# Learning Invariant Representations of Social Media Users

arXiv: https://arxiv.org/abs/1910.04979 · HTML: https://mlsanity.com/paper/1910.04979/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 21 of 21)

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- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)
- [USE: Dynamic User Modeling with Stateful Sequence Models](https://mlsanity.com/paper/2403.13344/) (2024)
- [URM4DMU: an user represention model for darknet markets users](https://mlsanity.com/paper/2303.10674/) (2023)
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- [Quantifying Misattribution Unfairness in Authorship Attribution](https://mlsanity.com/paper/2506.02321/) (2025)
- [STEB: Style Text Embedding Benchmark](https://mlsanity.com/paper/2606.31741/) (2026)

## Abstract (arXiv)

The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking. As a result, na"ive approaches may fail to generalize to new users or even to future observations of previously known users. In this paper, we propose a novel procedure to learn a mapping from short episodes of user activity on social media to a vector space in which the distance between points captures the similarity of the corresponding users' invariant features. We fit the model by optimizing a surrogate metric learning objective over a large corpus of unlabeled social media content. Once learned, the mapping may be applied to users not seen at training time and enables efficient comparisons of users in the resulting vector space. We present a comprehensive evaluation to validate the benefits of the proposed approach using data from Reddit, Twitter, and Wikipedia.