Temporal-Weighted Bipartite Graph Model for Sparse Expert Recommendation in Community Question Answering

Krishna, Vaibhav; Antulov-Fantulin, Nino

doi:10.1145/3565472.3592957

Cited by 4 publications

(1 citation statement)

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“…Therefore, we introduced additional measure to consider individual expertise as a PLOS ONE control variable. Prior research has emphasized that users' expertise tends to evolve on CQA platforms [83,84]. Acknowledging the limitations of observational data in directly measuring expertise or domain knowledge, we utilize badges earned by individuals prior to answer contribution as a proxy.…”

Section: Robustness Checksmentioning

confidence: 99%

“The double-edged sword of inflated help”: Unravelling the motivation crowding in community question-answering platforms

Krishna,

Shrestha,

von Krogh

2024

PLoS ONE

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The growth of digital platforms has led to the proliferation of Online Communities, providing individuals with opportunities to seek help and share knowledge. A key challenge of help-related platforms that address technical questions (i.e., utilitarian, rather than opinion or supportive) is to ensure the contributions address seekers’ specific information needs. Despite growing academic interest in such platforms, research has mainly focused on factors that influence the quantity of contributions, ignoring whether these contributions effectively helped the seekers. To fill this research gap, this study draws upon theories of self-determination and motivation crowding to examine contributing behaviors that result in successful helping. By analyzing a rich dataset collected from an online Q&A platform, we find that gains in a help provider’s past rewards positively influence the success of contribution. Further, while previous studies suggest that external rewards result in a high quantity of contribution, our findings show that an inflated frequency of contribution leads to a crowding-out effect. Specifically, the contribution frequency has a curvilinear relationship with the success of the contribution. Taken together, these findings demonstrate there is a need to revisit the gamification mechanism on help-related platforms to ensure the success of knowledge contribution. This is crucial for the sustainability of these platforms as low-quality answers can lead users to mistrust and eventually leave the platform.

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Section: Robustness Checksmentioning

confidence: 99%