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Argument Mining on Twitter: Arguments, Facts and Sources

Mihai Dusmanu 1 Elena Cabrio 2, 3 Serena Villata 3 
3 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Social media collect and spread on the Web personal opinions, facts, fake news and all kind of information users may be interested in. Applying argument mining methods to such heterogeneous data sources is a challenging open research issue, in particular considering the peculiarities of the language used to write textual messages on social media. In addition, new issues emerge when dealing with arguments posted on such platforms, such as the need to make a distinction between personal opinions and actual facts, and to detect the source disseminating information about such facts to allow for provenance verification. In this paper, we apply supervised classification to identify arguments on Twitter, and we present two new tasks for argument mining, namely facts recognition and source identification. We study the feasibility of the approaches proposed to address these tasks on a set of tweets related to the Grexit and Brexit news topics.
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Contributor : Serena Villata Connect in order to contact the contributor
Submitted on : Tuesday, November 21, 2017 - 3:32:32 PM
Last modification on : Thursday, August 4, 2022 - 4:55:01 PM


  • HAL Id : hal-01643653, version 1



Mihai Dusmanu, Elena Cabrio, Serena Villata. Argument Mining on Twitter: Arguments, Facts and Sources. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Sep 2017, Copenhagen, Denmark. ⟨hal-01643653⟩



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