Knowledge Discovery From Large Amounts Of Social Media Data

Abstract

In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people’s behaviors and interactions. In particular, they can be exploited to analyze the collective sentiment of people, understand the behavior of user groups during global events, monitor public opinion close to important events, identify the main topics in a public discussion, or detect the most frequent routes followed by social media users. As an example of the countless works in the state-of-the-art on social media analysis, this paper presents three significant applications in the field of opinion and pattern mining from social media data: $(i)$ an automatic application for discovering user mobility patterns, $(ii)$ a novel application for estimating the political polarization of public opinion, and $(iii)$ an application for discovering interesting social media discussion topics through a hashtag recommendation system. Such applications clearly highlight the abundance and wealth of useful information in many application contexts of human life that can be extracted from social media posts.