aalto1 untyped-item.component.html
Quantifying controversy in social media
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
Series
WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining, pp. 33-42
Abstract
Which topics spark the most heated debates in social media? Identifying these topics is a first step towards creating systems which pierce echo chambers. In this paper, we perform a systematic methodological study of controversy detection using social media network structure and content. Unlike previous work, rather than identifying controversy in a single hand-picked topic and use domain-specific knowledge, we focus on comparing topics in any domain. Our approach to quantifying controversy is a graph-based three-stage pipeline, which involves (i) building a conversation graph about a topic, which represents alignment of opinion among users; (ii) partitioning the conversation graph to identify potential sides of the controversy; and (iii) measuring the amount of controversy from characteristics of the graph. We perform an extensive comparison of controversy measures, as well as graph building approaches and data sources. We use both controversial and non-controversial topics on Twitter, as well as other external datasets. We find that our new random-walk-based measure outperforms existing ones in capturing the intuitive notion of controversy, and show that content features are vastly less helpful in this task.
Description
| openaire: EC/H2020/654024/EU//SoBigData
Keywords
Other note
Citation
Garimella, V, De Francisci Morales, G, Gionis, A & Mathioudakis, M 2016, Quantifying controversy in social media. in WSDM 2016 - Proceedings of the 9th ACM International Conference on Web Search and Data Mining. ACM, pp. 33-42, ACM International Conference on Web Search and Data Mining, San Francisco, California, United States, 22/02/2016. https://doi.org/10.1145/2835776.2835792