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

TL;DR

This paper introduces CTM, a neural model for large-scale multi-view tweet topic classification that leverages multi-modal content and author context, achieving significant performance improvements and deployment at Twitter.

Contribution

The paper presents a novel neural model, CTM, capable of classifying tweets into 300 topics by integrating multi-modal content and author context, addressing challenges of short text and multiple labels.

Findings

20% relative improvement in median average precision

Supports 300 topics for large-scale classification

Successfully deployed in Twitter production environment

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 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.