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I haven’t used it strictly for thematic analysis but I do use it for my lit reviews (in which I am coding sections of articles for concepts I would like to cover in a paper) and content analysis. My Uni provides it for free, but I have found it useful and easy to use and would likely pay to use it if I had to. There are many features I have not yet used but I have heard others say they also enjoy using it for a variety of projects. I do believe there is a thematic coding process you can use within the program. More on reddit.com

I hate QDA software, namely because my qualitative analysis is always inductive, interpretive, and about theory-building. If it’s for theory-testing, QDA software like NVivo is great; you just code stuff deductively and categorize it all and count it. You aren’t theory-testing much if you’re doing phenomenology, imo. QDA software won’t be much help. I would worry less about the tools and more about the sense you’re making of data. I’d write memos first, then see what emerges that’s interesting and follow that. Once you have a decent idea of what you’re looking for, I would then think about the unit of analysis that lets me gain insight into that. There are a lot of ways to chunk/segment data, so be intentional. THEN you can code - at which point you can ask what tool might help you do that. Personally, I do all my coding on Google Sheets or Google Docs (if coding is even a core analytic activity I engage in at all; oftentimes it’s not). I like those tools bc they’re flexible and easy to import or export data or segments. NVivo and MaxQDA and Atlas.TI are such a pain to me. (Again, useful for deductive coding and generating counts but not for emergent theory-building work.) So, I’d start with some sense making and memoing and see what feels right afterwards. More on reddit.com

With 50k tweets, you have to take a random sample, as manually coding everything isn’t feasible. NVivo can work well for that sample, but it’s not really built for massive, unstructured datasets like tweets, and the learning curve can be steep. If you want to process the whole dataset, AI tools like AILYZE can automate theme and sub-theme detection and give you visual reports. Just keep in mind: if you use AI for coding, it’s good practice (and often ethically necessary) to review a portion manually to validate accuracy and minimize bias. More on reddit.com