Abstract

Disagreements, oppositions and negative opinions are indispensable parts of online political debates. In social media, people express their beliefs and attitudes not only on issues but also about each other through both their conversations and platform-specific interactions such as like, share in Facebook and retweet in Twitter. While there are explicit "like" features in these platforms, there is no explicit "dislike" feature. Many network analysis tasks, such as detecting communities and monitoring their dynamics (i.e. polarization patterns) require information about both positive and negative linkages. Hence, predicting negative links between users is an important task and a challenging problem. In this study, we propose an unsupervised framework to predict the negative links between users by utilizing explicit positive interactions and sentiment cues in conversations. We show the effectiveness of the proposed framework on a political Twitter dataset annotated through Amazon MTurk crowdsourcing platform. Our experimental results show that the proposed framework outperforms other well-known methods and proposed baselines. To illustrate the contribution of the predicted negative links, we compare the community detection accuracies using signed and unsigned user networks. Experimental results using predicted negative links show superiority on three political datasets where the camps are known a priori. We also present qualitative evaluations related to the polarization patterns (i.e. rivalries and coalitions) between the detected communities which is only possible in the presence of negative links.

Original languageEnglish (US)
Title of host publicationHT 2017 - Proceedings of the 28th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
Pages125-134
Number of pages10
ISBN (Electronic)9781450347082
DOIs
StatePublished - Jul 4 2017
Event28th ACM Conference on Hypertext and Social Media, HT 2017 - Prague, Czech Republic
Duration: Jul 4 2017Jul 7 2017

Publication series

NameHT 2017 - Proceedings of the 28th ACM Conference on Hypertext and Social Media

Other

Other28th ACM Conference on Hypertext and Social Media, HT 2017
CountryCzech Republic
CityPrague
Period7/4/177/7/17

Keywords

  • Negative Link Prediction
  • Online Political Networks
  • Sentiment Analysis
  • Social Media Mining

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence
  • Human-Computer Interaction
  • Software

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  • Cite this

    Ozer, M., Yildirim, M. Y., & Davulcu, H. (2017). Negative link prediction and its applications in online political networks. In HT 2017 - Proceedings of the 28th ACM Conference on Hypertext and Social Media (pp. 125-134). (HT 2017 - Proceedings of the 28th ACM Conference on Hypertext and Social Media). Association for Computing Machinery, Inc. https://doi.org/10.1145/3078714.3078727