A Comment on "Negativity Drives Online News Consumption" - Michael V. Reiss; Hauke Roggenkamp (Institute for Replication (I4R), 2025)
We examine the reproducibility and robustness of the central claims from Robertson et al. (2023) who investigate the impact of negative language on online news consumption by analyzing over 12,448 randomized controlled trials on upworthy.com. Applying "lexical" sentiment analyses, the authors make two central claims: first, they find that headlines with negative words significantly increase click-through rates (CTR). Second, they find that positive words in a headline reduce a news headline's CTR. Our reproducibility efforts include two different techniques: using the same data and procedures described in the study, we successfully reproduce the two claims through a blind computational approach, with only minor and inconsequential discrepancies. When using the authors' codes, we reproduce the two claims with identical numerical results. Examining the robustness of the authors' claims in a pre-registered third step, we validate and apply a "semantic" sentiment analysis using two large language models to re-compute their independent variables describing negativity and positivity. While we find support for the negativity bias, we do not find semantic (in contrast to lexical) positivity to reduce online news consumption.
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