CTM -- A Model for Large-Scale Multi-View Tweet Topic Classification
This addresses the problem of effective search and recommendation on social media platforms by enabling scalable topic classification with a large topic space, though it is incremental as it builds on prior work for a more challenging setting.
The paper tackled large-scale multi-view tweet topic classification by proposing CTM, a neural model that leverages multi-modal content and author context to classify tweets into 300 topics, achieving a 20% relative lift in median average precision score and deployment at Twitter.
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 by proposing a novel neural model, CTM that (a) supports a large topic space of $300$ topics and (b) takes a holistic approach to tweet content modeling -- leveraging multi-modal content, author context, and deeper semantic cues in the Tweet. Our method offers an effective way to classify Tweets into topics at scale by yielding superior performance to other approaches (a relative lift of $\mathbf{20}\%$ in median average precision score) and has been successfully deployed in production at Twitter.