Testing Data Feminism in India

Abstract

Catherine D’Ignazio and Lauren F.Klein in their book Data Feminism argue that data models reflect existing power structures and social hierarchies. We aim to test this hypothesis in India on Instagram. Instagram is one of the most accessible and politically engaging social media platforms in India which makes its data models appropriate subjects for our study. The research question of our study is “Do Instagram data models disproportionately prioritise accounts that publish majoritarian feminist content over intersectional feminist content in India?” The paper employs two methodological approaches; an experimental set-up under controlled setting for primary data collection through a positivist sociological approach kept under time bound observational study and secondary data qualitative analysis. This paper first analyses the biases and preconceived notions which cloud digital data models. It further elaborates upon the concept of Data Justice which acknowledges historical inequalities and power differentials amongst communities that drive data collection. The paper attempts to test this hypothesis through an experiment. The experiment includes creation of two Instagram accounts dedicated to two different forms of feminism. Account “A” would publish content ascribing to popular feminist ideals i.e. non-intersectional and Account “B” would publish intersectional feminist content. The creation of new accounts is critical for establishing a causal relationship between data models and disparity in accounts growth, as a pre-established follower count would affect the accounts engagement. For a period of three months, both the accounts will employ the same strategies to increase user engagement/reach. The impact of these strategies on metrics such as follower count, post likes, post reshares, post comments, profile visits, frequency and duration of story views, and duration of post visibility would be documented through a weekly monitoring system. The data collected......