Research Area:  Machine Learning

Abstract:

Twitter is one of the most powerful social media platforms, reflecting both support and contrary opinions among people who use it. In a recent work, we developed an argumentative approach for analyzing the major opinions accepted and rejected in Twitter discussions. A Twitter discussion is modeled as a weighted argumentation graph where each node denotes a tweet, each edge denotes a relationship between a pair of tweets of the discussion and each node is attached to a weight that denotes the social relevance of the corresponding tweet in the discussion. In the social network Twitter, a tweet always refers to previous tweets in the discussion, and therefore the underlying argument graph obtained is acyclic. However, when in a discussion we group the tweets by author, the graph that we obtain can contain cycles. Based on the structure of graphs, in this work we introduce a distributed algorithm to compute the set of globally accepted opinions of a Twitter discussion based on valued argumentation. To understand the usefulness of our distributed algorithm, we study cases of argumentation graphs that can be solved efficiently with it. Finally, we present an experimental investigation that shows that when solving acyclic argumentation graphs associated with Twitter discussions our algorithm scales at most with linear time with respect to the size of the discussion. For argumentation graphs with cycles, we study tractable cases and we analyze how frequent are these cases in Twitter. Moreover, for the non-tractable cases we analyze how close is the solution of the distributed algorithm with respect to the one computed with the general sequential algorithm, that we have previously developed, that solves any argumentation graph.

Keywords:  
Distributed Argumentation Algorithm
Opinion Mining
Weighted
Twitter
Machine Learning
Deep Learning

Author(s) Name:  Teresa Alsinet, Josep Argelich, Ramón Béjar & Joel Cemeli

Publisher name:  Springer

DOI:  10.1007/s00500-018-3380-x

Volume Information:  volume 23, pages: 2147–2166

Paper Link:   https://link.springer.com/article/10.1007/s00500-018-3380-x