113 citations · 342 across the 17 of their papers we have counts for
18 papers
How Does Empowering Users with Greater System Control Affect News Filter Bubbles?
Ping Liu, Karthik Shivaram, Aron Culotta +2
While recommendation systems enable users to find articles of interest, they can also create ``filter bubbles'' by presenting content that reinforces users' pre-existing beliefs. U…
Forecasting Political News Engagement on Social Media
Karthik Shivaram, Mustafa Bilgic, Matthew Shapiro +1
Understanding how political news consumption changes over time can provide insights into issues such as hyperpartisanship, filter bubbles, and misinformation. To investigate long-t…
Characterizing Online Criticism of Partisan News Media Using Weakly Supervised Learning
Karthik Shivaram, Mustafa Bilgic, Matthew Shapiro +1
We propose novel methods to identify tweets that criticize partisan news sources. Prior work suggests that criticism, ridicule, and distrust of news media all play important roles…
Leaders or Followers? A Temporal Analysis of Tweets from IRA Trolls
Siva K. Balasubramanian, Mustafa Bilgic, Aron Culotta +3
The Internet Research Agency (IRA) influences online political conversations in the United States, exacerbating existing partisan divides and sowing discord. In this paper we inves…
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach
Zhao Wang, Kai Shu, Aron Culotta
Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In th…
Robustness to Spurious Correlations in Text Classification via Automatically Generated Counterfactuals
Zhao Wang, Aron Culotta
Spurious correlations threaten the validity of statistical classifiers. While model accuracy may appear high when the test data is from the same distribution as the training data,…