ABSTRACT
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 can promote online social campaigns is important but complex. Unlike influential individuals, focal structures can effect 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 individuals in the focal structures through their communication network. The CFSA model utilizes multiplex networks, where the first layer is the users-users 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 real-world datasets from Twitter related to domestic extremist groups spreading information about COVID-19 and the Black Lives Matter (BLM) social movement during the 2020–2021 time. The model identified Contextual Focal Structure (CFS) sets revealing the context regarding individuals’ interests. We then evaluated the model's efficacy by measuring the influence of the CFS sets in the network using various network structural measures such as the modularity method, network stability, and average clustering coefficient values. The ranking Correlation Coefficient (RCC) was used to conduct a comparative evaluation with real-world scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Complexity and dynamicity of the social networks are categorized as NP-hard problems to solve and analyze. These variables on social networks such as actions and interrelationships between the network's users, different behaviors, users' feedbacks and the networks' dynamics make them intractable. Systems thinking and modeling methods orient the relationships between all parts in online social networks. Complexity theories, system dynamics, and game theoretic approaches implemented by system thinkers help investigate the system's local parties' relationships. These methods also present a useful tool to interpret the social networks' complex interactions, dynamic activities in online networks and communities between users and their online dynamic interactions. In this paper, systems thinking concepts and organizational cybernetics are employed to analyze the armed protest demonstration against COVID-19 lockdown at Michigan capitol on May 12th through May 15th on Twitter. Utilizing these methods, we also present a systematic analysis to control, analyze, and comprehend the actions, tweets, and retweets exchanged between users' supporting and opposing the campaign in a complex and dynamic environment. © 2020 IEEE.