  In this paper, we investigate whether text from a Community Question
Answering (QA) platform can be used to predict and describe real-world
attributes. We experiment with predicting a wide range of 62 demographic
attributes for neighbourhoods of London. We use the text from QA platform of
Yahoo! Answers and compare our results to the ones obtained from Twitter
microblogs. Outcomes show that the correlation between the predicted
demographic attributes using text from Yahoo! Answers discussions and the
observed demographic attributes can reach an average Pearson correlation
coefficient of \r{ho} = 0.54, slightly higher than the predictions obtained
using Twitter data. Our qualitative analysis indicates that there is semantic
relatedness between the highest correlated terms extracted from both datasets
and their relative demographic attributes. Furthermore, the correlations
highlight the different natures of the information contained in Yahoo! Answers
and Twitter. While the former seems to offer a more encyclopedic content, the
latter provides information related to the current sociocultural aspects or
phenomena.
