Understanding user behavior in online communities is important to researchers, analysts, designers, and community managers who wish to assess the health of a community, design improved interaction mechanisms, or build incentive and reward structures for motivating participation. While it is understood that users often adjust their behavior and participation levels, the pathways that users follow during their lifecycle within a community is less understood. Hence, we ask: what stimulates one user’s progression to a position of leadership, while others fail to develop authority and influence within a community? To address this question, we identify various social roles in one online community using machine learning techniques. We then map these roles against Preece and Schneiderman’s ‘Reader-to-Leader framework’.
|Publication status||Published - 10 Jul 2018|
|Event||First International Conference on Behavioural and Social Sciences in Security - Lancaster House Hotel, Lancaster, UK United Kingdom|
Duration: 10 Jul 2018 → 12 Jul 2018
|Conference||First International Conference on Behavioural and Social Sciences in Security|
|Abbreviated title||BASS 2018|
|Country||UK United Kingdom|
|Period||10/07/18 → 12/07/18|