Abstract
The concept of weak ties was introduced by Granovetter through the seminal paper titled "Strength of weak ties". Since then the role of weak ties in general and their specific role as occupying the structural hole has been explored in many different fields. In this study, we identify actual or potential weak ties using publicly available social media data in the context of an event. Our case study environment is community managers' online discussions in social media in connection to the yearly-organized Community Manager Appreciation Day (CMAD 2016) event in Finland. We were able to identify potential weak ties using the conversation based structural holes, making use of social network analysis methods (like clustering) and content analysis in the context of events. We add to the understanding of and useful data sources for the Strength of weak ties theory originated from Granovetter, and developed further by other researchers. Our approach may be used in future to make more sophisticated conference recommendation systems, and significantly automate the data extraction for making useful contact recommendations from them for conference participants.
Originalsprog | Engelsk |
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Titel | Academic Mindtrek 2016 : Proceedings of the 20th International Academic Mindtrek Conference 2016 |
Antal sider | 9 |
Udgivelsessted | New York |
Forlag | Association for Computing Machinery |
Publikationsdato | 2016 |
Sider | 11-19 |
Artikelnummer | 2994354 |
ISBN (Trykt) | 9781450343671 |
ISBN (Elektronisk) | 9781450343671 |
DOI | |
Status | Udgivet - 2016 |
Begivenhed | 20th Academic Mindtrek Conference 2016 - University of Tampere, Tampere, Finland Varighed: 17 okt. 2016 → 19 okt. 2016 Konferencens nummer: 20 http://www.mindtrek.org/2016/academic/ |
Konference
Konference | 20th Academic Mindtrek Conference 2016 |
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Nummer | 20 |
Lokation | University of Tampere |
Land/Område | Finland |
By | Tampere |
Periode | 17/10/2016 → 19/10/2016 |
Internetadresse |
Emneord
- Weak ties
- Tie strength
- Structural hole
- Event
- Conference
- Social media
- Recommendation system