  This paper presents techniques to detect the "offline" activity a person is
engaged in when she is tweeting (such as dining, shopping or entertainment), in
order to create a dynamic profile of the user, for uses such as better
targeting of advertisements. To this end, we propose a hybrid LSTM model for
rich contextual learning, along with studies on the effects of applying and
combining multiple LSTM based methods with different contextual features. The
hybrid model is shown to outperform a set of baselines and state-of-the-art
methods. Finally, this paper presents an orthogonal validation with a real-case
application. Our model generates an offline activity analysis for the followers
of several well-known accounts, which is quite representative of the expected
characteristics of these accounts.
