# Its All in a Name: Detecting and Labeling Bots by Their Name

arXiv: https://arxiv.org/abs/1812.05932 · HTML: https://mlsanity.com/paper/1812.05932/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [Adversarial Socialbots Modeling Based on Structural Information Principles](https://mlsanity.com/paper/2312.08098/) (2023)
- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline](https://mlsanity.com/paper/2306.00037/) (2023)
- [BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2402.03740/) (2024)
- [Adversarial Botometer: Adversarial Analysis for Social Bot Detection](https://mlsanity.com/paper/2405.02016/) (2024)

## Abstract (arXiv)

Automated social media bots have existed almost as long as the social media environments they inhabit. Their emergence has triggered numerous research efforts to develop increasingly sophisticated means to detect these accounts. These efforts have resulted in a cat and mouse cycle in which detection algorithms evolve trying to keep up with ever evolving bots. As part of this continued evolution, our research proposes a multi-model 'tool-box' approach in order to conduct detection at various tiers of data granularity. To support this toolbox approach this research also uses random string detection applied to user names to filter twitter streams for bot accounts and use this as labeled training data for follow on research.