Efficient graph-based event detection scheme on social media

Abstract

As various opinions and thoughts of users are shared on social media, various events can be detected through social media data analysis. The graph-based event detection scheme may eliminate event duplication detection by clustering words related to an event. In this paper, we propose a graph-based event detection scheme for detecting various events in the real world through social media analysis. The proposed scheme expresses the simultaneous occurrence of words mentioned with an event in graph form. Therefore, it can convey the information about the detected event and avoid duplicate detection. A keyword graph is constructed and keywords related to an event are clustered by analyzing the collected social data. By considering user interest calculated through changes in social activities of users on social media, the proposed scheme can detect event results that receive more responses from users and improve the reliability of the results by excluding indiscriminate advertisements or malicious posts from the results. We assign a weight to the generated keyword graph by considering user interest and calculate an event detection coefficient to determine the event values of the candidate event graphs. We perform various evaluations to demonstrate the superiority of the proposed scheme.