That's similar to people describing how to catch other frauds, such as fake Amazon comments or bots. It's medieval 'science': They usually have no evidence of their accuracy, either false negatives (frauds who they overlook) and false positives (people falsely accused of fraud). So it's easy to say, 'this is how to identify them' - nobody will ever test your claim.
Regarding false negatives, for example, there is reason to believe that people detect only the obvious frauds, and that our detection becomes tuned for the obvious. Regarding false positives, people will cite the 'obvious' positives - e.g., some humanly impossible property - but even if they are correct, the problem is the cases in the grey area. False accusations are no joke.
Ironically, now we want a bot to solve our problems. What data do we have to say that it's accurate, or any more accurate than we are?
