How to Generate Popular Post Headlines on Social Media?

TL;DR

This paper introduces MEBART, a novel model that automates the generation of popular social media headlines by capturing trends and personal styles, significantly improving over existing methods.

Contribution

The paper presents MEBART, a new model that combines multiple preference extractors with BART to generate trending and personalized headlines for social media posts.

Findings

MEBART achieves state-of-the-art performance on real-world datasets.

It effectively captures trends and personal styles in headlines.

Extensive experiments validate its superiority over baselines.

Abstract

Posts, as important containers of user-generated-content pieces on social media, are of tremendous social influence and commercial value. As an integral components of a post, the headline has a decisive contribution to the post's popularity. However, current mainstream method for headline generation is still manually writing, which is unstable and requires extensive human effort. This drives us to explore a novel research question: Can we automate the generation of popular headlines on social media? We collect more than 1 million posts of 42,447 celebrities from public data of Xiaohongshu, which is a well-known social media platform in China. We then conduct careful observations on the headlines of these posts. Observation results demonstrate that trends and personal styles are widespread in headlines on social medias and have significant contribution to posts's popularity. Motivated by these insights, we present MEBART, which combines Multiple preference-Extractors with Bidirectional and Auto-Regressive Transformers (BART), capturing trends and personal styles to generate popular headlines on social medias. We perform extensive experiments on real-world datasets and achieve state-of-the-art performance compared with several advanced baselines. In addition, ablation and case studies demonstrate that MEBART advances in capturing trends and personal styles.