First of, there is. These NN are good at exploiting the 'spatiality prior' in some types of data, like text and images. It means that features in the data which are close together, should be combined when you climb in the hierarchy of features. Databases with columns and rows don't have that prior for instance.
Second, there is also the peer reviewing problem. You are still trying to explain a very abstract concept to your peers in a paper which is usually limited to 6 or 8 pages. Text and images make for very graspable examples in such a short paper. That's the reason why some other data with a spatial prior is not used as often, like time series or EEG-data.
So, there is a combination of those two elements at play.
Only the first reason is correct (NNs are good at data with dimensional relationships).
The second reason is pretty bogus (text/images more graspable). It's valid if you're talking about mass media / popular press. But for research papers 1) images / large snippets of text are actually a negative since images take a lot of space and 2) the people doing peer review are expert scientists. They know the benchmarks and the theory.
Second, there is also the peer reviewing problem. You are still trying to explain a very abstract concept to your peers in a paper which is usually limited to 6 or 8 pages. Text and images make for very graspable examples in such a short paper. That's the reason why some other data with a spatial prior is not used as often, like time series or EEG-data.
So, there is a combination of those two elements at play.