Online Spreading of Topic Tags and Social Behavior
Bibliographic Data
| ID | 22107582 |
|---|---|
| Authors | Fuzhong Nian (0000-0002-2179-0895, Lanzhou University of Technology), Jinhu Ren (0000-0002-0196-929X, Lanzhou University of Technology), Xuelong Yu (0000-0002-8250-9710, Northeastern University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 1 |
| Pages | 1277-1288 |
| Publication date | 2024-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3235011 |
| OpenAlex | W4319996412 |
| Language | EN |
| Citations received | 5 |
| References cited | 42 |
This article explores information spreading in modern online social networks. According to the law of information spread in real social networks, information retweeting is divided into two types: topic retweeting and relationship retweeting. Social behavior is considered as a higher order interaction, and the nodal influence effect in the traditional approach is abstracted as part of it for analysis. The process of topic communities being subjected to social behavior is simulated by the social behavior model, and the dynamic retweeting rate is established. A network evolution model is constructed based on the centrality and noncontinuity characteristics of topic communities in the spread process. The social reinforcement effect in information spreading is described in two dimensions by defining topic expansion rate and topic diffusion rate. This work conducts multiple views of analysis and visualization, which provide more results of quantitative aspect. The validity of the model is verified by comparing the model simulation results with real cases and the generalization ability experiments
Centrality · Data science · Generalization · Social media · Social network analysis · Statistics · Visualization · World Wide Web · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Social Media and Politics · Artificial Intelligence
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| Unique citing works | 5 |
|---|---|
| Citations per year | 2.5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |