Research

We propose analyzing refractive datasets (i.e. datasets from platforms with relatively more open data policies that reflect data from closed datasets) as a methodology for researchers to understand platforms in the post-API era.

Despite search engines' crucial role in health information, the study finds that search accuracy on abortion topics is influenced by users' positions and hindered by search engine optimization and advertising.

This paper describes how biographies about women who meet Wikipedia’s criteria for inclusion are more frequently considered non-notable and nominated for deletion compared to men’s biographies.

Applying a combination of web-scraping, deep learning, natural language processing, and qualitative analysis to pages of academics nominated for deletion on Wikipedia, we demonstrate how Wikipedia's notability guidelines are unequally applied across race and gender.

This paper bridges a gap in affordance theory by analyzing how pundits, propagandists, and conspiracy theorists activate technological affordances on platforms like Twitter, Google Scholar, and Yandex, transforming disinformation into an entangled, participatory process.

A sociotechnical framework for understanding the stickiness of misinformation

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