Used by politicians, journalists, and citizens, Twitter has been the most important social media platform to investigate political phenomena such as hate speech, polarization, or terrorism for over a decade. A high proportion of Twitter studies of emotionally charged or controversial content limit their ability to replicate findings due to incomplete Twitter-related replication data and the inability to recrawl their datasets entirely. This paper shows that these Twitter studies and their findings are considerably affected by nonrandom tweet mortality and data access restrictions imposed by the platform. While sensitive datasets suffer a notably higher removal rate than nonsensitive datasets, attempting to replicate key findings of Kim’s (2023, Political Science Research and Methods 11, 673–695) influential study on the content of violent tweets leads to significantly different results. The results highlight that access to complete replication data is particularly important in light of dynamically changing social media research conditions. Thus, the study raises concerns and potential solutions about the broader implications of nonrandom tweet mortality for future social media research on Twitter and similar platforms.
Hyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model’s performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31%) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models.