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Data Matters: Reflection on User Defined Social Prioritization
Telenor Group, Norway.
Ericsson Research.
Luleå University of Technology.
2014 (English)In: ASE@360 Open Scientific Digital Library, ASE@360 , 2014Conference paper, Published paper (Refereed)
Abstract [en]

Online social networking becomes an integral part of our everyday life and thus, social computing is get- ting huge attention in these years. One of the areas of social computing is to understand humans' social or tie strength by observing measurable social interactions. Thanks to today's communication and social media services that open tremendous opportunities for communicating through electronic media, such as through mobile phone calls, SMS, emails, or social media tools. That has made it possible to automatically measure and predict human's social strength. 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 be- tween the persons. For example, 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. This paper thus presents reflection of user- defined ranking of social prioritization in comparison with machine defined social strength. The study found that there is significant difference in results between the algorithmic and the user-defined strength ranking, which indicates the inability of the algorithms to capture intrinsic knowledge (such as the importance of family bonds and non-electronic interaction). This would mean that the participants' ranking was colored by their interaction in real-life. This study also found several implications, in which diverse source and volume of interaction data are considered as key performance issues for algorithmic strength ranking.

Place, publisher, year, edition, pages
ASE@360 , 2014.
Keyword [en]
Data, Social networking, Euclidean Distance, Prediction
National Category
Media and Communication Technology
Research subject
Computer and Information Sciences Computer Science, Media Technology
Identifiers
URN: urn:nbn:se:lnu:diva-41712ISBN: 978-1-62561-000-3 (print)OAI: oai:DiVA.org:lnu-41712DiVA: diva2:800465
Conference
2014 ASE BIGDATA/SOCIALCOM/CYBERSECURITY Conference, Stanford University, May 27-31, 2014
Available from: 2015-04-05 Created: 2015-04-05 Last updated: 2015-04-22Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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