Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs
Journal:
arXiv
Published Date:
May 3, 2025
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.
