Evaluating the impact of tweet characteristics on audience engagement with fact-checking: a machine learning approach
Fiorucci, Daniele (A.A. 2023/2024) Evaluating the impact of tweet characteristics on audience engagement with fact-checking: a machine learning approach. Tesi di Laurea in Machine learning, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 89. [Master's Degree Thesis]
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Abstract/Index
Why is this problem important? Literature review and identification of research gaps. The new frontier of fake news: the deepfake. Fake news, deepfake and social network. Exploring deepfake risks: politics, wars and pornography. Types of deepfakes. Deepfake generative’s models. Fact-checking and deepfakes. Regulation and education. Collaboration between different actors. Methods and analysis. Dataset and schema. Methods. Workflow overview. Analysis.
References
Bibliografia: pp. 72-76. Sitografia: p. 77.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Machine learning |
| Thesis Supervisor: | Italiano, Giuseppe Francesco |
| Thesis Co-Supervisor: | Finocchi, Irene |
| Academic Year: | 2023/2024 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 07 Jan 2025 15:07 |
| Last Modified: | 07 Jan 2025 15:07 |
| URI: | https://tesi.luiss.it/id/eprint/40759 |
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