# Understanding Types of Users on Twitter

arXiv: https://arxiv.org/abs/1406.1335 · HTML: https://mlsanity.com/paper/1406.1335/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2014.

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

- [TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels](https://mlsanity.com/paper/2110.03664/) (2021)
- [Agent-based simulation of online social networks and disinformation](https://mlsanity.com/paper/2512.22082/) (2025)
- [Seeing and Believing: Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2107.08027/) (2021)
- [Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2210.15214/) (2022)
- [Should LLM Agents Decide in Social Simulations? Comparing Finite-State and LLM-Based Decision Policies](https://mlsanity.com/paper/2606.12369/) (2026)

## Abstract (arXiv)

People use microblogging platforms like Twitter to involve with other users for a wide range of interests and practices. Twitter profiles run by different types of users such as humans, bots, spammers, businesses and professionals. This research work identifies six broad classes of Twitter users, and employs a supervised machine learning approach which uses a comprehensive set of features to classify users into the identified classes. For this purpose, we exploit users' profile and tweeting behavior information. We evaluate our approach by performing 10-fold cross validation using manually annotated 716 different Twitter profiles. High classification accuracy (measured using AUC, and precision, recall) reveals the significance of the proposed approach.