Understanding User Engagement with Cross-Platform Social Media Content Created by Humans Versus AI: An Evaluation of ChatGPT in Content Marketing

Even though generative artificial intelligence (GenAI) is increasingly integrated into user-facing technologies like social media, its impact on content marketing remains unverified. Early evidence suggests that language models (LLMs) can generate content that rivals human-created content (HCC) in terms of appeal. However, the question of adapting such content for various social media platforms remains unanswered. This study examines the effectiveness of an LLM, GPT-4, in customizing cross-platform content for Facebook, Instagram, and X. A total of 892 participants evaluated 30 pairs of AI-created content (ACC) and HCC. The findings reveal that ACC was preferred by users, delivered stronger calls to action, and elicited more user engagement than HCC, especially on Facebook, with a less pronounced effect for shorter posts on X and Instagram. We further generated six data-driven user personas of the 892 participants, illustrating the differences between those who preferred ACC or HCC on the three platforms. The results indicate that GPT-4 can adapt content to platform-specific requirements and maintain high perceived quality, making LLMs applicable for cross-platform content creation for user engagement. Findings contribute to understanding user engagement with AI-generated content across platforms. We also discuss the role of LLMs in content creation, including their ethical implications.

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Publication Details

Journal
ACM Transactions on the Web
Published
2026-04-20
DOI
https://doi.org/10.1145/3756014
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Understanding User Engagement with Cross-Platform Social Media Content Created by Humans Versus AI: An Evaluation of ChatGPT in Content Marketing

Kholoud K. Aldous, Soon-Gyo Jung, Ali Farooq, Bernard J. Jim Jansen et al.
ACM Transactions on the Web
AI in Service Interactions
article

Understanding User Engagement with Cross-Platform Social Media Content Created by Humans Versus AI: An Evaluation of ChatGPT in Content Marketing

Kholoud K. Aldous, Soon-Gyo Jung, Ali Farooq, Bernard J. Jim Jansen, Joni O. Salminen
article en

Abstract

Even though generative artificial intelligence (GenAI) is increasingly integrated into user-facing technologies like social media, its impact on content marketing remains unverified. Early evidence suggests that language models (LLMs) can generate content that rivals human-created content (HCC) in terms of appeal. However, the question of adapting such content for various social media platforms remains unanswered. This study examines the effectiveness of an LLM, GPT-4, in customizing cross-platform content for Facebook, Instagram, and X. A total of 892 participants evaluated 30 pairs of AI-created content (ACC) and HCC. The findings reveal that ACC was preferred by users, delivered stronger calls to action, and elicited more user engagement than HCC, especially on Facebook, with a less pronounced effect for shorter posts on X and Instagram. We further generated six data-driven user personas of the 892 participants, illustrating the differences between those who preferred ACC or HCC on the three platforms. The results indicate that GPT-4 can adapt content to platform-specific requirements and maintain high perceived quality, making LLMs applicable for cross-platform content creation for user engagement. Findings contribute to understanding user engagement with AI-generated content across platforms. We also discuss the role of LLMs in content creation, including their ethical implications.

ACM Transactions on the Web
University of Strathclyde (GB), Hamad bin Khalifa University (QA), University of Vaasa (FI)
Openalex Percentile: Top 14%
AI in Service Interactions
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Understanding User Engagement with Cross-Platform Social Media Content Created by Humans Versus AI: An Evaluation of ChatGPT in Content Marketing — Kholoud K. Aldous, Soon-Gyo Jung, et al. · ACM Transactions on the Web (2026) | TGRS Research Map | TGRS