Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
Accurate bot detection is necessary for the safety and integrity of online
platforms. It is also crucial for research on the influence of bots in
elections, the spread of misinformation, and financial market manipulation.
Platforms deploy infrastructure to flag or remove automated accounts, but their
tools and data are not publicly available. Thus, the public must rely on
third-party bot detection. These tools employ machine learning and often
achieve near perfect performance for classification on existing datasets,
suggesting bot detection is accurate, reliable and fit for use in downstream
applications. We provide evidence that this is not the case and show that high
performance is attributable to limitations in dataset collection and labeling
rather than sophistication of the tools. Specifically, we show that simple
decision rules -- shallow decision trees trained on a small number of features
-- achieve near-state-of-the-art performance on most available datasets and
that bot detection datasets, even when combined together, do not generalize
well to out-of-sample datasets. Our findings reveal that predictions are highly
dependent on each dataset's collection and labeling procedures rather than
fundamental differences between bots and humans. These results have important
implications for both transparency in sampling and labeling procedures and
potential biases in research using existing bot detection tools for
pre-processing.