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This paper investigates how providing users with transparency and control over a political news recommendation system affects filter bubbles. A user study found that the enhanced interface increased awareness of filter bubbles but had heterogeneous effects on news consumption diversity.
This paper investigates whether topic sentiment causally affects perceived political ideology in news articles, comparing human annotations from AllSides with those from LLMs including GPT-4o-mini and Llama-3.3-70B. It finds that fine-tuned GPT-4o-mini exhibits a spurious sentiment-ideology coupling not present in human judgments, highlighting risks of using LLM annotations as proxies in causal analyses.