Human-Driven Dynamic Community Influence Maximization in Social Media Data Streams

• Main Authors: Jun Ge, Lei-Lei Shi, Yan Wu, Jie Liu
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Dynamic community influence maximization; microblogging; IC; event evolution
• Online Access: https://ieeexplore.ieee.org/document/9187341/

Microblogging-a popular social media service platform-has become a new information channel for users to receive and exchange the most up-to-date information on current events. Consequently, it is a crucial platform for maximizing community influence which has broad application prospects in recommendation system, advertising and other fields. With the rapid development of the mobile Internet, online social networks are gradually infiltrating into our daily lives, in which the communities are an important part of social networks. The combination of social networking and edge computing technology has important application value and is the development trend of influence maximization in future networks. However, traditional influence maximization models look for the most influential seed nodes while ignoring the fact that the selected seed nodes are various for different event topics, which significantly reduces the efficiency and accuracy of event propagation. In addition, most existing methods focus only on event propagation and neglect multiple topics in event propagation. At the same time, the interests of users in the network are not always single and the user's interest and the topic of the event will change over time, thus making it challenging to track momentous events in a timely manner. To address these issues, this paper proposes a Multi-Topic Learning-based Independent Cascade model (MTL-IC), and a Similarity Priority Mechanism-based Event Evolution model (SPM-EE). MTL-IC incorporates multi-topic factors and considers the authority and hub in interests of user, which makes the results more efficient and more accurate. SPM-EE can update the seed users according to their changeable interest in time, which largely improve the precision of event evolution. The experimental results, using Twitter datasets, demonstrate the effectiveness of our proposed methods for both dynamic community influence maximization and event evolution.