2017/03/04 by Xingjun Ma, Ma, Xingjun, Chunping Li +5
Computer Science · #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #cs.SI
paper · pdf · doi:10.48550/arxiv.1703.01468
The 14th Australasian Data Mining Conference (AusDM), 2016. Code and dataset are available at: https://github.com/xingjunm/Finding-Influentials-in-Twitter
arxiv created 2022/03/23 · arxiv updated 2022/03/24
With the growing popularity of online social media, identifying influential users in these social networks has become very popular. Existing works have studied user attributes, network structure and user interactions when measuring user influence. In contrast to these works, we focus on user behavioural characteristics. We investigate the temporal dynamics of user activity patterns and how these patterns affect user interactions. We assimilate such characteristics into a PageRank based temporal influence ranking model (TIR) to identify influential users. The transition probability in TIR is predicted by a logistic regression model and the random walk, biased according to users' temporal activity patterns. Experiments demonstrate that TIR has better performance and is more stable than the existing models in global influence ranking and friend recommendation.