# Predicting User-Interactions on Reddit

arXiv: https://arxiv.org/abs/1707.00195 · HTML: https://mlsanity.com/paper/1707.00195/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Dialogue Response Ranking Training with Large-Scale Human Feedback Data](https://mlsanity.com/paper/2009.06978/) (2020)
- [The Anatomy of the Moltbook Social Graph](https://mlsanity.com/paper/2602.10131/) (2026)
- [This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign](https://mlsanity.com/paper/2201.05334/) (2022)
- [ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Tests](https://mlsanity.com/paper/2510.26095/) (2025)

## Abstract (arXiv)

In order to keep up with the demand of curating the deluge of crowd-sourced content, social media platforms leverage user interaction feedback to make decisions about which content to display, highlight, and hide. User interactions such as likes, votes, clicks, and views are assumed to be a proxy of a content's quality, popularity, or news-worthiness. In this paper we ask: how predictable are the interactions of a user on social media? To answer this question we recorded the clicking, browsing, and voting behavior of 186 Reddit users over a year. We present interesting descriptive statistics about their combined 339,270 interactions, and we find that relatively simple models are able to predict users' individual browse- or vote-interactions with reasonable accuracy.