Leveraging Topic Features in Prediction of Social Network Community Evolutions

While the cornerstone of community evolution revolves on discussion topics, and both the structure and topics undergo dynamic changes simultaneously. Most studies in this field have focused on complex network structural features, overlooking the influence of topics and their features on network complexity and time consumption. Topic features, in particular, play a critical role in shaping the community’s evolution and are increasingly recognized for their influence in driving changes within these digital ecosystems. Our study aims to fill this research gap by proposing topical features to enhancing the accuracy of tracking and efficiency of predictive models. In this study, we developed Identification of Community Evolution by Mapping “ICEM” method by integrating topic model with network structure analysis to track community evolution. We employed three predictive classifiers to assess the significance of topical features and compared their performance against and with combination of structural features. Our findings not only demonstrate the effectiveness of topical features in predicting community evolution with high accuracy, but also highlight a significant reduction in execution time compared to existing approaches.