GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

Lü, Yi; Li, Cheng-Te

doi:10.18653/v1/2020.acl-main.48

Cited by 296 publications

(161 citation statements)

References 28 publications

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“…Yang et al (2019) also use, somewhat similarly, self-attention to extract n-gram explanations and linguistic analysis to extract features, e.g., verb ratio. Lu and Li (2020) take a slightly different approach to generating explanations. Like Shu et al ( 2019) they make use of a co-attention mechanism, however the work, which looks at fact-checking tweets looks at explainability from three perspectives source tweets, retweet propagation, and retweeter characteristics i.e., suspicious users (see Figure 3(b)).…”

Section: Attention-based Explanationsmentioning

confidence: 99%

“…Kotonya and Toni (2020) formalize three coherence properties for evaluating the quality of explanations: local coherence, and strong and weak global coherence. ExpClaim (Ahmadi et al, 2019) is evaluated against and outperforms CredEye (Popat et al, 2018), and GCAN (Lu and Li, 2020) is shown to outperform dE-FEND (Shu et al, 2019). However, these evaluations are with respect to the prediction, not explanations.…”

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Automated Fact-Checking: A Survey

Kotonya¹,

Toni²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality -that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.

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Section: Attention-based Explanationsmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Explainable Automated Fact-Checking: A Survey

Kotonya¹,

Toni²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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show abstract

“…Khattar et al [38] used textual and visual information in a model variational autoencoder coupled with a binary classifier for the task of fake news detection. Lu and Li [39] integrated attention mechanism with graph neural networks using text information and propagation structure to identify whether the source information is fake or not.…”

Section: Misinformation Detectionmentioning

confidence: 99%

Evaluating vulnerability to fake news in social networks

Rath

Gao

Srivastava

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individuallevel) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using three popular community detection algorithms for twelve real-world news spreading networks collected from Twitter. Experimental results show that the proposed metrics perform significantly better on the fake news spreading networks than on the true news, indicating that our community health assessment model is effective.

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“…The recent Hostile Post Detection task in English takes into account information other than news [11]. For example, the official news is always true.…”

Section: Related Workmentioning

confidence: 99%

Extracting Latent Information from Datasets in CONSTRAINT 2021 Shared Task

Liu

Zhou

2021

Combating Online Hostile Posts in Regional Languages During Emergency Situation

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This paper introduces the result of Team Grenzlinie's experiment in CONSTRAINT 2021 shared task. This task has two subtasks. Subtask1 is the COVID-19 Fake News Detection task in English, a binary classification task. This paper chooses RoBERTa as the pre-trained model, and tries to build a graph from news datasets. Finally, our system achieves an accuracy of 98.64% and an F1-score of 98.64% on the test dataset. Subtask2 is a Hostile Post Detection task in Hindi, a multi-labels task. In this task, XLM-RoBERTa is chosen as the pre-trained model. The adapted threshold is adopted to solve the data unbalanced problem, and then Bi-LSTM, LEAM, LaSO approaches are adopted to obtain more abundant semantic information. The final approach achieves the accuracy of 74.11% and weight F1-score of 81.77% on the test dataset.

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GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

Cited by 296 publications

References 28 publications

Explainable Automated Fact-Checking: A Survey

Explainable Automated Fact-Checking: A Survey

Evaluating vulnerability to fake news in social networks

Extracting Latent Information from Datasets in CONSTRAINT 2021 Shared Task

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