TY - JOUR
T1 - Intelligent identification of hate speeches to address the increased rate of individual mental degeneration
AU - Ava, Lamima Tabassum
AU - Karim, Asif
AU - Hassan, Md Mehedi
AU - Faisal, Fahad
AU - Azam, Sami
AU - Al Haque, A. S.M.Farhan
AU - Zaman, Sadika
N1 - Publisher Copyright:
© 2022 The Authors. Published by ELSEVIER B.V.
PY - 2023
Y1 - 2023
N2 - Hate speech is a public statement that demonstrates resentment or provokes disturbance towards a person or group often based upon race, age, religion, sexual orientation, minority group, psychical disability, political belief, etc. Such an act is a leading cause of mental degeneration in individuals observed throughout the world. We have witnessed an upsurge in the spreading of hateful speech through videos in recent times due to increased social media usage. Researchers are working on this issue because it has become more frequent on several social media platforms, and it leads to low self-esteem and has significant negative impacts on human life. In this work, we focus on collecting data from such videos as nowadays people are sharing numerous videos of this negative nature on platforms like Facebook and YouTube. The audio data of these videos were then converted into text to build the dataset, and we applied some classifier models to our dataset. In this paper, we utilized a transfer learning Bidirectional Encoder Representations from Transformers (BERT) model that gives state-of-The-Art outcomes. More precisely, we fine-Tuned our model based on transfer learning to evaluate BERT's capacity to capture hostile contexts inside YouTube videos. We examined Fine-Tuning BERT; with different learning rates and listed the outcomes. We train the BERT by freezing all the hyperparameters but with various random seeds to evaluate our suggested Fine-Tuning approach. Compared to previous methodologies that used our dataset, our proposition fared better in terms of accuracy and execution time.
AB - Hate speech is a public statement that demonstrates resentment or provokes disturbance towards a person or group often based upon race, age, religion, sexual orientation, minority group, psychical disability, political belief, etc. Such an act is a leading cause of mental degeneration in individuals observed throughout the world. We have witnessed an upsurge in the spreading of hateful speech through videos in recent times due to increased social media usage. Researchers are working on this issue because it has become more frequent on several social media platforms, and it leads to low self-esteem and has significant negative impacts on human life. In this work, we focus on collecting data from such videos as nowadays people are sharing numerous videos of this negative nature on platforms like Facebook and YouTube. The audio data of these videos were then converted into text to build the dataset, and we applied some classifier models to our dataset. In this paper, we utilized a transfer learning Bidirectional Encoder Representations from Transformers (BERT) model that gives state-of-The-Art outcomes. More precisely, we fine-Tuned our model based on transfer learning to evaluate BERT's capacity to capture hostile contexts inside YouTube videos. We examined Fine-Tuning BERT; with different learning rates and listed the outcomes. We train the BERT by freezing all the hyperparameters but with various random seeds to evaluate our suggested Fine-Tuning approach. Compared to previous methodologies that used our dataset, our proposition fared better in terms of accuracy and execution time.
KW - Hate Speech
KW - Hyperparameter
KW - MP4 to Text; BERT
KW - NLP
KW - Random Seed
UR - http://www.scopus.com/inward/record.url?scp=85164249529&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2023.01.444
DO - 10.1016/j.procs.2023.01.444
M3 - Conference article
AN - SCOPUS:85164249529
SN - 1877-0509
VL - 219
SP - 1527
EP - 1537
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022
Y2 - 9 November 2022 through 11 November 2022
ER -