Search Results for author: Zach Wood-Doughty

Found 9 papers, 5 papers with code

Faithful and Plausible Explanations of Medical Code Predictions

1 code implementation16 Apr 2021 Zach Wood-Doughty, Isabel Cachola, Mark Dredze

Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making.

Decision Making

Generating Synthetic Text Data to Evaluate Causal Inference Methods

no code implementations10 Feb 2021 Zach Wood-Doughty, Ilya Shpitser, Mark Dredze

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.

Causal Inference Text Generation

Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement

no code implementations13 Oct 2020 Aaron Mueller, Zach Wood-Doughty, Silvio Amir, Mark Dredze, Alicia L. Nobles

The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence.

Using Noisy Self-Reports to Predict Twitter User Demographics

1 code implementation1 May 2020 Zach Wood-Doughty, Paiheng Xu, Xiao Liu, Mark Dredze

We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions.

Convolutions Are All You Need (For Classifying Character Sequences)

no code implementations WS 2018 Zach Wood-Doughty, Nicholas Andrews, Mark Dredze

While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences.

Document Classification General Classification +2

Challenges of Using Text Classifiers for Causal Inference

1 code implementation EMNLP 2018 Zach Wood-Doughty, Ilya Shpitser, Mark Dredze

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.

Causal Inference Decision Making

Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter

1 code implementation WS 2018 Zach Wood-Doughty, Praateek Mahajan, Mark Dredze

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.

General Classification

Predicting Twitter User Demographics from Names Alone

1 code implementation WS 2018 Zach Wood-Doughty, Nicholas Andrews, Rebecca Marvin, Mark Dredze

Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends.

How Does Twitter User Behavior Vary Across Demographic Groups?

no code implementations WS 2017 Zach Wood-Doughty, Michael Smith, David Broniatowski, Mark Dredze

Demographically-tagged social media messages are a common source of data for computational social science.

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