# Author2Vec: A Framework for Generating User Embedding

arXiv: https://arxiv.org/abs/2003.11627 · HTML: https://mlsanity.com/paper/2003.11627/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [Humanoid Agents: Platform for Simulating Human-like Generative Agents](https://mlsanity.com/paper/2310.05418/) (2023)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Learning User Embeddings from Human Gaze for Personalised Saliency Prediction](https://mlsanity.com/paper/2403.13653/) (2024)
- [Myers-Briggs personality classification from social media text using pre-trained language models](https://mlsanity.com/paper/2207.04476/) (2022)
- [The Sensitivity of Word Embeddings-based Author Detection Models to Semantic-preserving Adversarial Perturbations](https://mlsanity.com/paper/2102.11917/) (2021)

## Abstract (arXiv)

Online forums and social media platforms provide noisy but valuable data every day. In this paper, we propose a novel end-to-end neural network-based user embedding system, Author2Vec. The model incorporates sentence representations generated by BERT (Bidirectional Encoder Representations from Transformers) with a novel unsupervised pre-training objective, authorship classification, to produce better user embedding that encodes useful user-intrinsic properties. This user embedding system was pre-trained on post data of 10k Reddit users and was analyzed and evaluated on two user classification benchmarks: depression detection and personality classification, in which the model proved to outperform traditional count-based and prediction-based methods. We substantiate that Author2Vec successfully encoded useful user attributes and the generated user embedding performs well in downstream classification tasks without further finetuning.