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Mining User Deliberation and Bias in Online Newsgroups: A Dynamic View
University of California - Davis, USA.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-3219-9598
University of California - Davis, USA.
2015 (English)In: Proceedings of the 2015 ACM on Conference on Online Social Networks, ACM , 2015, p. 209-219Conference paper, Published paper (Refereed)
Abstract [en]

Social media is changing many different aspects of our lives. By participating in online discussions, people exchange opinions on various topics, shape their stances, and gradually form their own characteristics. In this paper, we propose a framework for identifying online user characteristics and understanding the formation of user deliberation and bias in online newsgroups. In the first section of the paper, we propose a dynamic user-like graph model for recognizing user deliberation and bias automatically in online newsgroups. In addition, we evaluate our identification results with linguistic features and implement this model in our SINCERE system as a real-time service. In the second section, after applying this model to two large online newsgroups, we analyze the influence of early discussion context on the formation of user characteristics. Our conclusion is that user deliberation and bias are a product of situations, not simply dispositions: confronting disagreement in unfamiliar circumstances promotes more consideration of different opinions, while recurring conflict in familiar circumstances evokes close-minded behavior and bias. Based on this observation, we also build a supervised learning model to predict user deliberation and bias at an early online life-stage. Our results show that having only the first three months of users' interaction data generates an F1 accuracy level of around 70% in predicting user deliberation and bias in online newsgroups. This work has practical significance for people who design and maintain online newsgroups. It yields new insights into opinion diffusion and has wide potential applications in politics, education, and online social media.

Place, publisher, year, edition, pages
ACM , 2015. p. 209-219
Series
COSN ’15
Keywords [en]
dynamic graph, opinion diffusion, social media
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-15507DOI: 10.1145/2817946.2817951ISBN: 978-1-4503-3951-3 (electronic)OAI: oai:DiVA.org:bth-15507DiVA, id: diva2:1157255
Conference
ACM Conference on Online Social Networks (COSN´15), Stanford University
Available from: 2017-11-15 Created: 2017-11-15 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks
Open this publication in new window or tab >>Human Interactions on Online Social Media: Collecting and Analyzing Social Interaction Networks
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Online social media, such as Facebook, Twitter, and LinkedIn, provides users with services that enable them to interact both globally and instantly. The nature of social media interactions follows a constantly growing pattern that requires selection mechanisms to find and analyze interesting data. These interactions on social media can then be modeled into interaction networks, which enable network-based and graph-based methods to model and understand users’ behaviors on social media. These methods could also benefit the field of complex networks in terms of finding initial seeds in the information cascade model. This thesis aims to investigate how to efficiently collect user-generated content and interactions from online social media sites. A novel method for data collection that is using an exploratory research, which includes prototyping, is presented, as part of the research results in this thesis.

 

Analysis of social data requires data that covers all the interactions in a given domain, which has shown to be difficult to handle in previous work. An additional contribution from the research conducted is that a novel method of crawling that extracts all social interactions from Facebook is presented. Over the period of the last few years, we have collected 280 million posts from public pages on Facebook using this crawling method. The collected posts include 35 billion likes and 5 billion comments from 700 million users. The data collection is the largest research dataset of social interactions on Facebook, enabling further and more accurate research in the area of social network analysis.

 

With the extracted data, it is possible to illustrate interactions between different users that do not necessarily have to be connected. Methods using the same data to identify and cluster different opinions in online communities have also been developed and evaluated. Furthermore, a proposed method is used and validated for finding appropriate seeds for information cascade analyses, and identification of influential users. Based upon the conducted research, it appears that the data mining approach, association rule learning, can be used successfully in identifying influential users with high accuracy. In addition, the same method can also be used for identifying seeds in an information cascade setting, with no significant difference than other network-based methods. Finally, privacy-related consequences of posting online is an important area for users to consider. Therefore, mitigating privacy risks contributes to a secure environment and methods to protect user privacy are presented.

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2018
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 1
Keywords
Social Media, Social Networks, Crawling, Complex Networks, Information Cascade, Seed Selection, Privacy
National Category
Computer Sciences
Identifiers
urn:nbn:se:bth-15503 (URN)978-91-7295-344-4 (ISBN)
Public defence
2017-01-15, J1650, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2017-11-23 Created: 2017-11-15 Last updated: 2018-01-13

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