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

Bibliografiske detaljer
Parent link:Electronics
Vol. 10, iss. 11.— 2021.— [1332, 18 p.]
Institution som forfatter: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Andre forfattere: 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.
Summary: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.
Sprog:engelsk
Udgivet: 2021
Fag:
труды учёных ТПУ
электронный ресурс
toxic
social media
brexit
Twitter
BERT
sentiment analysis
социальные медиа
социальные сети
глубокое обучение
Online adgang:https://doi.org/10.3390/electronics10111332
Format: Electronisk Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=665172

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