Lillio Mok


Behavioral science and HCI through massive datasets.

I do research on social systems by measuring how user behaviors and attitudes are tied to desirable outcomes. My work falls under computational social science and uses a variety of approaches from behavioral data science (e.g. user modeling and survey science), applied ML/NLP (e.g. embeddings), and HCI (e.g. experiments and user studies).

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Unfair Risk Assessments

Through an online experiment simulating credit risk assessments, we find that people judge an unfair algorithmic assessor more harshly than an identically unfair human agent.


Politicians in Social Media

We analyze hundreds of thousands of news articles referring to politicians on Reddit, and discover that the people sharing the articles and the language they use are polarized in opposite ways.


Polarized Online News

We investigate millions of links shared on Reddit to show that polarized news is restricted to but endemic in a small group of hyper-partisan communities.


Cross-Timezone Meetings

By combining contextual inquiry, a survey, and a telemetry trace of millions of meetings, we quantify how difficult but important it is for temporally-distant colleagues to meet.


Exploration Over Lifecycles

By modeling billions of listening events at Spotify, we find that people seek different kinds of content variety over the course of their lifecycles.


Screentime and Well-being

Our study combinatorially searches over millions of regressions to robustly measure the relationship between user’s time spent on Lichess, perceived time online, and well-being.


Note: WIP, under construction