Measuring Time-Sensitive and Topic-Specific Influence in Social Networks With LSTM and Self-Attention

Cheng Zheng,Chengqi Zhang,Wei Wang,Sean D. Young,Qin Zhang,Guodong Long

doi:10.1109/access.2020.2991683

Abstract

Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on streaming texts and dynamic social networks. A user's influence can change rapidly in response to a new event and vary on different topics. For example, the political influence of Douglas Jones increased dramatically after winning the Alabama special election, and then rapidly decreased after the election week. During the same period, however, Douglas Jones' influence on sports remained low. Most existing approaches can only model the influence based on static social network structures and topic distributions. Furthermore, as popular social networking services embody many features to connect their users, multi-typed interactions make it hard to learn the roles that different interactions play when propagating information. To address these challenges, we propose a Time-sensitive and Topic-specific Influence Measurement (TTIM) method, to jointly model the streaming texts and dynamic social networks. We simulate the influence propagation process with a self-attention mechanism to learn the contributions of different interactions and track the influence dynamics with a matrix-adaptive long short-term memory. To the best of our knowledge, this is the first attempt to measure time-sensitive and topic-specific influence. Furthermore, the TTIM model can be easily adapted to supporting online learning which consumes constant training time on newly arrived data for each timestamp. We comprehensively evaluate the proposed TTIM model on five datasets from Twitter and Reddit. The experimental results demonstrate promising performance compared to the state-of-the-art social influence analysis models and the potential of TTIM in visualizing influence dynamics and topic distribution.

Highlights

Social network influence refers to the ability of a user to change the feelings, attitudes, or behaviors of other users within a network [1]–[3]
To address the challenges above, we propose an unsupervised model, called Time-sensitive and Topic-specific Influence Measurement (TTIM) model
The results show that TTIM is effective and outperforms other baselines in precision, F1, and AUC on these datasets

Summary

Introduction

Social network influence refers to the ability of a user to change the feelings, attitudes, or behaviors of other users within a network [1]–[3]. Influence measurement has become an essential task in many fields such as online marketing [4], [5] and political campaigns [6]. Due to its practical importance, measuring social influence has drawn growing research interests [7]–[10]. We study the problem of influence measurement in temporal social networks: Given streaming posts and interactional activities, the goal is. One example is Douglas Jones, the current United States Senator for Alabama. Douglas Jones’ political influence increased dramatically during the election period and vanished rapidly after the election. This is reflected by the influence scores

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Conclusion