Social media data analysis for revealing collective behaviors

Abstract

Along with the development of Web 2.0 applications, social media services has attracted many users and become their hands-on toolkits for recording life, sharing ideas, and social networking. Though social media services are essentially web or mobile applications and services, they combine user-generated content and social networks together, so that information can be created, transmitted, transformed, and consumed in the cyberspace. Thus, social media somehow could be regarded as a kind of sensor to the real life of its users. In general, the data from social media is of low quality. Pieces of information in social media are usually short, with informal presentation, and in some specific context that is highly related to the physical world. Therefore, it is challenging to extract semantics from social media data. However, we argue that given sufficient social media data, users' collective behaviors could be sensed, studied, and even predicted in a certain circumstance. Our study is conducted on data from two services, i.e. Twitter, and Sina Weibo, the most popular microblogging services all over the world and in China, respectively. Collective behaviors are actions of a large amount of various people, which are neither conforming nor deviant. Various collective behaviors are studied in the context of social media. Our studies show that there are various information flow patterns in social media, some of which are similar to traditional media such as newspapers, while others are embedded deep in the social network structure. The evolution of hotspots is highly affected by external stimulation, the social network structure, and individual user's activities. Furthermore, social media tends to be immune to some repeated similar external stimulations. Last but not the least, there is considerable difference in users' behavior between Twitter and Sina Weibo.