Abstract
Political conversations have become a ubiquitous part of social media. When users interact and engage in discussions, there are usually two mediums available to them; textual conversations and platform-specific interactions such as like, share (Facebook) or retweet (Twitter). Major social media platforms do not facilitate users with negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Thus, detecting implicit negative links is an important and a challenging task. In this work, we propose an unsupervised framework utilising positive interactions, sentiment cues, and socially balanced triads for detecting implicit negative links. We also present an online variant of it for streaming data tasks. We show the effectiveness of both frameworks with experiments on two annotated datasets of politician Twitter accounts. Our experiments show that the proposed frameworks outperform other well-known and proposed baselines. To illustrate the detected implicit negative links' contribution, we compare the community detection accuracies using unsigned and signed networks. Experimental results using detected negative links show superiority on the three datasets where the camps are known a priori. We also present qualitative evaluations of polarisation patterns between communities which are only possible in the presence of negative links.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-25 |
| Number of pages | 25 |
| Journal | New Review of Hypermedia and Multimedia |
| DOIs | |
| State | Accepted/In press - Jun 26 2018 |
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Keywords
- dynamic graph algorithms
- Negative link
- non-negative matrix tri-factorization
- online political networks
- online social networks
ASJC Scopus subject areas
- Information Systems
- Media Technology
- Computer Science Applications
Cite this
Implicit negative link detection on online political networks via matrix tri-factorizations. / Ozer, Mert; Yildirim, Mehmet Yigit; Davulcu, Hasan.
In: New Review of Hypermedia and Multimedia, 26.06.2018, p. 1-25.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Implicit negative link detection on online political networks via matrix tri-factorizations
AU - Ozer, Mert
AU - Yildirim, Mehmet Yigit
AU - Davulcu, Hasan
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Political conversations have become a ubiquitous part of social media. When users interact and engage in discussions, there are usually two mediums available to them; textual conversations and platform-specific interactions such as like, share (Facebook) or retweet (Twitter). Major social media platforms do not facilitate users with negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Thus, detecting implicit negative links is an important and a challenging task. In this work, we propose an unsupervised framework utilising positive interactions, sentiment cues, and socially balanced triads for detecting implicit negative links. We also present an online variant of it for streaming data tasks. We show the effectiveness of both frameworks with experiments on two annotated datasets of politician Twitter accounts. Our experiments show that the proposed frameworks outperform other well-known and proposed baselines. To illustrate the detected implicit negative links' contribution, we compare the community detection accuracies using unsigned and signed networks. Experimental results using detected negative links show superiority on the three datasets where the camps are known a priori. We also present qualitative evaluations of polarisation patterns between communities which are only possible in the presence of negative links.
AB - Political conversations have become a ubiquitous part of social media. When users interact and engage in discussions, there are usually two mediums available to them; textual conversations and platform-specific interactions such as like, share (Facebook) or retweet (Twitter). Major social media platforms do not facilitate users with negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Thus, detecting implicit negative links is an important and a challenging task. In this work, we propose an unsupervised framework utilising positive interactions, sentiment cues, and socially balanced triads for detecting implicit negative links. We also present an online variant of it for streaming data tasks. We show the effectiveness of both frameworks with experiments on two annotated datasets of politician Twitter accounts. Our experiments show that the proposed frameworks outperform other well-known and proposed baselines. To illustrate the detected implicit negative links' contribution, we compare the community detection accuracies using unsigned and signed networks. Experimental results using detected negative links show superiority on the three datasets where the camps are known a priori. We also present qualitative evaluations of polarisation patterns between communities which are only possible in the presence of negative links.
KW - dynamic graph algorithms
KW - Negative link
KW - non-negative matrix tri-factorization
KW - online political networks
KW - online social networks
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U2 - 10.1080/13614568.2018.1482964
DO - 10.1080/13614568.2018.1482964
M3 - Article
SP - 1
EP - 25
JO - New Review of Hypermedia and Multimedia
JF - New Review of Hypermedia and Multimedia
SN - 1361-4568
ER -