Learning Joint Topic Representation for Detecting Drift in Social Media Text

Social media texts like tweets and blogs are collaboratively created by human interaction. Rapidly changing trends are leading to topic drift in the social media text. This drift is usually associated with words and hashtags. However, geotags play an essential part in determining topic distribution with location context. The rate of change in the distribution of words, hashtags and geotags cannot be considered uniform and must be handled accordingly. This paper builds a topic model that associates the topic with a mixture of distributions of words, hashtags and geotags. Stochastic gradient Langevin dynamic model with varying mini-batch sizes is used to capture the changes due to the asynchronous distribution of words and tags. Topic representations with co-occurrence and location contexts are specified as hashtag context vector and geotag context vector respectively. These two vectors are jointly learned to yield topical word embedding vectors over time conditioned on hashtags and geotags that can predict location-based topical variations effectively. When evaluated with Chennai and UK geolocated Twitter data, the proposed joint topical word embedding model enhanced by the social tags context, outperforms other methods.