Tweeties Squabbling: Positive and Negative Results in Applying Argument Mining on Social Media.

Tom Bosc 1 Elena Cabrio 1 Serena Villata 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe an ongoing work towards the creation of a complete argument mining pipeline over Twitter messages: (i) we identify which tweets can be considered as arguments and which cannot, (ii) over the set of tweet-arguments, we group them by topic, and (iii) we predict whether such tweets support or attack each other. The final goal is to compute the set of tweets which are widely recognized as accepted, and the different (possibly conflicting) viewpoints that emerge on a topic, given a stream of messages.
Type de document :
Communication dans un congrès
Proceedings of the 6th International Conference on Computational Models of Argument , Sep 2016, Potsdam, Germany. 2016
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https://hal.inria.fr/hal-01332617
Contributeur : Elena Cabrio <>
Soumis le : jeudi 16 juin 2016 - 11:39:58
Dernière modification le : vendredi 17 juin 2016 - 01:05:16

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  • HAL Id : hal-01332617, version 1

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Tom Bosc, Elena Cabrio, Serena Villata. Tweeties Squabbling: Positive and Negative Results in Applying Argument Mining on Social Media.. Proceedings of the 6th International Conference on Computational Models of Argument , Sep 2016, Potsdam, Germany. 2016. <hal-01332617>

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