InfluenceRank: An Improved Online Social Influence Model
- 10.2991/assehr.k.200727.040How to use a DOI?
- Influence, social media, PageRank
User influence is a popular research content in online social networks, and it plays an important role in marketing, public opinion management, and network relationships. Traditional research on user influence based on graph structure mainly considers whether users follow each other. However, “zombie fans” can make the influence analysis results inaccurate. Based on the PageRank algorithm, this study proposes a novel model for measuring user influence: InfluenceRank. User behavior and interaction information are introduced into the model through three indicators: activity, interaction and credibility. The experimental results, which are more comprehensive and persuasive, prove that the influence ranking of the InfluenceRank model on Microblog (Chinese Twitter) users is not limited to the number of users’ fans.
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yun Bai AU - Suling Jia AU - Meng Wu PY - 2020 DA - 2020/07/28 TI - InfluenceRank: An Improved Online Social Influence Model BT - Proceedings of the 2020 International Conference on Advanced Education, Management and Information Technology (AEMIT 2020) PB - Atlantis Press SP - 182 EP - 186 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200727.040 DO - 10.2991/assehr.k.200727.040 ID - Bai2020 ER -