Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs

• Type: Article in International Scientific Journal
• Date: 2023-06

Authors

Tabassum, S (Author) Other; João Gama (Author) FEP; Azevedo, PJ (Author) Other; Cordeiro, M (Author) Other; Martins, C (Author) Other; Martins, A (Author) Other

Journal

Title: Expert Systems

Vol. 40 No. 5

ISSN: 0266-4720

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 6 Citations

Other information

Authenticus ID: P-00X-HZN

DOI: 10.1111/exsy.13195

Abstract (EN): Influence Analysis is one of the well-known areas of Social Network Analysis. However, discovering influencers from micro-blog networks based on topics has gained recent popularity due to its specificity. Besides, these data networks are massive, continuous and evolving. Therefore, to address the above challenges we propose a dynamic framework for topic modelling and identifying influencers in the same process. It incorporates dynamic sampling, community detection and network statistics over graph data stream from a social media activity management application. Further, we compare the graph measures against each other empirically and observe that there is no evidence of correlation between the sets of users having large number of friends and the users whose posts achieve high acceptance (i.e., highly liked, commented and shared posts). Therefore, we propose a novel approach that incorporates a user's reachability and also acceptability by other users. Consequently, we improve on graph metrics by including a dynamic acceptance score (integrating content quality with network structure) for ranking influencers in micro-blogs. Additionally, we analysed the topic clusters' structure and quality with empirical experiments and visualization.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21