Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making.
High-dimensional and unstructured data such as natural language complicates the evaluation of causal inference methods; such evaluations rely on synthetic datasets with known causal effects.
The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence.
We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions.
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets.
Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets.
Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends.