A Systematic Approach for Contextualizing Focal Structure Analysis in Social Networks
15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022
; 13558 LNCS:46-56, 2022.
Article
in English
| Scopus | ID: covidwho-2059739
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.
Complex network; Contextual Focal Structures; COVID-19; Entropy; Focal structures; Information gain; Multiplex networks; Complex networks; Social networking (online); Analysis models; Citizen engagements; Contextual focal structure; Focal structure; Hashtags; Interpretability; Social media networks; Structure analysis
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Systematic review/Meta Analysis
Language:
English
Journal:
15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022
Year:
2022
Document Type:
Article
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