CTM - A Model for Large-Scale Multi-View Tweet Topic Classification

Anonymous

Abstract: Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classification of such posts is quite challenging because of (a) a large topic space (b) short text with weak topical cues, and (c) multiple topic associations per post. In contrast to most prior work which only focuses on post classification into a small number of topics ($10-20$), we consider the task of large-scale topic classification in the context of Twitter where the topic space is $10$ times larger with potentially multiple topic associations per Tweet. We address the challenges above and propose a novel neural model, CTM that (a) associates tweets from a large topic space of $300$ topics (b) takes a holistic approach to tweet content modeling -- leveraging multi-modal content, author context, and deeper semantic cues in the tweet. We evaluate CTM quantitatively and show that our method offers an effective way to classify Tweets into topics at scale and is superior in performance to other approaches yielding a significant relative lift of $\mathbf{20}\%$.