Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs

TL;DR

This paper explores how Large Language Models can generate personalized, influential social media content by integrating network structure information into prompts, aiming to enhance user visibility and post influence.

Contribution

It introduces a novel prompt-based approach that combines content and social network data to improve social media influence using LLMs.

Findings

Network-aware prompts outperform content-only prompts in influence metrics

Incorporating social network structure enhances post visibility

Empirical results validate the effectiveness of the proposed methods

Abstract

The remarkable advancements in Large Language Models (LLMs) have revolutionized the content generation process in social media, offering significant convenience in writing tasks. However, existing applications, such as sentence completion and fluency enhancement, do not fully address the complex challenges in real-world social media contexts. A prevalent goal among social media users is to increase the visibility and influence of their posts. This paper, therefore, delves into the compelling question: Can LLMs generate personalized influential content to amplify a user's presence on social media? We begin by examining prevalent techniques in content generation to assess their impact on post influence. Acknowledging the critical impact of underlying network structures in social media, which are instrumental in initiating content cascades and highly related to the influence/popularity of a post, we then inject network information into prompt for content generation to boost the post's influence. We design multiple content-centric and structure-aware prompts. The empirical experiments across LLMs validate their ability in improving the influence and draw insights on which strategies are more effective. Our code is available at https://github.com/YuyingZhao/LLM-influence-amplifier.