# Birdspotter: A Tool for Analyzing and Labeling Twitter Users

arXiv: https://arxiv.org/abs/2012.02370 · HTML: https://mlsanity.com/paper/2012.02370/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Interval-censored Hawkes processes](https://mlsanity.com/paper/2104.07932/) (2021)
- [X-Troll: eXplainable Detection of State-Sponsored Information Operations Agents](https://mlsanity.com/paper/2508.16021/) (2025)

## Abstract (arXiv)

The impact of online social media on societal events and institutions is profound; and with the rapid increases in user uptake, we are just starting to understand its ramifications. Social scientists and practitioners who model online discourse as a proxy for real-world behavior, often curate large social media datasets. A lack of available tooling aimed at non-data science experts frequently leaves this data (and the insights it holds) underutilized. Here, we propose birdspotter -- a tool to analyze and label Twitter users --, and birdspotter.ml -- an exploratory visualizer for the computed metrics. birdspotter provides an end-to-end analysis pipeline, from the processing of pre-collected Twitter data, to general-purpose labeling of users, and estimating their social influence, within a few lines of code. The package features tutorials and detailed documentation. We also illustrate how to train birdspotter into a fully-fledged bot detector that achieves better than state-of-the-art performances without making any Twitter API online calls, and we showcase its usage in an exploratory analysis of a topical COVID-19 dataset.