Abstract
This paper describes characteristics of information flow on social channels, as a function of content type and relations among individual sources, distilled from analysis of Twitter data as well as human subject survey results. The working hypothesis is that individuals who propagate content on social media act (e.g., decide whether to relay information or not) in accordance with their understanding of the content, as well as their own beliefs and trust relations. Hence, the resulting aggregate content propagation pattern encodes the collective content interpretation of the underlying group, as well as their relations. Analysis algorithms are described to recover such relations from the observed propagation patterns as well as improve our understanding of the content itself in a language agnostic manner simply from its propagation characteristics. An example is to measure the degree of community polarization around contentious topics, identify the factions involved, and recognize their individual views on issues. The analysis is independent of the language of discourse itself, making it valuable for multilingual media, where the number of languages used may render language-specific analysis less scalable.
| Original language | English (US) |
|---|---|
| Title of host publication | Next-Generation Analyst V |
| Editors | Timothy P. Hanratty, James Llinas |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510609150 |
| DOIs | |
| State | Published - Jan 1 2017 |
| Event | 5th Conference on Next-Generation Analyst - Anaheim, United States Duration: Apr 10 2017 → Apr 11 2017 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 10207 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Other
| Other | 5th Conference on Next-Generation Analyst |
|---|---|
| Country | United States |
| City | Anaheim |
| Period | 4/10/17 → 4/11/17 |
Fingerprint
Keywords
- Social networks
- signal processing
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering
Cite this
Information flow on social networks : From empirical data to situation understanding. / Roy, Heather; Abdelzaher, Tarek; Bowman, Elizabeth K.; Al Amin, Md Tanvir.
Next-Generation Analyst V. ed. / Timothy P. Hanratty; James Llinas. SPIE, 2017. 1020702 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10207).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Information flow on social networks
T2 - From empirical data to situation understanding
AU - Roy, Heather
AU - Abdelzaher, Tarek
AU - Bowman, Elizabeth K.
AU - Al Amin, Md Tanvir
PY - 2017/1/1
Y1 - 2017/1/1
N2 - This paper describes characteristics of information flow on social channels, as a function of content type and relations among individual sources, distilled from analysis of Twitter data as well as human subject survey results. The working hypothesis is that individuals who propagate content on social media act (e.g., decide whether to relay information or not) in accordance with their understanding of the content, as well as their own beliefs and trust relations. Hence, the resulting aggregate content propagation pattern encodes the collective content interpretation of the underlying group, as well as their relations. Analysis algorithms are described to recover such relations from the observed propagation patterns as well as improve our understanding of the content itself in a language agnostic manner simply from its propagation characteristics. An example is to measure the degree of community polarization around contentious topics, identify the factions involved, and recognize their individual views on issues. The analysis is independent of the language of discourse itself, making it valuable for multilingual media, where the number of languages used may render language-specific analysis less scalable.
AB - This paper describes characteristics of information flow on social channels, as a function of content type and relations among individual sources, distilled from analysis of Twitter data as well as human subject survey results. The working hypothesis is that individuals who propagate content on social media act (e.g., decide whether to relay information or not) in accordance with their understanding of the content, as well as their own beliefs and trust relations. Hence, the resulting aggregate content propagation pattern encodes the collective content interpretation of the underlying group, as well as their relations. Analysis algorithms are described to recover such relations from the observed propagation patterns as well as improve our understanding of the content itself in a language agnostic manner simply from its propagation characteristics. An example is to measure the degree of community polarization around contentious topics, identify the factions involved, and recognize their individual views on issues. The analysis is independent of the language of discourse itself, making it valuable for multilingual media, where the number of languages used may render language-specific analysis less scalable.
KW - Social networks
KW - signal processing
UR - http://www.scopus.com/inward/record.url?scp=85025686234&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025686234&partnerID=8YFLogxK
U2 - 10.1117/12.2266585
DO - 10.1117/12.2266585
M3 - Conference contribution
AN - SCOPUS:85025686234
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Next-Generation Analyst V
A2 - Hanratty, Timothy P.
A2 - Llinas, James
PB - SPIE
ER -