Abstract: 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.

Introduction: Recent years have seen a huge boom in the number of different social media platforms available to users. People are increasingly using these platforms to voice their opinions or let others know about their whereabouts and activities. Each of these platforms has its own characteristics and is used for different purposes. The availability of a huge amount of data from many social media platforms has inspired researchers to study the relation between the data generated through the use of these platforms and real-world attributes. Many recent studies in this field are particularly inspired by the availability of text-based social media platforms such as blogs and Twitter. Text from Twitter microblogs, in particular, has been widely used as data source to make predictions in many domains. For example, box-office revenues are predicted using text from Twitter BIBREF0 . Twitter data has also been used to find correlations between the mood stated in tweets and the value of Dow Jones Industrial Average (DJIA) BIBREF1 . Predicting demographics of individual users using their language on social media platforms, especially Twitter, has been the focus of many research works: text from blogs and on-line forum posts are utilised to predict user's age through the analysis of linguistic features. Results show that the age of users can be predicted where the predicted and observed values reach a Pearson correlation coefficient of almost $0.7$ . Sociolinguistic associations using geo-tagged Twitter data have been discovered BIBREF2 and the results indicate that the demographic information of users such as first language, race, and ethnicity can be predicted by using text from Twitter with a correlation up to $0.3$ . Other research shows that users' income can also be predicted using tweets with a good prediction accuracy BIBREF3 . Text from Twitter microblogs has also been used to discover the relation between the language of users and the deprivation index of neighbourhoods. The collective sentiment extracted from the tweets of users has been shown BIBREF4 to have significant correlation ( $0.35$ ) with the deprivation index of the communities the users belong to. Data generated on QA platforms have not been used in the past for predicting real-world attributes. Most research work that utilise QA data aim to increase the performance of such platforms in analysing question quality BIBREF5 , predicting the best answers BIBREF6 , BIBREF7 or the best responder BIBREF8 . In this paper, we use the text from the discussions on the QA platform of Yahoo! Answers about neighbourhoods of London to show that the QA text can be used to predict the demographic attributes of the population of those neighbourhoods. We compare the performance of Yahoo! Answers data to the performance of data from Twitter, a platform that has been widely used for predicting many real-world attributes. Unlike many current works that focus on predicting one or few selected attributes (e.g. deprivation, race or income) using social media data, we study a wide range of 62 demographic attributes. Furthermore, we test whether terms extracted from both Yahoo! Answers and Twitter are semantically related to these attributes and provide examples of sociocultural profiles of neighbourhoods through the interpretation of the coefficients of the predictive models. The contributions of this paper can be summarised as follows:

Question: How do they define local variance?
Answer: The reciprocal of the variance of the attention distribution