# Twitter User Representation Using Weakly Supervised Graph Embedding

arXiv: https://arxiv.org/abs/2108.08988 · HTML: https://mlsanity.com/paper/2108.08988/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)
- [Overcoming Language Variation in Sentiment Analysis with Social Attention](https://mlsanity.com/paper/1511.06052/) (2015)
- [Abusive Language Detection with Graph Convolutional Networks](https://mlsanity.com/paper/1904.04073/) (2019)
- [You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP](https://mlsanity.com/paper/1909.00412/) (2019)
- [Quantifying Mental Health from Social Media with Neural User Embeddings](https://mlsanity.com/paper/1705.00335/) (2017)
- [Neural Character-based Composition Models for Abuse Detection](https://mlsanity.com/paper/1809.00378/) (2018)
- [A Hierarchical Location Prediction Neural Network for Twitter User Geolocation](https://mlsanity.com/paper/1910.12941/) (2019)
- [Exploiting Text and Network Context for Geolocation of Social Media Users](https://mlsanity.com/paper/1506.04803/) (2015)
- [Does Yoga Make You Happy? Analyzing Twitter User Happiness using Textual and Temporal Information](https://mlsanity.com/paper/2012.02939/) (2020)
- [Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model](https://mlsanity.com/paper/2104.03189/) (2021)
- [Yoga-Veganism: Correlation Mining of Twitter Health Data](https://mlsanity.com/paper/1906.07668/) (2019)
- [Do You Do Yoga? Understanding Twitter Users' Types and Motivations using Social and Textual Information](https://mlsanity.com/paper/2012.09332/) (2020)

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

- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Weakly Supervised Learning for Analyzing Political Campaigns on Facebook](https://mlsanity.com/paper/2210.10669/) (2022)
- [Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging](https://mlsanity.com/paper/2305.06174/) (2023)
- [Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations](https://mlsanity.com/paper/2409.02965/) (2024)

## Abstract (arXiv)

Social media platforms provide convenient means for users to participate in multiple online activities on various contents and create fast widespread interactions. However, this rapidly growing access has also increased the diverse information, and characterizing user types to understand people's lifestyle decisions shared in social media is challenging. In this paper, we propose a weakly supervised graph embedding based framework for understanding user types. We evaluate the user embedding learned using weak supervision over well-being related tweets from Twitter, focusing on 'Yoga', 'Keto diet'. Experiments on real-world datasets demonstrate that the proposed framework outperforms the baselines for detecting user types. Finally, we illustrate data analysis on different types of users (e.g., practitioner vs. promotional) from our dataset. While we focus on lifestyle-related tweets (i.e., yoga, keto), our method for constructing user representation readily generalizes to other domains.