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Found 14 papers, 5 papers with code
Leaders or Followers? A Temporal Analysis of Tweets from IRA Trolls
The Internet Research Agency (IRA) influences online political conversations in the United States, exacerbating existing partisan divides and sowing discord.
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach
In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features.
Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals
However, the classifier trained on the combined data is more robust and performs well on both the original test data and the counterfactual test data (e. g., 12%-25% increase in accuracy compared with the traditional classifier).
Identifying Spurious Correlations for Robust Text Classification
The predictions of text classifiers are often driven by spurious correlations -- e. g., the term `Spielberg' correlates with positively reviewed movies, even though the term itself does not semantically convey a positive sentiment.
Are Words Commensurate with Actions? Quantifying Commitment to a Cause from Online Public Messaging
Often, this public messaging is aimed at aligning the entity with a particular cause or issue, such as the environment or public health.
When do Words Matter? Understanding the Impact of Lexical Choice on Audience Perception using Individual Treatment Effect Estimation
However, we lack general methods for estimating the causal effect of lexical choice on the perception of a specific sentence.
Forecasting the presence and intensity of hostility on Instagram using linguistic and social features
Online antisocial behavior, such as cyberbullying, harassment, and trolling, is a widespread problem that threatens free discussion and has negative physical and mental health consequences for victims and communities.
Co-training for Demographic Classification Using Deep Learning from Label Proportions
To further support domains in which the data consist of two conditionally independent feature views (e. g. image and text), we propose a co-training algorithm that iteratively generates pseudo bags and refits the deep LLP model to improve classification accuracy.
Controlling for Unobserved Confounds in Classification Using Correlational Constraints
In response, we propose a method to properly control for the influence of $z$ by first estimating its relationship with the class variable $y$, then updating predictions for $z$ to match that estimated relationship.
Anytime Active Learning
Our simulated experiments show that anytime active learning outperforms several baselines on these two datasets.
Ranked #1 on
Text Classification
on IMDb Movie Reviews
Inferring the Origin Locations of Tweets with Quantitative Confidence
Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics.
