arXiv:2606.07219v1 Announce Type: new Abstract: The convergence of large language models and social bots allows malicious actors to manipulate the information ecosystem by generating human-like content at scale. Existing models for detecting AI-generated content often fail in the wild, primarily due to the lack of ground-truth data. We address this gap through an adversarial methodology that models the impersonation of real social media users by malicious actors. Using this methodology, we curate a multilingual, cross-platform dataset of paired human and AI-generated messages. Training on such
The proliferation of advanced large language models has made the generation of highly convincing, AI-created disinformation much easier and more scalable, necessitating new detection methods.
This research directly addresses the challenge of identifying sophisticated AI-generated content on social platforms, crucial for maintaining information integrity and mitigating manipulation.
The development of adversarial methodologies and curated datasets equips cybersecurity and social media platforms with better tools to combat malicious AI-driven information campaigns.
- · Cybersecurity firms
- · Social media platforms
- · Information integrity researchers
- · Democratic institutions
- · Malicious actors
- · Disinformation networks
- · Unsophisticated detection models
Improved detection capabilities will make it harder for AI-generated content to spread unchecked on social platforms.
This could lead to an arms race, with malicious actors developing more sophisticated generation techniques and detectors adapting in response.
Long-term, this could foster greater public trust in online information if the most egregious forms of AI-driven manipulation are contained, or further erode it if the arms race is lost.
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