Constructive Approach for Early Extraction of Viral Spreading Social Issues from Twitter

Abstract

In recent years, there has been a rapid increase in viral spreading social issues that emerge in the public consciousness through the fast spread of information online. With the advent of social media, they sometimes yield unexpected side effects such as product boycotts. Therefore, it is important to recognize them as the earliest and take preventive measures against them. Existing researches on social issue extraction have mainly focused on news channels and newspapers as the primary information sources. However, such approaches cannot be applied for the early extraction of viral spreading social issues because their epicenter is the online public's opinion. In this study, we propose a constructive method inspired by a social issues research approach, called constructivism, for the early extraction of viral spreading social issues. It is characteristic that our method identifies the keywords related to social issues using information obtained from the claims-making activities on Twitter and Twitter-user clustering. We conducted experiments on a large Twitter dataset comprising tens of billions of tweets and the proposed method successfully extracted six out of the seven viral spreading social issues earlier than their first TV news coverage. Furthermore, the proposed method could identify such cases approximately two weeks earlier, on average, than the first national TV news coverage.