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

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.

A survey of Big Data dimensions vs Social Networks analysis

“…Furthermore, textual streams content has been investigated by Li et al ( 2016 ) to deal with dynamic social networks. Zheng et al ( 2020 ) developed a Topic-Specific Influence in Social Networks analyzing streaming data by using LSTM and self attention.…”

Technology-Delivered Intervention Strategies to Bolster HIV Testing

“… 18+-year-old MSM HIV testing for intervention group was 17.0% compared to 7.0% for control group (AOR 2.61, 95% CI 1.55–4.38). Study websites Bauermeister et al, 2019 [ 29 ] RCT to determine the acceptability and efficacy of an online intervention to change sexual risk behaviors and HIV testing. Two groups: (1) intervention group received distinct and affective content designed to build HIV risk reduction skills and positive sexual health behaviors and (2) control group provided with general HIV prevention content.…”

“…Social media includes any digital tool used to create content and share ideas, opinions, and information with a virtual network of people and communities [21]. Research in the past 5 years suggests social media can be an effective tool for increasing HIV testing among high-risk populations, including MSM [18,22,23], as well as using "social big data" to monitor HIV-related outcomes [24][25][26][27][28][29][30].…”

Public perceptions and implementation considerations on the use of artificial intelligence in health

“…Artificial intelligence (AI) has been used in public health research and healthcare with vast applications in both. AI techniques such as machine learning and natural language processing allow for the analysis of large data from private spaces such as healthcare organizations 1 and public platforms like social media 2‐6 . For example, in mental health, at the individual level, prediction analytics were used to identify people in crisis and help get them connected with resources 7 .…”