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Found 13 papers, 7 papers with code
How Does Twitter User Behavior Vary Across Demographic Groups?
Demographically-tagged social media messages are a common source of data for computational social science.
Predicting Twitter User Demographics from Names Alone
Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends.
Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter
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.
Challenges of Using Text Classifiers for Causal Inference
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.
Convolutions Are All You Need (For Classifying Character Sequences)
While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences.
Using Noisy Self-Reports to Predict Twitter User Demographics
We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions.
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement
The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence.
Generating Synthetic Text Data to Evaluate Causal Inference Methods
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.
Faithful and Plausible Explanations of Medical Code Predictions
Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making.
The Proximal ID Algorithm
Unobserved confounding is a fundamental obstacle to establishing valid causal conclusions from observational data.
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
A fundamental goal of scientific research is to learn about causal relationships.
Segment Anything Model is a Good Teacher for Local Feature Learning
To do so, first, we construct an auxiliary task of Pixel Semantic Relational Distillation (PSRD), which distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination.
Model Distillation for Faithful Explanations of Medical Code Predictions
We propose to use knowledge distillation, or training a student model that mimics the behavior of a trained teacher model, as a technique to generate faithful and plausible explanations.
