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A member’s reputation in an online community is a quantified representation of their trustworthiness within the community. Reputation is calculated using rules-based algorithms which are primarily tied to the upvotes or downvotes a member receives on posts. The main drawback of this form of reputation calculation is the inability to consider dynamic factors such as a member’s activity (or inactivity) within the community. The research involves the construction of dynamic mathematical models to calculate reputation and then determine to what extent these results compare with rules-based models. This research begins with exploratory research of the existing corpus of knowledge. Constructive research in the building of mathematical dynamic models and then empirical research to determine the effectiveness of the models. Data collected from the Stack Overflow (SO) database is used by models to calculate a rule-based and dynamic member reputation and then using statistical correlation testing methods (i.e., Pearson and Spearman) to determine the extent of the relationship. Statistically significant results with moderate relationship size were found from correlation testing between rules-based and dynamic temporal models. The significance of the research and its conclusion that dynamic and temporal models can indeed produce results comparative to that of subjective vote-based systems is important in the context of building trust in online communities. Developing models to determine reputation in online communities based upon member post and comment activity avoids the potential drawbacks associated with vote-based reputation systems.
O'Neill, P. (2022). A Computational Model of Trust Based on Dynamic Interaction in the Stack Overflow Community [Technological University Dublin]. DOI: 10.21427/X57Z-VV43