Skip to content

The Impact of Personalized AI Response Suggestions on Content Creators'Direct Messaging

Oct 2026 · 0 citations · 111 references
Computer Science

TL;DR

A system providing personalized, AI-generated response suggestions tailored to creators'writing styles to maintain their media personas is designed, indicating that while AI suggestions enhance efficiency, they can undermine a creator's engagement in communication by shifting their attention toward monitoring AI.

Abstract

Direct messaging is often employed by social media content creators to directly interact with followers. Those creators attempt to engage with followers in private communication, but they often encounter a large volume of messages. Through formative interviews with 15 creators, we found that creators frequently experience fatigue from writing suitable content, as they moderate audience expectations while concurrently integrating their media personas into responses. To explore how AI can address these challenges, we designed a system providing personalized, AI-generated response suggestions tailored to creators'writing styles to maintain their media personas. In a comparison study with 13 creators that examines how the system affects response behaviors, creators valued the system's ability to soften potential impoliteness and anticipated higher response rates, helping them maintain a professional image. However, creators reported additional labor in verifying the authenticity of AI outputs to mitigate risks caused by AI misalignment. Our results indicate that while AI suggestions enhance efficiency, they can undermine a creator's engagement in communication by shifting their attention toward monitoring AI.

View source

Similar papers

The AI Penalty: Exploring User Perception and Engagement with AI-Generated Content on Instagram

Surprisingly, in consumer lifestyle content, an AI-generated caption can lead to improved perceptions and greater engagement, and there were also unexpected outcomes, including the revelation that labeling AI-generated content does not significantly affect consumer responses.

Vanessa Lau, Student In Marketing, Chi Thuy et al. · 0 citations
Oct 2026

The Impact of AI-Generated Content on Trust and Authenticity in Social Media Communication: A Study of Three Australian Case Studies

The findings suggest that generative AI has reshaped social media communication by improving efficiency and personalization while disrupting authenticity assessment, and sustainable AI integration requires stronger governance, platform transparency, and human oversight to maintain communication credibility.

Zhao-Yang Song · 0 citations
Oct 2026

Toward a Theory of Critical AI Literacies as Media Literacies

A teachable framework for critical AI literacies is proposed that shares key outcomes with frameworks for critical media literacies: the ability to competently navigate interface persuasion, recognize the incentives found in product design, identify simulations of empathy and care, and make safer decisions about when—a...

Sean Cho Ayres · 0 citations
Book Open access Oct 2026

Interpreting AI-Mediated Support: Understanding Its Effectiveness in Social Media–Induced Anxiety

Algorithmically curated social media feeds can provoke perceived anxiety in everyday use, yet little is known about how AI can provide effective support in these contexts. We present TriggerDetector, an LLM-powered system that infers plausible anxiety triggers from user-encountered posts and offers coping suggestions....

Yi-Wen Shang, Yang-Tao Ge, Lucy J. Robinson et al. · 0 citations
Book Open access Oct 2026

Does AI Kill "Real" Networking? Opportunities and Risks of AI-Assisted Self-Presentation on Professional Online Networks

LinkedIn is the largest social networking platform in the world for job seeking, recruitment, and presenting a professional identity online. As AI-assisted writing tools become increasingly embedded in how professionals write and post on LinkedIn, they raise important questions about the presentation and authenticity o...

Han Nguyen, Guo Freeman, Christopher Flathmann · 0 citations
Open access Oct 2026

A Web-Based Multi-Modal AI Framework for Intelligent Content Moderation with Constructive Feedback

Traditional content moderation systems primarily rely on punitive measures that focus on removing offending or inappropriate content without offering users any form of educational feedback or guidance. This lack of constructive interaction often leads to a poor user experience, leaving users frustrated and uninformed r...

Naveen Palanichamy, Marwah Zaid Mohammed Al-Helali, S. Haw et al. · 0 citations

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.