Modeling Interactions in Web Forums

The ability to accurately identify “reply-to” relations in online discussions has important implications for various social media analytics applications. However, accurately identifying such interactions remains a challenge, with existing methods providing inadequate performance. In this study, we propose a novel method for modeling social media interactions. The proposed method leverages several empirical insights about online interaction patterns, coupled with a robust machine learning algorithm, for enhanced classification of social media interactions. Furthermore, the proposed method also facilitates the creation of more accurate social media networks. As topological information derived from online communication continues to play an integral role in various social media analytics application areas, the results of our work have important implications.