Analyzing the repercussions of the actions based on the emotional state in social networks

Abstract

The present work is a study of the detection of negative affective or emotional states that people have using social network sites (SNSs), and the effect that this negative state has on the repercussions of posted messages. We aim to discover in which grade an user having an affective state considered negative by an analyzer (Sentiment Analyzer and Stress Analyzer), can affect other users and generate bad repercussions, and to know whether its more suitable to predict a bad future situation using the different analyzers. We propose a method for creating a combined model of sentiment and stress and use it in our experimentation in order to discern if it is more suitable to predict future bad situations, and in what context. Additionally, we created a Multi-Agent System (MAS) that integrate the analyzers to protect or advice users, which uses the trained and tested system to predict and avoid future bad situations in social media, that could be triggered by the actions of an user that has an emotional state considered negative. We conduct this study as a way to help building future systems that prevent bad situations where an user that has a negative state creates a repercussion in the system. This can help avoid users to achieve a bad mood, or help avoid privacy issues, in the way that an user that has a negative state post information that he don’t really want to post.