Gender prediction on Norwegian Twitter accounts

In this thesis, methods for predicting the gender of Norwegian Twitter accounts were investigated. Through Twitterâ s public APIs, various account information is available. Tweets (text), personal descriptions, friends networks, and profile images were the main fields investigated. First separate classifiers were fitted to features from the different fields, and later the individual classifiersâ posterior probability estimates were combined to achieve increased accuracy. The datasets were labeled though comparison of the accountsâ names and names in the Norwegian population. Subsets of accounts with very gender specific names were used for training and testing. The highest balanced accuracy obtained was around 0.89. This, however, required access to the accountsâ profile images (85% of the data). Without images, the accuracy dropped to around 0.85