CNA-TCC: Campaign Network Attribute Based Thematic Campaign Classification

With the emergence of social media and computing, many users have utilized social media platforms (SMPs) in communicating and sharing their interests and preferences. One critical challenge is that social media have been employed for propaganda and influence campaigns for various purposes, such as spreading fake news. SMPs generate vast amounts of data that demand machine learning (ML) capabilities to efficiently learn and infer influence campaigns. This study proposes an ML framework for the thematic campaign classification (TCC), assisting decision-makers in understanding the impacts of social media toward end-users and aiding the mitigation of their side effects. The proposed framework relies on a newly developed characterization that we have termed the campaign network attribute (CNA), which adopts representative network features for the effective TCC by neural networks. The proposed CNA-TCC framework was validated using Twitter and Instagram data sources. The proposed framework can achieve a classification precision in the range of 68%-90% for two campaigns. Also, it can identify a known campaign in generic social media data with a 60% precision. The empirical results indicated high-performance levels of the proposed CNA-TCC framework that can substantially reduce the search spaces for social influence campaigns on specific themes. The proposed CNA-TCC framework has the potential to be applied in real-world SMPs and to process their large-scale data, so as to effectively classify influence campaigns.