Longitudinal Investigation of Algorithmically Curated Content for Misinformation on Social Media Platforms

Social Data Dissertation Fellowship

Abstract

My research aims to tackle the problem of algorithmically curated misinformation on social media platforms by auditing search systems to investigate their role in surfacing misinformation by either recommending fake content or by ranking it higher in the search results. Through my research, I answer the following questions: How can we systematically and ethically investigate online search systems for misinformation? What role do user attributes (gender, age, political affiliation), user actions (click-action, like-action, follow-action, etc.), and high-impact events (elections, Covid-19, shootings, etc.) play in amplifying the misinformation surfacing in algorithmically curated content like search results and recommendations? What is the longitudinal impact of these attributes on the amount of misinformation presented to users? How can we empirically quantify the prevalence of this misinformation?

To further this research, I am designing audit methodologies to measure algorithmically curated misinformation on multiple online platforms across various user features, external event occurrences, and popular search queries. Using this methodology, I will conduct an exhaustive set of carefully controlled experiments to audit Web and social media search interfaces with a focus on YouTube and Amazon. My research will result in tools that can automatically run audit experiments to test for personalization and amplification of misinformation in search systems.

Research Team

Principal Investigator

Prerna Juneja

PhD Student, University of Washington

  • Bio ▾

    Prerna Juneja is a third-year PhD student at the University of Washington Information School. She is advised by Dr. Tanushree Mitra and is a member of the Social Computing Lab. She is interested in computational social science, wherein she applies methods from statistical modeling, machine learning, and natural language processing to study the credibility of content on social media platforms. Her research involves designing and deploying auditing pipelines to study biases in algorithmic platforms and their impact on society with a long-term goal of designing spaces that communities and users can trust. Currently, she is investigating social media platforms for algorithmically surfaced misinformation that appears in search results and recommendations and studying the attributes that amplify such misinformation. As evident from her research direction, she deeply cares about algorithmic transparency and accountability.

    Previously, she obtained her master's degree in computer science from IIIT Delhi. After graduation, she worked as a software engineer at Dell EMC for three years, where she garnered four awards for her work: the Dell Champion Award in 2018, and Excellence@Dell Bronze Award in 2018, 2017, and 2016. Her research has been published at several venues, including CSCW, GROUP, COMPSAC, and BPI.

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