1. Social Role Evolution of an Ideological Online Community Brittany I
Social Role Evolution of an Ideological Online Community Brittany I. Davidson, Simon Jones, Adam Joinson, & Joanne Hinds
For each of the clusters, we took the average of the reputation score, as seen in the two plots below.
From the plot on the left, we can see that clusters 7 and 8 have incredibly high reputation scores in comparison to the other clusters, therefore, these were regarded as leaders. The plot on the right shows clusters 1-6, and the 3 with the higher reputation scores were regarded as collaborators, leaving the rest of the clusters with the lowest reputation scores as contributors.
We mapped the number of users in each of the three roles: leaders, collaborators, and contributors, over each time slice to see how much or little the distributions changed.
We can see the the number of users that are leaders remains consistent over time. The collaborators and contributors were less consistent, which implies it may be more likely for users to change between clusters within those roles more frequently. Interestingly, as also shown in the plot above, contributors and collaborators are similar in user numbers, which is unexpected, as the Reader to Leader framework suggests the number of collaborators should be less than contributors, but more than leaders.
The next part of the analysis consists of checking the number of users joining and leaving at each time slice, and to see how well we are able to predict user roles, and whether they might change.
INTRODUCTION
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’.
Background & Aims
The Reader-to-Leader Framework is well established within human-computer interaction literature. It describes four roles that users adopt in online communities: Readers, Contributors, Collaborators, and Leaders. While these roles are far from exhaustive, they capture and attempt to explain common pathways of user participation.
Our work aims to improve the classification of community members by considering all behavioral metrics, as well as considering communication patterns between members of particular social roles once they have been classified. The initial research questions are therefore threefold:
What specific social roles can be identified using meta-data from online communities through user behavior?
Do user social roles change over time? What are the pathways for users?
How are these social roles structured in the overall system, and what does this tell us about the system heath and ecology?
This framework also captures that users may not move in a linear movement, which is helpful in terms of users being dynamic. However, what it does not offer is an indication of the proportion of users that shift from one role to another, nor the characteristics of those who do. We contend that it is useful to understand, and possibly predict, specific social roles and their path trajectories of certain individuals.
METHODS & ANALYSIS
The meta-data scraped from the online community was split into 4 six-month time slices. We utilized the k-means algorithm over the newest time slice of data. We found the idea number of clusters was 8. The table below shows descriptions of each of the 8 clusters found:
We then used a Naïve-Bayes algorithm to classify the users from all other time slices according to the 8 clusters found. From here, we needed to map these clusters back against the Reader to Leader Framework. We used the in-built reputation score in order to do this.