Abstract
A social tie is a target for ongoing, high-level scientific debate. Measuring the tie strength in social networks has been an important topic for academic studies since Mark Granovetter's seminal papers in 1970's. However, it is still a problematic issue mainly for two reasons: 1) existing tie strength measurements may not reflect the true social connections of individuals accurately enough, and 2) many different methods to gather data from social media are not applicable anymore due to different data openness issues. In addition, we have only little empirical knowledge of the actual tie strengthening process in online social networks. Therefore, we suggest a new approach to tie strength research, which focuses on studying communication patterns (edges) more rather than actors (nodes) in a social network.
In this paper we build a social network analysis-based approach to enable the evaluation of tie strength based on reciprocal interaction from publicly available Facebook data, and suggest that this approach could work as a basis for further tie strength studies. Our approach makes use of weak tie theory, and enables researchers to study micro-level interactions (i.e. discussions, messages, relationships) with large-scale social network analysis (SNA). This study provides a way to find relevant actors from publicly available data in the context of tie strengthening process, and answers how to take this stream of research closer to computational social science.
In this paper we build a social network analysis-based approach to enable the evaluation of tie strength based on reciprocal interaction from publicly available Facebook data, and suggest that this approach could work as a basis for further tie strength studies. Our approach makes use of weak tie theory, and enables researchers to study micro-level interactions (i.e. discussions, messages, relationships) with large-scale social network analysis (SNA). This study provides a way to find relevant actors from publicly available data in the context of tie strengthening process, and answers how to take this stream of research closer to computational social science.
Originalsprog | Engelsk |
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Titel | Proceedings of the 21st International Academic Mindtrek Conference |
Redaktører | Markku Turunen, Heli Väätäjä, Janne Paavilainen, Thomas Olsson |
Udgivelsessted | New York |
Forlag | Association for Computing Machinery |
Publikationsdato | 2017 |
Sider | 203-209 |
ISBN (Trykt) | 9781450354264 |
ISBN (Elektronisk) | 9781450354264 |
DOI | |
Status | Udgivet - 2017 |
Begivenhed | 21st Academic Mindtrek Conference 2017 - Tampere Hall Congress and Concert Centre, Tampere, Finland Varighed: 20 sep. 2017 → 21 sep. 2017 Konferencens nummer: 21 https://www.mindtrek.org/2017/academic/ |
Konference
Konference | 21st Academic Mindtrek Conference 2017 |
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Nummer | 21 |
Lokation | Tampere Hall Congress and Concert Centre |
Land/Område | Finland |
By | Tampere |
Periode | 20/09/2017 → 21/09/2017 |
Internetadresse |
Emneord
- Social network
- Social tie
- Social network analysis
- Tie strength
- Open data