Identifying Weak Ties from Publicly Available Social Media Data in an Event

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings


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.

Publication information

Original languageEnglish
Title of host publicationAcademic Mindtrek 2016 : Proceedings of the 20th International Academic Mindtrek Conference 2016
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Publication date2016
ISBN (Print)9781450343671
StatePublished - 2016
Event20th Academic Mindtrek Conference 2016 - University of Tampere, Tampere, Finland
Duration: 17 Oct 201619 Oct 2016
Conference number: 20


Conference20th Academic Mindtrek Conference 2016
LocationUniversity of Tampere

Bibliographical note

CBS Library does not have access to the material

    Research areas

  • Weak ties, Tie strength, Structural hole, Event, Conference, Social media, Twitter, Facebook, Recommendation system

ID: 45271571