We know what @you #tag

TLDR

Hashtags function both as content bookmarks linking similar tweets and as symbols of community membership, bridging virtual user communities. The study investigates whether users recognize hashtags’ dual role, how it influences their adoption behavior, and whether adoption is predictable. The authors develop comprehensive measures to quantify content and community factors influencing how users select hashtags and join communities. Large‑scale Twitter experiments show that content and community measures strongly correlate with hashtag adoption, enabling machine‑learning models to predict future adoption of previously unused hashtags.

Abstract

Researchers and social observers have both believed that hashtags, as a new type of organizational objects of information, play a dual role in online microblogging communities (e.g., Twitter). On one hand, a hashtag serves as a bookmark of content, which links tweets with similar topics; on the other hand, a hashtag serves as the symbol of a community membership, which bridges a virtual community of users. Are the real users aware of this dual role of hashtags? Is the dual role affecting their behavior of adopting a hashtag? Is hashtag adoption predictable? We take the initiative to investigate and quantify the effects of the dual role on hashtag adoption. We propose comprehensive measures to quantify the major factors of how a user selects content tags as well as joins communities. Experiments using large scale Twitter datasets prove the effectiveness of the dual role, where both the content measures and the community measures significantly correlate to hashtag adoption on Twitter. With these measures as features, a machine learning model can effectively predict the future adoption of hashtags that a user has never used before.