android is particularly concerning. This was also the case when I did the original analysis, so it doesn't look like things have changed much since then. It may be that the community lacks enough experts to tackle answering the huge volume of questions. It may also be that the APIs are not yet mature and there are lots of people running into problems that have no solution.
This analysis is flawed. Android, iOS, Facebook...these are "hawt technologies" that everyone and their mother in the development world is trying to get in on. Consequently, it is not unusual to see badly worded questions from people who can barely write english about these technologies.
Additionally, some of the questions asked are so basic and betray such a lack of understanding (from Person off the Street X picking up O'Reilly book Y and thinking they're going to put their resume on the Net as a programmer today) that many experts will read such a question and not bother to waste their time with it.
Especially given that sometimes taking the time to write a well-thought out and reasoned response to question yields limited views and up votes. A reputation based incentive model works as long as the community is well engaged. But when many questions asked are unmarked as answered by the questioner, and there's little activity, there's not as much incentive to spend the time necessary to provide a detailed answer on question that "looks shady."
A more compelling, but obviously harder to answer, question than just looking at the tags is:
Based on these tagged questions, how many of them are substantive?
How can we define substanative? A difficult scientific question to answer. Proposed metrics:
- How many of them encourage lengthy discussion?
- What is the reputation of the asker? (are they User19374655845092 with rating 1? Those should probably be filtered out)
- Do they betray a basic lack of understanding of principles of programming or introductory concepts, such as polymorphism, inheritance, etc etc
- (this is the hardest question to answer. Involves some sort of text analysis, deriving a semantic network and trying to glean the concepts from it. May help to cluster users based on keywords used and classify question in proximity to others)
Just looking at tags and answered versus unanswered is good and fine. But be careful of the conclusions you're drawing from such a limited scope. Richer analysis would probably involve use of paired t-tests or ANOVA across more metrics. It would be fruitful to write up a blog describing the data collection and analysis process as well.