, F alse}. For conversation graphs on social networks, we follow previous work (Wei et al, 2019;Li et al, 2020) and define the conversa-tion graph as an undirected graph: G = (X, A), where X ∈ R d denotes the node features and A ∈ R m×m denotes the adjacency matrix.…”

Section: Problem Statementmentioning

confidence: 99%

Towards Robust False Information Detection on Social Networks with Contrastive Learning

et al. 2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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Current works about false information detection based on conversation graphs on social networks focus primarily on two research streams from the standpoint of topic distribution: intopic and cross-topic techniques, which assume that the data topic distribution is identical or cross, respectively. This signifies that all test data topics are seen or unseen by the model. However, these assumptions are too harsh for actual social networks that contain both seen and unseen topics simultaneously, hence restricting their practical application. In light of this, this paper develops a novel open-topic scenario that is better suited to actual social networks. In this open-topic scenario, we empirically find that the existing models suffer from impairment in the detection performance for seen or unseen topic data, resulting in poor overall model performance. To address this issue, we propose a novel Contrastive Adversarial Learning Network, CALN, that employs an unsupervised topic clustering method to capture topic-specific features to enhance the model's performance for seen topics and an unsupervised adversarial learning method to align data representation distributions to enhance the model's generalisation to unseen topics. Experiments on two benchmark datasets and a variety of graph neural networks demonstrate the effectiveness of our approach.

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Exploiting Microblog Conversation Structures to Detect Rumors

Cited by 24 publications

References 25 publications

MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

MVAN: Multi-View Attention Networks for Fake News Detection on Social Media

Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection

Towards Robust False Information Detection on Social Networks with Contrastive Learning

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