Türkçe Sosyal Medya Yorumlarındaki Siber Zorbalığın Derin Öğrenme ile Tespiti

Year 2021, Issue: 31, 77 - 84, 31.12.2021

Gözde Nergiz , Erdinç Avaroğlu

https://doi.org/10.31590/ejosat.987259

Cited By: 1

Abstract

Siber zorbalık, internet teknolojisinin gelişimi ve sosyal ağlara erişim kolaylığı ile birlikte büyük bir problem haline dönüşmüştür. Bir kişi veya grup tarafından gerçekleştirilen siber zorbalık, başkalarını taciz etmek için bilgi ve iletişim teknolojilerinin kullanılması anlamına gelir. İntihar ile sonuçlanan siber zorbalık vakaları siber zorbalık tespitini önemli hale getirmiştir. Bu çalışmada günümüzde yaygın olarak kullanılan Twitter, Instagram ve Youtube sosyal ağlarından toplanan Türkçe yorumlar üzerinde siber zorbalık tespiti yapılmıştır. Derin öğrenme tabanlı kelime gömme modelleri kullanılarak sınıflandırma modelleri oluşturulup başarı oranları karşılaştırılmıştır. En yüksek başarı oranı elde eden Fasttext modeli ile LSTM sinir ağı kullanılarak sosyal medya yorumlarının sınıflandırılması sağlanmıştır.

Keywords

Sosyal Ağlar, Derin Öğrenme, Fasttext, LSTM, Siber Zorbalık

Supporting Institution

Mersin Üniversitesi

Project Number

2019-1-TP2-3339

Thanks

Bu çalışma Mersin Üniversitesi Bilimsel Araştırma Projeleri Birimi tarafından desteklenmiştir.

Detection of Cyberbullying in Turkish Social Media Comments with Deep Learning

Abstract

Cyberbullying has become a big problem with the development of internet technology and ease of access to social networks. Cyberbullying is by a person or group refers to the use of information and communication technologies to harass others. Cyberbullying cases resulting in suicide have made the detection of cyberbullying important. In this study, cyberbullying was detected on Turkish comments collected from Twitter, Instagram and Youtube social networks, which are widely used today. Classification models were created using deep learning-based word embedding models and success rates were compared. The classification of social media comments was achieved by using the Fasttext model, which achieved the highest success rate, and the LSTM neural network.

Keywords

Social Networks, Deep Learning, Fasttext, LSTM, Cyberbullying

Project Number

2019-1-TP2-3339

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