Abstract
Signal processing techniques such as filtering, detection, estimation and frequency domain analysis have long been applied to extract information from noisy sensor data. This paper describes the exploitation of these signal processing techniques to extract information from social networks, such as Twitter and Instagram. Specifically, we view social networks as noisy sensors that report events in the physical world. We then present a data processing stack for detection, localization, tracking, and veracity analysis of reported events using social network data. We show using a controlled experiment that the behavior of social sources as information relays varies dramatically depending on context. In benign contexts, there is general agreement on events, whereas in conflict scenarios, a significant amount of collective filtering is introduced by conflicted groups, creating a large data distortion. We describe signal processing techniques that mitigate such distortion, resulting in meaningful approximations of actual ground truth, given noisy reported observations. Finally, we briefly present an implementation of the aforementioned social network data processing stack in a sensor network analysis toolkit, called Apollo. Experiences with Apollo show that our techniques are successful at identifying and tracking credible events in the physical world.
| Original language | English (US) |
|---|---|
| Title of host publication | Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII |
| Editors | Michael A. Kolodny, Tien Pham |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510600720 |
| DOIs | |
| State | Published - Jan 1 2016 |
| Event | Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII - Baltimore, United States Duration: Apr 18 2016 → Apr 20 2016 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 9831 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Other
| Other | Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII |
|---|---|
| Country | United States |
| City | Baltimore |
| Period | 4/18/16 → 4/20/16 |
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
Applying traditional signal processing techniques to social media exploitation for situational understanding. / Abdelzaher, Tarek; Roy, Heather; Wang, Shiguang; Giridhar, Prasanna; Al Amin, Md Tanvir; Bowman, Elizabeth K.; Kolodny, Michael A.
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII. ed. / Michael A. Kolodny; Tien Pham. SPIE, 2016. 98310R (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9831).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Applying traditional signal processing techniques to social media exploitation for situational understanding
AU - Abdelzaher, Tarek
AU - Roy, Heather
AU - Wang, Shiguang
AU - Giridhar, Prasanna
AU - Al Amin, Md Tanvir
AU - Bowman, Elizabeth K.
AU - Kolodny, Michael A.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Signal processing techniques such as filtering, detection, estimation and frequency domain analysis have long been applied to extract information from noisy sensor data. This paper describes the exploitation of these signal processing techniques to extract information from social networks, such as Twitter and Instagram. Specifically, we view social networks as noisy sensors that report events in the physical world. We then present a data processing stack for detection, localization, tracking, and veracity analysis of reported events using social network data. We show using a controlled experiment that the behavior of social sources as information relays varies dramatically depending on context. In benign contexts, there is general agreement on events, whereas in conflict scenarios, a significant amount of collective filtering is introduced by conflicted groups, creating a large data distortion. We describe signal processing techniques that mitigate such distortion, resulting in meaningful approximations of actual ground truth, given noisy reported observations. Finally, we briefly present an implementation of the aforementioned social network data processing stack in a sensor network analysis toolkit, called Apollo. Experiences with Apollo show that our techniques are successful at identifying and tracking credible events in the physical world.
AB - Signal processing techniques such as filtering, detection, estimation and frequency domain analysis have long been applied to extract information from noisy sensor data. This paper describes the exploitation of these signal processing techniques to extract information from social networks, such as Twitter and Instagram. Specifically, we view social networks as noisy sensors that report events in the physical world. We then present a data processing stack for detection, localization, tracking, and veracity analysis of reported events using social network data. We show using a controlled experiment that the behavior of social sources as information relays varies dramatically depending on context. In benign contexts, there is general agreement on events, whereas in conflict scenarios, a significant amount of collective filtering is introduced by conflicted groups, creating a large data distortion. We describe signal processing techniques that mitigate such distortion, resulting in meaningful approximations of actual ground truth, given noisy reported observations. Finally, we briefly present an implementation of the aforementioned social network data processing stack in a sensor network analysis toolkit, called Apollo. Experiences with Apollo show that our techniques are successful at identifying and tracking credible events in the physical world.
KW - Social networks
KW - signal processing.
UR - http://www.scopus.com/inward/record.url?scp=84987853187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987853187&partnerID=8YFLogxK
U2 - 10.1117/12.2229723
DO - 10.1117/12.2229723
M3 - Conference contribution
AN - SCOPUS:84987853187
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII
A2 - Kolodny, Michael A.
A2 - Pham, Tien
PB - SPIE
ER -