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 Twitter API calls, and we showcase its usage in an exploratory analysis of a topical COVID-19 dataset.
|Publication status||Published - 2021|
|Event||WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining - Virtual Event Israel|
Duration: 1 Jan 2021 → …
|Conference||WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining|
|Period||1/01/21 → …|