Classifying and Ranking Microblogging Hashtags with News Categories

TL;DR

This paper presents a method to classify microblogging hashtags into news categories and rank their popularity within each domain, helping users find relevant and trending hashtags more effectively.

Contribution

It introduces a novel approach combining domain classification with popularity ranking based on news content, tailored for microblogging platforms.

Findings

Effective classification of hashtags into news categories.

Domain-sensitive popularity ranking identifies hot hashtags.

Experimental results demonstrate approach's usefulness.

Abstract

In microblogging, hashtags are used to be topical markers, and they are adopted by users that contribute similar content or express a related idea. However, hashtags are created in a free style and there is no domain category information about them, which make users hard to get access to organized hashtag presentation. In this paper, we propose an approach that classifies hashtags with news categories, and then carry out a domain-sensitive popularity ranking to get hot hashtags in each domain. The proposed approach first trains a domain classification model with news content and news category information, then detects microblogs related to a hashtag to be its representative text, based on which we can classify this hashtag with a domain. Finally, we calculate the domain-sensitive popularity of each hashtag with multiple factors, to get most hotly discussed hashtags in each domain. Preliminary experimental results on a dataset from Sina Weibo, one of the largest Chinese microblogging websites, show usefulness of the proposed approach on describing hashtags.