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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2511.05555 |
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| _version_ | 1866915605988245504 |
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| author | Kerbage, C. Bowman |
| author_facet | Kerbage, C. Bowman |
| contents | Generative AI (GenAI) poses a substantial threat to the integrity of information within the contemporary public sphere, which increasingly relies on social media platforms as intermediaries for news consumption. At present, most research efforts are directed toward automated and machine learning-based detection methods, despite growing concerns regarding false positives, social and political biases, and susceptibility to circumvention. This dissertation instead adopts a human-centred approach. It proposes the Deception Decoder; a multimodal, systematic, and topological framework designed to support general users in identifying AI-generated misinformation and disinformation across text, image, and video. The framework was developed through a comparative synthesis of existing models, supplemented by a content analysis of GenAI-video, and refined through a small-scale focus group session. While initial testing indicates promising improvements, further research is required to confirm its generalisability across user groups, and sustained effectiveness over time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deception Decoder: Proposing a Human-Focused Framework for Identifying AI-Generated Content on Social Media Kerbage, C. Bowman Computers and Society Social and Information Networks Generative AI (GenAI) poses a substantial threat to the integrity of information within the contemporary public sphere, which increasingly relies on social media platforms as intermediaries for news consumption. At present, most research efforts are directed toward automated and machine learning-based detection methods, despite growing concerns regarding false positives, social and political biases, and susceptibility to circumvention. This dissertation instead adopts a human-centred approach. It proposes the Deception Decoder; a multimodal, systematic, and topological framework designed to support general users in identifying AI-generated misinformation and disinformation across text, image, and video. The framework was developed through a comparative synthesis of existing models, supplemented by a content analysis of GenAI-video, and refined through a small-scale focus group session. While initial testing indicates promising improvements, further research is required to confirm its generalisability across user groups, and sustained effectiveness over time. |
| title | Deception Decoder: Proposing a Human-Focused Framework for Identifying AI-Generated Content on Social Media |
| topic | Computers and Society Social and Information Networks |
| url | https://arxiv.org/abs/2511.05555 |