Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets

Oana Cocarascu, Francesca Toni

Research output: Contribution to journalConference paperpeer-review

23 Citations (Scopus)
112 Downloads (Pure)

Abstract

The use of social media has become a regular habit for many and has changed the way people
interact with each other. In this article, we focus on analyzing whether news headlines support
tweets and whether reviews are deceptive by analyzing the interaction or the influence that these
texts have on the others, thus exploiting contextual information. Concretely, we define a deep
learning method for relation–based argument mining to extract argumentative relations of attack
and support. We then use this method for determining whether news articles support tweets,
a useful task in fact-checking settings, where determining agreement toward a statement is a
useful step toward determining its truthfulness. Furthermore, we use our method for extracting
bipolar argumentation frameworks from reviews to help detect whether they are deceptive. We
show experimentally that our method performs well in both settings. In particular, in the case of
deception detection, our method contributes a novel argumentative feature that, when used in
combination with other features in standard supervised classifiers, outperforms the latter even
on small data sets.
Original languageEnglish
Pages (from-to)833-858
JournalCOMPUTATIONAL LINGUISTICS
Volume44
Issue number4
Early online date26 Dec 2018
DOIs
Publication statusPublished - Dec 2018

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