Diya Saraf

Bio

My research studies the gap between what people say they want to see online and what recommendation algorithms learn they engage with — and what that gap means for the design of social platforms. I work on algorithmic auditing, recommender systems and personalization, and computational social science, combining large-scale observational data, controlled audits, and field studies with instrumented data collection. Recent work has looked at coordinated influence operations [npj Complexity'25] and engagement dynamics around harmful content [DPSH'24].

I'm a Ph.D. student in Computer Science at Johns Hopkins University, where I'm part of the Social Computing group advised by Tiziano Piccardi. Before Hopkins, I completed an M.S. in Computer Science at the University of Southern California, where I worked in the HUMANS Lab at the Information Sciences Institute with Emilio Ferrara and Luca Luceri on coordinated inauthentic behavior, algorithmic amplification, and audits of YouTube and TikTok recommendations. I received my B.S. in Computer Science & Engineering with a minor in Mathematics from Santa Clara University, where I was a Clare Boothe Luce Scholar advised by Yuhong Liu. I've also spent time in industry as a software engineering intern at Dell Technologies.

I'm broadly interested in industry research internships at the intersection of machine learning and social systems — in responsible AI, integrity and trust & safety, recommender systems, and computational social science. Feel free to reach out.

Research

Stated vs. revealed preferences in social media feeds

Comparing LLM-verbalized preference summaries against embedding-based representations for predicting which posts a user will actually engage with, and measuring where the two diverge.

Auditing knowledge platforms at scale

Systematically comparing coverage and framing across knowledge platforms — for example Grokipedia against Wikipedia — to surface structural differences in what gets documented and how.

Instrumented feed data collection

Building browser-based tooling that collects real, consented feed data from study participants, enabling field experiments on live recommendation systems rather than simulated ones.

Interests: computational social science · algorithmic auditing & accountability · recommender systems & personalization · LLMs and NLP for modeling user behavior · human–AI interaction · network science · trust & safety · responsible and human-centered AI

Publications

Most recent publications on Google Scholar.
^‡ indicates equal contribution.

Selected
All

Large-scale Detection of Multilingual Coordinated Activity on Telegram

Leonardo Blas, Diya Saraf^‡, Tanishq Salkar^‡, Nora Adadurova, Luca Luceri, Emilio Ferrara

npj Complexity, vol. 2, 33. 2025.

Paper Code

The Impact of Emojis on User Engagement with Trolling Content in Online Platforms

Diya Saraf, Yuhong Liu, Hooria Jazaieri

IEEE DPSH'24: Digital Platforms and Societal Harms. 2024.

Paper