Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit; Electronics; Vol. 10, iss. 11

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit; Electronics; Vol. 10, iss. 11

Λεπτομέρειες βιβλιογραφικής εγγραφής
Parent link:Electronics
Vol. 10, iss. 11.— 2021.— [1332, 18 p.]
Συγγραφή απο Οργανισμό/Αρχή: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники
Άλλοι συγγραφείς: Fan Hong, Du Wu, Dahou A. Abdelghani, Ewees A. A. Ahmed, Yousri D. Dalia, Mokhamed Elsaed A. M. Akhmed Mokhamed, Elsheikh A. H. Ammar, Abualigah L. Lait, Al-qaness Mohammed A. A.
Περίληψη:Title screen
Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.
Γλώσσα:Αγγλικά
Έκδοση: 2021
Θέματα:
труды учёных ТПУ
электронный ресурс
toxic
social media
brexit
Twitter
BERT
sentiment analysis
социальные медиа
социальные сети
глубокое обучение
Διαθέσιμο Online:https://doi.org/10.3390/electronics10111332
Μορφή: Ηλεκτρονική πηγή Κεφάλαιο βιβλίου
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665172

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