Flamino et al. (2023) estimate the levels of ideological polarization and echo chamber behavior for Twitter (now X) users during the 2016 and 2020 U.S. presidential elections using political bias classification and network analysis methods. Using 873 million tweets, they find a decline in the proportion of fake and extremely biased content but identify an increase in echo chamber behaviors and latent ideological polarization among both users and influencers over the investigated period. Using the Twitter data and analysis code provided in the complementary OSF.io repository, we successfully reproduced the results of their analysis with only minor deviations due to small technical adjustments. In general, social media analyses frequently blur the distinction between reproduction and replication due to the dynamic nature of platform data and changing access policies resulting in difficulties retrieving consistent datasets over time. Hence, we conducted a robustness check by querying the Twitter/X Batch Compliance API to evaluate how many tweets from the initial dataset remain accessible today. Our "rehydration" attempts exposed substantial limitations in the Twitter/X API, as data retrieval issues arose across both free and paid access tiers, preventing us from re-collecting the original dataset or obtaining reliable estimates of tweet accessibility from the original study. While the study was largely reproducible with the intermediary and aggregated data provided, its full reproducibility and replicability are constrained by restrictive social media platform data access policies.

Knöpfle, P., Haim, M., & Breuer, J. (2025). A Replication Report on "Political polarization of news media and influencers on Twitter in the 2016 and 2020 US presidential elections" by Flamino et al. 2023. I4R Discussion Paper Series, 238. https://hdl.handle.net/10419/319836. (content_copy)