dc.contributor.author Alsinet, Teresa dc.contributor.author Argelich Romà, Josep dc.contributor.author Béjar Torres, Ramón dc.contributor.author Fernàndez Camon, César dc.contributor.author Mateu Piñol, Carles dc.contributor.author Planes Cid, Jordi dc.date.accessioned 2018-04-13T10:59:15Z dc.date.issued 2017-07-08 dc.identifier.issn 0167-8655 dc.identifier.uri http://hdl.handle.net/10459.1/63097 dc.description.abstract Twitter is one of the most widely used social networks when it comes to sharing and criticizing relevant news and events. In order to understand the major opinions accepted and rejected in different domains by Twitter users, in a recent work we developed an analysis system based on valued abstract argumentation to model and reason about the social acceptance of tweets, considering different information sources from the social network. Given a Twitter discussion, the system outputs the set of accepted tweets from the discussion, considering two kinds of relationship between tweets: criticism and support. In this paper, we introduce and investigate a natural extension of the system, in which relationships between tweets are associated with a probability value, indicating the uncertainty that the relationships hold. An important element in our system is the notion of an uncertainty threshold, which characterizes how much uncertainty on probability values we are willing to tolerate: given an uncertainty threshold $\alpha$, we reject criticism and support relationships with probability below $\alpha$. We also extend our analysis system by incorporating support propagation when computing the social relevance of tweets. To this end, we extend the abstract argumentation framework with a new valuation function that propagates the support between tweets by taking into account not only the social relevance of tweets but also the probability that the support relationship holds, provided that it is above the specified uncertainty threshold $\alpha$. In order to test these new extensions, we analyze different Twitter discussions from the political domain. Our analysis shows that the social support of the accepted tweets is typically much stronger than the one for the rejected tweets. Also, the set of accepted tweets seems to be very stable with respect to changes to the social support of the tweets, and therefore even when considering support propagation we mainly observe differences in such set when using the more permissive probability thresholds. dc.description.sponsorship This work was partially funded by the Spanish MICINN Projects TIN2014-53234-C2-2- R, TIN2015-71799-C2-2-P and ENE2015-64117-C5-1- R. This research article has received a grant for its linguistic revision from the Language Institute of the University of Lleida (2017 call). The authors would like to thank anonymous reviewers for providing helpful comments to improve the paper. dc.format.mimetype application/pdf dc.language.iso eng dc.publisher Elsevier dc.relation MINECO/PN2013-2016/TIN2014-53234-C2-2-R dc.relation MINECO/PN2013-2016/TIN2015-71799-C2-2-P dc.relation MINECO/PN2013-2016/ENE2015-64117-C5-1-R dc.relation.isformatof Versió postprint del document publicat a https://doi.org/10.1016/j.patrec.2017.07.004 dc.relation.ispartof Pattern Recognition Letters, 2018, vol. 105, p. 191-199 dc.rights cc-by-nc-nd (c) Elsevier, 2018 dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es dc.subject Abstract argumentation dc.subject Social networks dc.subject Probabilistic relationships dc.title An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships dc.type info:eu-repo/semantics/article dc.date.updated 2018-04-13T10:59:15Z dc.identifier.idgrec 027625 dc.type.version info:eu-repo/semantics/acceptedVersion dc.rights.accessRights info:eu-repo/semantics/embargoedAccess dc.identifier.doi https://doi.org/10.1016/j.patrec.2017.07.004 dc.date.embargoEndDate 2020-04-01
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