Improving Categorisation in Social Media Using Hyperlinks to Structured Data Sources

Abstract. Social media presents unique challenges for topic classifica-tion, including the brevity of posts, the informal nature of conversations, and the frequent reliance on external hyperlinks to give context to a con-versation. In this paper we investigate the usefulness of these external hyperlinks for categorising the topic of individual posts. We focus our analysis on objects that have related metadata available on the Web, either via APIs or as Linked Data. Our experiments show that the in-clusion of metadata from hyperlinked objects in addition to the original post content significantly improved classifier performance on two dis-parate datasets. We found that including selected metadata from APIs and Linked Data gave better results than including text from HTML pages. We investigate how this improvement varies across different top-ics. We also make use of the structure of the data to compare the use-fulness of different types of external metadata for topic classification in a social media dataset