Sexism Identification on TikTok: A Multimodal AI Approach with Text, Audio, and Video

Author:

Arcos IvánORCID,Rosso PaoloORCID

Publisher

Springer Nature Switzerland

Reference18 articles.

1. Alcântara, C., Moreira, V., Feijo, D.: Offensive video detection: dataset and baseline results. In: Proceedings of the Twelfth Language Resources and Evaluation Conference, pp. 4309–4319 (2020)

2. Ali, S., Tanweer, S., Khalid, S., Rao, N.: Mel frequency cepstral coefficient: a review, January 2021

3. Anzovino, M., Fersini, E., Rosso, P.: Automatic identification and classification of misogynistic language on twitter, pp. 57–64, May 2018

4. Baevski, A., Zhou, Y., Mohamed, A., Auli, M.: wav2vec 2.0: a framework for self-supervised learning of speech representations. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 12449–12460. Curran Associates, Inc. (2020)

5. Basile, V., et al.: Semeval-2019 task 5: multilingual detection of hate speech against immigrants and women in twitter, pp. 54–63, January 2019

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