Investigating audio emotional patterns in pseudoscience videos

Vryzas N, Vrysis L, Kostarella I, Dimoulas C. Investigating audio emotional patterns in pseudoscience videos. In: Express Paper. Audio Engineering Society; 2024. Paper 243. Available from: https://aes.org/publications/elibrary-page/?id=22589

@inproceedings{Vryzas2024_22589,
  author = {Vryzas, Nikolaos and Vrysis, Lazaros and Kostarella, Ioanna and Dimoulas, Charalampos},
  title = {{Investigating audio emotional patterns in pseudoscience videos}},
  note = {Paper 243},
  year = {2024},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22589}
}

TY  - CPAPER
TI  - Investigating audio emotional patterns in pseudoscience videos
AU  - Vryzas, Nikolaos
AU  - Vrysis, Lazaros
AU  - Kostarella, Ioanna
AU  - Dimoulas, Charalampos
M1  - Paper 243
PY  - 2024
DA  - 2024/06/06
UR  - https://aes.org/publications/elibrary-page/?id=22589
PB  - Audio Engineering Society
LA  - en
AB  - The SHAZAAM project aims to combat misinformation targeted at Generation Z through the integration of state-of-the-art technological tools. Evidence from the literature indicates that misinformation videos share common emotional patterns. Automated models using machine learning have shown promise in identifying misinformation in videos, particularly focusing on the emotional content of the audio. This research proposes a hierarchical classification approach that classifies audio segments extracted from short social media videos into music, speech, and other categories. On a second level, it applies music and speech sentiment analysis models trained on benchmark datasets. The functionality is offered as a web application to the end-user. A prototype of the application is presented.
ER  -