Towards Detection of AI-Generated Texts and Misinformation
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There have been some initial efforts to identify AI-generated misinformation from human-created datasets.
“There have been some initial efforts to identify AI-generated misinformation from human-created datasets [78,111].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [78]
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- Authors
- Najee-Ullah, A., Landeros, L., Balytskyi, Y., & Chang, S.-Y.
- Affiliations
- University of Colorado Colorado Springs (US)
- Corresponding author
- Najee-Ullah, Ahmad
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2022
- Pages
- 194-205
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4285293235
- Cited by
- 18
- References
- 11
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 3 · 2023: 3 · 2017: 1
- FWCI
- 10.87
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- AI-generated text detection, misinformation detection, disinformation, social bots, GPT, neural network classification, loss function comparison
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