Social network analysis of Twitter interactions: a directed multilayer network approach

Abstract

Effective employment of social media for any social influence outcome requires a detailed understanding of the target audience. Social media provides a rich repository of self-reported information that provides insight regarding the sentiments and implied priorities of an online population. Using Social Network Analysis, this research models user interactions on Twitter as a weighted, directed network. Topic modeling through Latent Dirichlet Allocation identifies the topics of discussion in Tweets, which this study uses to induce a directed multilayer network wherein users (in one layer) are connected to the conversations and topics (in a second layer) in which they have participated, with inter-layer connections representing user participation in conversations. Analysis of the resulting network identifies both influential users and highly connected groups of individuals, informing an understanding of group dynamics and individual connectivity. The results demonstrate that the generation of a topically-focused social network to represent conversations yields more robust findings regarding influential users, particularly when analysts collect Tweets from a variety of discussions through more general search queries. Within the analysis, PageRank performed best among four measures used to rank individual influence within this problem context. In contrast, the results of applying both the Greedy Modular Algorithm and the Leiden Algorithm to identify communities were mixed; each method yielded valuable insights, but neither technique was uniformly superior. The demonstrated four-step process is readily replicable, and an interested user can automate the process with relatively low effort or expense.

RETRACTED: A Novel Weight-based Fish School Search Approach for Hierarchical Network Clustering

“…Clustering techniques in social network analysis are valuable for discovering groups of individuals with strong ties, revealing social substructures like cliques (Mathematik, 2023), subgroups, or communities. This understanding provides insights into social interactions and helps identify influential individuals or groups (Logan et al, 2023). In molecular biology (Schirra et al, 2023), clustering methods can identify protein communities, aiding the discovery of functional protein groups.…”

AraBERTopic: A Neural Topic Modeling Approach for News Extraction from Arabic Facebook Pages using Pre-trained BERT Transformer Model

“…Their results showed that the model enhances the performance of topic modeling and can effectively capture topic correlation. However, the authors in [11] used LDA to identify the topics of discussion in Tweets, resulting in a directed multilayer network in which users (in one layer) are linked to discussions and topics (in a second layer) in which they contributed, with interlayer connections indicating user participation in discussions. Although there are other methods for topic modeling on short texts, such as the Biterm Topic Model.…”

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

“…Moreover, Twitter distinguishes itself from other OSNs as its user base exhibits a distinct focus on real-time communication by sharing thoughts and ideas related to speci c topics rather than prioritizing personal connections (Jain et al, 2021;Logan et al, 2023;Masrom et al, 2021). For example, we selected 150 primary users who had the highest number of likes on their tweets related to a recent topic.…”