ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models

Feixiang Wang, Man Lan*, Yuanbin Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations

Abstract

This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a plethora of replied tweets, subtask A is to label whether these tweets are support, deny, query or comment, and subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods incorporating rich features. Since the training data is imbalanced, we specifically designed a two-step classifier to address subtask A.

Original languageEnglish
Title of host publicationACL 2017 - 11th International Workshop on Semantic Evaluations, SemEval 2017, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages491-496
Number of pages6
ISBN (Electronic)9781945626555
DOIs
StatePublished - 2017
Event11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017 - Vancouver, Canada
Duration: 3 Aug 20174 Aug 2017

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference11th International Workshop on Semantic Evaluations, SemEval 2017, co-located with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017
Country/TerritoryCanada
CityVancouver
Period3/08/174/08/17

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