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The strength of social strength: an evaluation study of algorithmic versus user-defined ranking
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-4549-6751
2014 (English)In: Proceedings of the ACM Symposium on Applied Computing, New York: ACM Digital Library, 2014, p. 658-659Conference paper, Published paper (Refereed)
Abstract [en]

A family relation is generally considered to be stronger than a relation between coworkers in our society, but the strength of the relation is intrinsic and have been cumbersome to measure. The fact that we increasingly communicate electronically, such as through email, mobile phone calls or social media, has made it possible to automatically measure and analyze the relation between persons. This paper presents an evaluation study of social strength, where the social strength is defined as a metric that represents the tie strength of the relation between persons, calculated based on the frequency, duration, context and media type of the electronic communication between the persons. The study found that the Utility Function performs better because it emphasize the communication frequency between persons. There is however a significant difference in results between the algorithms and the user-defined ranking. This indicates the inability of the algorithms to capture intrinsic knowledge (such as the importance of family bonds and nonelectronic interaction). This would mean that the participants' ranking was colored by their interaction in real-life. It is however evident from the study that the functions provide more accurate results when they utilize multiple sources of communication history over only a single source. Finally, capturing sufficient communication data from multiple data sources is very hard, as access to such data is restricted because of concerns regarding for instance business and privacy. A conclusion is that the algorithms requires a larger data set, preferably being captured continuously over a period longer than 2 weeks, to achieve a better accuracy that is closer to the ground truth. However, the study shows the feasibility of capturing social strength automatically and we believe that the results is an important step towards systems that reason about the relation between persons in order to make communications services more pervasive.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2014. p. 658-659
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-30801DOI: 10.1145/2554850.2555158Local ID: 4bed0465-7010-4876-88b1-b82afdad4127ISBN: 978-1-4503-2469-4 (print)OAI: oai:DiVA.org:ltu-30801DiVA, id: diva2:1004030
Conference
Annual ACM Symposium on Applied Computing : 24/03/2014 - 28/03/2014
Note
Godkänd; 2014; 20140819 (johsod)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-01-14Bibliographically approved

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Total: 191 hits
CiteExportLink to record
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Citation style
  • apa
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Output format
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