Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909600701218816 |
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| author | Zhao, Yuying Wang, Yu Cheng, Xueqi Tumlin, Anne Marie Liu, Yunchao Xia, Damin Jiang, Meng Derr, Tyler |
| author_facet | Zhao, Yuying Wang, Yu Cheng, Xueqi Tumlin, Anne Marie Liu, Yunchao Xia, Damin Jiang, Meng Derr, Tyler |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01698 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs Zhao, Yuying Wang, Yu Cheng, Xueqi Tumlin, Anne Marie Liu, Yunchao Xia, Damin Jiang, Meng Derr, Tyler Social and Information Networks 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. |
| title | Amplifying Your Social Media Presence: Personalized Influential Content Generation with LLMs |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2505.01698 |