Predicting Personal Attitudes Using Contextual Microblog Activity Logs

In this paper, we present a corpus model to show how personal attitudes can be predicted from social media or microblog activities for a specific domain of events such as natural disasters. More specifically, given a user tweet and an event, the model is used to predict whether the user will be willing to help or show a positive attitude towards that event or similar events in the future. We present a new dataset related to a specific natural disaster event, i.e., Hurricane Harvey, that distinguishes user tweets into positive and non-positive attitudes. We build Term Embeddings for Tweet (TEmT) to generate features to model personal attitudes for arbitrary user tweets. Finally, we evaluate the effectiveness of our method by employing multiple classification techniques on the newly created dataset.