Influence diffusion detection using the influence style ( Infuse ) model
Bibliographic Data
| ID | 19127979 |
|---|---|
| Authors | Luke Kien‐Weng Tan (Wee Kim Wee School of Communication and Information Nanyang Technological University 31 Nanyang Link Singapore 637718 Singapore, corresponding author), Jin‐Cheon (0000-0002-2211-9382), Ying Ding (0000-0003-2567-2009, School of Informatics and Computing Indiana University 1320 E. 10th Street, LI 025 Bloomington IN 47405) |
| Year | 2015 |
| Volume | 66 |
| Issue | 8 |
| Pages | 1717-1733 |
| Publication date | 2015-04-07 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of the Association for Information Science and Technology (JOURNAL) |
| Journal identifiers | ISSN: 2330-1635 • E-ISSN: 2330-1643 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/asi.23287 |
| OpenAlex | W1803433474 |
| Language | EN |
| References cited | 34 |
Blogs are readily available sources of opinions and sentiments that in turn could influence the opinions of the blog readers. Previous studies have attempted to infer influence from blog features, but they have ignored the possible influence styles that describe the different ways in which influence is exerted. We propose a novel approach to analyzing bloggers' influence styles and using the influence styles as features to improve the performance of influence diffusion detection among linked bloggers. The proposed influence style ( INFUSE ) model describes bloggers' influence through their engagement style, persuasion style, and persona. Methods used include similarity analysis to detect the creating−sharing aspect of engagement style, subjectivity analysis to measure persuasion style, and sentiment analysis to identify persona style. We further extend the INFUSE model to detect influence diffusion among linked bloggers based on the bloggers' influence styles. The INFUSE model performed well with an average F 1 score of 76% compared with the in‐degree and sentiment‐value baseline approaches. Previous studies have focused on the existence of influence among linked bloggers in detecting influence diffusion, but our INFUSE model is shown to provide a fine‐grained description of the manner in which influence is diffused based on the bloggers' influence styles
Art · Human–computer interaction · Persona · Persuasion · Complex Network Analysis Techniques · Computer Science · Opinion Dynamics and Social Influence · Psychology · Sentiment Analysis and Opinion Mining · Social Psychology · Artificial Intelligence
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Scalable influence maximization for prevalent viral marketing in large-scale social networks
Social Influence
Networks and organizations
A Coefficient of Agreement for Nominal Scales
Systematic and nonsystematic processing of majority and minority persuasive communications
Recognizing Contextual Polarity
Personality in 100,000 Words
Compliance, identification, and internalization three processes of attitude change
Behavioral Study of obedience
Threshold Models of Collective Behavior
The Strength of Weak Ties
| Highly cited | No |
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