CTM - A Model for Large-Scale Multi-View Tweet Topic Classification (2022)

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, that (a) supports 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. 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.

## introduction on many social media platforms like twitter, users find posts that they are interested in through two mechanisms: (a) search and (b) recommendation. both mechanisms typically use the topics associated with posts to identify potential candidates that are displayed to the user. therefore, automatically associating a post with topics is important for effective search and recommendation. furthermore, due to the diverse nature of social media content, for such topic association to be useful in practice, it is important to (a) support classification into a large number of topics (potentially hundreds or thousands of topics) and (b) allow for a post to have multiple topics or no topic at all. traditionally, there has been a long line of work on classifying documents (like news articles, movie reviews etc.) into topics @xcite @xcite @xcite . additionally, there have been attempts to leverage known label hierarchy to perform hierarchical classification of documents. most of these approaches learn a model per node of the hierarchy with potentially some form of hierarchy-based regularization in-order to assign labels to a document at each level in the label taxonomy @xcite @xcite . with the rise of social media platforms, researchers noted that classification of social media content poses several unique challenges @xcite . first, such posts can be very short and noisy with very weak cues provided by the linguistic context @xcite . second, content may be multi-modal with associated images, videos, and hyperlinks. approaches for classifying documents tend to ignore this multimodal nature @xcite . several works do explore classification of social media posts (like tweets) @xcite @xcite @xcite @xcite @xcite @xcite . however, all of these works suffer from one or more limitations: (a) support only a few topics (an order of 10 topics) (b) model only the text, ignore multi-modal content, deeper semantic-cues and (c) do not support multiple labels per post. in this paper, we address all of the above limitations in the context of tweet classification. we propose ctm (concept topic model), a tweet topic classification model that (a) supports classification into 300 topics (10 times larger than prior work) (b) incorporates rich content like media, hyperlinks, author features, entity features thus moving beyond shallow tweet text features and (c) supports multiple topics to be associated per tweet. 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 20% in median average precision score. ctm has been successfully deployed at twitter where online a/b experiments have also shown increased engagement and improved customer experience. ## related work early works on tweet classification used bag-ofwords features constructed from tweet text and classifiers like rocchio classifiers, logistic regression, and support-vector machines @xcite @xcite @xcite . follow-up work investigated using increasingly rich features for topic classification including graph-based features of term-co-occurrence graphs, hyperlink information, and distributed representations derived from deep learning models @xcite @xcite @xcite . however, one notes at-least one of the following limitations in all of the above works: (a) focus on a very small number of topics (5 -20) (b) do not support multiple topic labels per tweet (c) do not consider or discuss how to model content beyond the raw tweet text (d) do not capture label constraints. a sole exception to some of the above limitations is the work of @xcite which performs large-scale tweet topic classification focusing on 300 topic labels in a real-time setting using only n-gram based features derived from the tweet text, but ignores other cues. we revisit their large-scale setting after a decade and propose a vastly improved model for large-scale tweet topic classification modeling tweets holistically. we closely follow the procedure outlined by @xcite which first samples tweets based on topic priors to obtain tweets that are weakly relevant to a topic, and then seeks label confirmation from trained human annotators. specifically, we consider tweets originating from users that are known to tweet mostly about a given topic (for example: tweets authored by cnn are almost certainly about the "news" topic). we collect 100k such tweets with at-least 200 tweets per topic. we then sought label confirmation from trained human annotators with each tweet-topic pair being independently rated by 3 annotators and use a majority vote to determine the final labels (see appendix for details). finally, we create training, validation, and test splits of this dataset disjoint at both the tweet and the user level. foot_2 • weakly labeled data (wld dataset): we also construct a large-scale data-set of weakly labeled tweets (wld dataset) for taskspecific pre-training (see section 4). specifically, we use the rule-based system to obtain a random sample of 250 million weakly labeled tweets that is disjoint from the hcomp dataset both in terms of time-span and tweets. ## models and methods problem formulation. we formulate our problem as one of standard multi-label classification. formally, let s denote the given set of topics. given x, a set of tweet features and a set of topics @xmath0, we seek to model pr(l|x). we encode the topic labels l as a binary vector y of length |s| using multi-hot encoding. we consider a simple approach to multi-label classification foot_3 -a neural architecture parameterized by θ that outputs a vector ŷ of length |s| where ŷi ∈ [0, 1] is the probability of the tweet belonging to topic i. model overview. ctm has three components: ## conclusion we revisited the problem of large scale tweet topic classification posed by @xcite and proposed a model for classifying tweets into a large set of 300 topics with improved performance. in contrast to prior work we take a holistic approach to modeling tweets and model not only the immediate tweet text, but also associated media, hyperlinks, author context, entity mentions, and incorporate domain knowledge expressed as topic constraints in a principled manner. our model showed significantly increased engagement and improved customer experience in several online a/b experiments, and it has been deployed into production at twitter with millions of active users. finally, while our model and approach has been restricted to tweet classification, our proposed methods and observations may benefit other social media platforms seeking to classify content into a large number of topics effectively.