How to decide between mixed effects and panel data? I am trying to understand how the gender of the user relates to her/his use of language in tweets. For this purpose, I have downloaded all tweets of several hundred users and have obtained positive and negative sentiment scores for each tweet. The data looks as the following:

Should I analyze this as mixed-effects model with tweet_possent (or tweet_negsent) as DVs with twitter_user_gender nested under twitter_user? Or should I be converting this to panel data?
I am not sure if panel data analysis is more appropriate here.

Being aggressively practical here: Use the same methodology as prior well-cited works on the subject and/or what your audience is more accustomed too.
Realistically this question boils down to the meta-question: Should we deal with this primarily as econometricians (panel data) or as biostatisticians (longitudinal data)? In Biostatistics, longitudinal data are repeated observations of the same individuals (Twitter users in the use case above), these repeated measurements are what Econometrics terms as panel data.
CV.SE has some great threads looking into the distinction between fixed/random effects:

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*What is the difference between fixed effect, random effect and mixed effect models?

*How exactly does a "random effects model" in econometrics relate to mixed models outside of econometrics?

*What is a difference between random effects-, fixed effects- and marginal model?
