Abstract
Contagion in Online Social Networks (OSN) is typically measured by the tendency of users to re-post information or to adopt a new behavior after exposure to that information/behavior. Most contagion research is bound by modeling: (i) only local neighbor-to-neighbor contagion (ii) the spread of viral information. However, most contagion events are non-viral and can also occur globally by non-neighbors through for example, exposure to information by exploratory browsing, or by content recommendation algorithms. This study is the first to address the phenomenon of both global and local contagion of non-viral information in a quantitative way. Analysis of Twitter networks reveals the prevailing nature of global contagion, the different temporal patterns between global and local contagion, and the ways it varies across topical categories. an interesting finding shows that users who retweeted due to global contagion have more Followers than those who retweeted due to local contagion.
Original language | English |
---|---|
Title of host publication | Proceedings of the 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020 |
Editors | Tung X. Bui |
Publisher | IEEE Computer Society |
Pages | 2803-2812 |
Number of pages | 10 |
ISBN (Electronic) | 9780998133133 |
State | Published - 2020 |
Externally published | Yes |
Event | 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020 - Maui, United States Duration: 7 Jan 2020 → 10 Jan 2020 |
Publication series
Name | Proceedings of the Annual Hawaii International Conference on System Sciences |
---|---|
Volume | 2020-January |
ISSN (Print) | 1530-1605 |
Conference
Conference | 53rd Annual Hawaii International Conference on System Sciences, HICSS 2020 |
---|---|
Country/Territory | United States |
City | Maui |
Period | 7/01/20 → 10/01/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE Computer Society. All rights reserved.