Birdspotter: A Tool for Analyzing and Labeling Twitter Users
Abstract
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.
Cite
@article{arxiv.2012.02370,
title = {Birdspotter: A Tool for Analyzing and Labeling Twitter Users},
author = {Rohit Ram and Quyu Kong and Marian-Andrei Rizoiu},
journal= {arXiv preprint arXiv:2012.02370},
year = {2021}
}