Contextualizing focal structure analysis in social networks

Focal structures are key sets of individuals who may be responsible for coordinating events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structures that are able to promote online social campaigns is important but complex. Unlike influential individuals, focal structures can affect large-scale complex social processes. In our prior work, we applied a greedy algorithm and bi-level decomposition optimization solution to identify focal structures in social media networks. However, the outcomes lacked a contextual representation of the focal structures that affected interpretability. In this research, we present a novel contextual focal structure analysis (CFSA) model to enhance the discovery and the interpretability of the focal structures to provide the context in terms of the content shared by the focal structures through their communication network. The model utilizes multiplex networks, where one layer is the user network based on mentions, replies, friends, and followers, and the second layer is the hashtag co-occurrence network. The two layers have interconnections based on the user hashtag relations. The model's performance was evaluated on various real-world datasets from Twitter related to COVID-19, the Trump vaccine hashtag, and the Black Lives Matter (BLM) social movement during the 2020-2021 time. The model discovered contextual focal structures (CFS) sets revealed the context regarding individuals' interests. We then evaluated the model's efficacy using various network structural measures such as the modularity method, network stability, and average clustering coefficient to measure the influence of the CFS sets in the network. Ranking correlation coefficient (RCC) was used to conduct the comparative evaluation with real-world scenarios to find the correlated solutions.