A Cross-Modal Classification Dataset on Social Network

Abstract

Classifying tweets into general categories, such as food, music and games, is an essential work for social network platforms, which is the basis for information recommendation, user portraits and content construction. As far as we know, nearly all existing general tweet classification datasets only have textual content. However, textual content in tweets may be short, meaningless, and even none, which would harm the classification performance. In fact, images and videos are widespread in tweets, and they can intuitively provide extra useful information. To fill this gap, we construct a novel Cross-Modal Classification Dataset constructed from Weibo called CMCD. Specifically, we collect tweets with three modalities of text, image and video from 18 general categories, and then filter tweets that can easily be classified by only textual contents. Finally, the whole dataset consists of 85,860 tweets, and all of them have been manually labelled. Among them, 64.4% of tweets contain images, and 16.2% of tweets contain videos. We implement classical baselines for tweets classification and report human performance. Empirical results show that the classification over CMCD is challenging enough and requires further efforts.