2012/06/29 by Derek Greene, Derek O'Callaghan, Derek O’Callaghan +4
Computer Science · Physics and Astronomy · Social Sciences · #Business #Complex Network Analysis Techniques #Computer science #Context (archaeology) #Crowdsourcing #Data science #Event (particle physics) #FOS: Computer and information sciences #FOS: Physical sciences #Geography #Information retrieval #Internet privacy #Microblogging #Opinion Dynamics and Social Influence #Order (exchange) #Physics and Society (physics.soc-ph) #Social Media and Politics #Social and Information Networks (cs.SI) #Social media #Topic model #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1207.0017
arxiv created 2012/06/29 · openalex publication_date 2012/06/29 · arxiv updated 2012/07/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A particular challenge in the area of social media analysis is how to find communities within a larger network of social interactions. Here a community may be a group of microblogging users who post content on a coherent topic, or who are associated with a specific event or news story. Twitter provides the ability to curate users into lists, corresponding to meaningful topics or themes. Here we describe an approach for crowdsourcing the list building efforts of many different Twitter users, in order to identify topical communities. This approach involves the use of ensemble community finding to produce stable groupings of user lists, and by extension, individual Twitter users. We examine this approach in the context of a case study surrounding the detection of communities on Twitter relating to the London 2012 Olympics.