SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

Gorrell, Genevieve; Kochkina, Elena; Liakata, Maria; Aker, Ahmet; Zubiaga, Arkaitz; Bontcheva, Kalina; Derczynski, Leon

doi:10.18653/v1/s19-2147

Cited by 175 publications

(224 citation statements)

References 25 publications

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“…Rumour classification and rumour verification attract significant attention from the research community in shared tasks like RumourEval [8]. According to the type of data used, rumour detection approaches can be divided into three major categories, content-based, feature-based and propagation-based.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Rumours on Twitter via Stance Transfer Learning

Lin

Zhang

Wang

et al. 2020

Lecture Notes in Computer Science

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Rumour detection on Twitter is an important problem. Existing studies mainly focus on high detection accuracy, which often requires large volumes of data on contents, source credibility or propagation. In this paper we focus on early detection of rumours when data for information sources or propagation is scarce. We observe that tweets attract immediate comments from the public who often express uncertain and questioning attitudes towards rumour tweets. We therefore propose to learn user attitude distribution for Twitter posts from their comments, and then combine it with content analysis for early detection of rumours. Specifically we propose convolutional neural network (CNN) CNN and BERT neural network language models to learn attitude representation for user comments without human annotation via transfer learning based on external data sources for stance classification. We further propose CNN-BiLSTM-and BERT-based deep neural models to combine attitude representation and content representation for early rumour detection. Experiments on real-world rumour datasets show that our BERT-based model can achieve effective early rumour detection and significantly outperform start-of-the-art rumour detection models.

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Section: Related Workmentioning

confidence: 99%

Early Detection of Rumours on Twitter via Stance Transfer Learning

Lin

Zhang

Wang

et al. 2020

Lecture Notes in Computer Science

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“…After cleansing the collection, the final dataset consists of 7.2K sentences provided by 512 unique contributors, for whom demographic data is also collected and made available. The SemEval'17 challenge dataset contains 5.5K crowdsourced annotated claims [11], while the FEVER dataset, with 185K entries, extracts claims from Wikipedia. Semanticspreserving sentence altering techniques are then applied and the resulting claims are annotated by the crowd [25].…”

Section: Use Cases and Exploitationmentioning

confidence: 99%

ClaimsKG: A Knowledge Graph of Fact-Checked Claims

Tchechmedjiev¹,

Fafalios²,

Boland³

et al. 2019

Lecture Notes in Computer Science

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“…For more details about these two tasks, please check the task description paper from the task organizers (Gorrell et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

eventAI at SemEval-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation Information

Zhang

2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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This paper describes our system for SemEval 2019 RumorEval: Determining rumor veracity and support for rumors (SemEval 2019 Task 7). This track has two tasks: Task A is to determine a user's stance towards the source rumor, and Task B is to detect the veracity of the rumor: true, false or unverified. For stance classification, a neural network model with language features is utilized. For rumor verification, our approach exploits information from different dimensions: rumor content, source credibility, user credibility, user stance, event propagation path, etc. We use an ensemble approach in both tasks, which includes neural network models as well as the traditional classification algorithms. Our system is ranked 1 st place in the rumor verification task by both the macro F1 measure and the RMSE measure.

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SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours

Cited by 175 publications

References 25 publications

Early Detection of Rumours on Twitter via Stance Transfer Learning

Early Detection of Rumours on Twitter via Stance Transfer Learning

ClaimsKG: A Knowledge Graph of Fact-Checked Claims

eventAI at SemEval-2019 Task 7: Rumor Detection on Social Media by Exploiting Content, User Credibility and Propagation Information

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