We did a bunch of analysis of the Tiobe methodology here:
https://juliacomputing.com/blog/2019/09/16/tiobe-blog.html
To summarize, Tiobe’s rankings are based on how may web page hits each language has for the phrase “$lang programming”. This methodology has severe problems. One problem is that people talk and write about different languages differently for linguistic reasons, so for some languages the phrase “$lang programming” is used a lot, whereas for others that phrasing isn’t used as much. For whatever reason, “Julia programming” just isn’t a phrase that people use to talk about Julia very much.
An even deeper problem with Tiobe’s methodology is that search engines return essentially random and frequently changing values for the number of hits on a given phrase. The number of hits reported can change massively from second to second with seemingly no rhyme or reason and it doesn’t seem like most search engines make any effort to ensure consistency or accuracy of the number of hits for a phrase. If you think about it, the question “how many web pages are there containing this phrase?” is not a really well-defined question—what exactly counts as a web page these days when there’s so much dynamically generated content? It’s also not something that search engines have much incentive to accurately measure or report since nobody looks beyond the first couple of pages of search results. Who actually cares about how many pages of search results there are?
My personal take away is that the Tiobe index doesn’t actually mean much except for the very top spots. It’s only a ranking that people talk about because people talk about it. There are a number of other indexes that have saner methodologies and are much more consistent over time, such as the IEEE Spectrum and PyPL rankings, which rank Julia as the 23rd and 26th most popular languages, respectively. Not only are these indices much less noisy then Tiobe, but their rankings also seem to make more intuitive sense.