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SIN: A Platform to Make Interactions in Social Networks Accessible
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0003-3219-9598
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2012 (English)In: Proceedings of the 2012 ASE International Conference on Social Information, IEEE conference proceedings, 2012, p. 205-214Conference paper, Published paper (Refereed)
Abstract [en]

Online Social Networks (OSNs) are popular platforms for interaction, communication and collaboration between friends. In this paper we develop and present a new platform to make interactions in OSNs accessible. Most of today's social networks, including Facebook, Twitter, and Google+ provide support for third party applications to use their social network graph and content. Such applications are strongly dependent on the set of software tools and libraries provided by the OSNs for their own development and growth. For example, third party companies like CNN provide recommendation materials based on user interactions and user's relationship graph. One of the limitations with this graph (or APIs) is the segregation from the shared content. We believe, and present in this paper, that the content shared and the actions taken on the content, creates a Social Interaction Network (SIN). As such, we extend Facebook's current API in order to allow applications to retrieve a weighted graph instead of Facebooks unweighted graph. Finally, we evaluate the proposed platform based on completeness and speed of the crawled results from selected community pages. We also give a few example uses of our API on how it can be used by third party applications.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2012. p. 205-214
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-10889DOI: 10.1109/SocialInformatics.2012.29ISBN: 978-1-4799-0234-7 (print)OAI: oai:DiVA.org:bth-10889DiVA, id: diva2:865144
Conference
International Conference on Social Informatics (SocialInformatics, Washington D.C.
Available from: 2015-10-27 Created: 2015-10-27 Last updated: 2018-01-10Bibliographically 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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Erlandsson, FredrikJohnson, Henric

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