By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- A new study on Pattern Analysis is now available. According to news reporting from Lleida, Spain, by VerticalNews journalists, research stated, “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.”
Financial support for this research came from MICINN.
The news correspondents obtained a quote from the research from the University of Lleida, “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 a, we reject criticism and support relationships with probability below a. 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 a. 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.”
According to the news reporters, the research concluded: “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.”
For more information on this research see: An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships. Pattern Recognition Letters , 2018;105():191-199. Pattern Recognition Letters can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Pattern Recognition Letters - http://www.journals.elsevier.com/pattern-recognition-letters/)
Our news journalists report that additional information may be obtained by contacting R. Bejar, Univ Lleida, INSPIRES Res Center, Artificial Intelligence Grp, Lleida 25001, Spain. Additional authors for this research include J. Argelich, T. Alsinet, C. Fernandez, C. Mateu and J. Planes.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.patrec.2017.07.004. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-23), Studies from University of Lleida in the Area of Pattern Analysis Described (An argumentative approach for discovering relevant opinions in Twitter with probabilistic valued relationships), Journal of Robotics & Machine Learning, 386, ISSN: 1944-186X, BUTTER® ID: 015548214
From the newsletter Journal of Robotics & Machine Learning.
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