Analysis of Discussions in Twitter with an Argumentation Tool

The analysis of opinions on general and specialized social networks, has recently received a lot of attention on many application fields. For example, there is a vivid interest in the analysis of opinions of tourists about destinations and facilities, aimed at getting insight on tourist behavior and preferences for improvement and investment policy planning (McCarthy and Stock 2010; Villatoro et al. 2013; Williams et al. 2015), and similar efforts are being done on marketing (Burton and Soboleva 2011), customer engagement (Zhang, Jansen, and Chowdhury 2011), and related fields (Jansen et al. 2009; Chu and Kim 2011). Less numerous are the contributions centered around analyzing, not individual opinions, but debates and conversations where the structural relations between opinions are a key component to be able to pinpoint the accepted, or winning, opinions in a discussion. In this line, key contributions are the works of Atkinson et al. about using argumentation for tools to support e-participation in deliberation processes (Atkinson, Bench-Capon, and McBurney 2006; Cartwright and Atkinson 2008; 2009; Wardeh et al. 2013). Although there exist many specialized and generalist social networks, nowadays Twitter is one of the most widely used ones when it comes to share and criticize relevant news, and the citizens response to news and events in Twitter is frequently taken as an indicator of the social interest for that topic. In order to understand what are the major accepted and rejected opinions in different domains by Twitter users, in a recent work (Alsinet et al. Submitted to Elsevier Science) we have developed a system for analysis of discussions in Twitter. The system architecture has two main components: a discussion retrieval system and a reasoning system. The discussion retrieval system allows us to move from a discussion in Twitter (a set of tweets) in natural language to a specialized structure called Weighted Labeled Discussions Graph (WLDisG) which is computed taking into account two semantic relationships between tweets: criticizes and supports, and three different attributes of a tweet: the number of followers of the author, the number of retweets and the number of favorites. The reasoning system maps the WLDisG graph into a valued argumentation framework (VAF) and the set of socially accepted tweets in the discussion is computed from the weight assigned to each tweet and the criticism relationship, as the ideal extension of the VAF associated with the WLDisG graph of the Twitter discussion. Our system is close to the argumentation framework developed by Cabrio and Villata (Cabrio and Villata 2013). The authors use bipolar argumentation algorithms to evaluate the set of accepted arguments, given the support and the attack relations among them. The arguments and the relations among them are detected by an automated framework by applying natural language techniques, since the system is focused on online debates, such as Debatepedia, where user posts tend to have a rich structure that allow natural language methods to infer semantic relationships. One key difference between our system and the one proposed by Cabrio and Villata is that we incorporate weighted arguments, by means of different weighting schemes, and define attacks between them by means of preference relations over the weights. We believe that the incorporation of weights to get the relative relevance of arguments, considering information taken from the social network, is an important aspect if we want to finally build tools that are useful for analyzing discussions considering different sources of information for widely socially accepted arguments. Although our argumentation system can be utilized to analyze discussions in different social networks, in this work we have focused on the analysis of Twitter discussions. The discussions extracted from Twitter are characterized by: limited number of characters by tweet, use of emoticons and jargon, and social relevance attributes present in tweets. From these elements, we compute weighted arguments and relations between them by means of an automatic labeling system based on Support Vector Machines.