Abusive Bangla comments detection on Facebook using transformer-based deep learning models

Aurpa, Tanjim Taharat; Sadik, Rifat; Ahmed, Md Shoaib

doi:10.1007/s13278-021-00852-x

Cited by 62 publications

(21 citation statements)

References 31 publications

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“…To train embeddings, a recent research in cyberbullying detection has mostly focused on employing recurrent neural networks (RNN) and pre-trained models using plain tokens [14]. Other studies offer alternative solutions [15], by using psychological variables such as personalities, attitudes, and emotions in order to improve automated cyberbullying-related texts classification. However, these results were obtained by means of the rudimentary models.…”

Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

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Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

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Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…(a) Visual-based: the types of fake news are described in the material using a graphical depiction of video or photo shopped pictures or a mix of both [10] (b) User-based: using this method, the intended audience could be attracted by establishing fictitious accounts that reflect certain demographics such as gender, age, and culture [16] (c) Post-based: social media sites like Facebook posts with video or picture captions, memes, tweets, and so on are the common places for this kind of fake news to emerge [17] (d) Network-based: there are some people of an organization who are linked to this type of fake news, where this concept is primarily used to groups of linked persons on LinkedIn and friends-of-friends on Facebook [18] (e) Knowledge-based: these new articles will be created using articles that provide plausible explanations or 4 Wireless Communications and Mobile Computing scientific knowledge about an unsolved problem to disseminate false info [19] (f) Style-based: false news may be produced by anybody with the ability to write in a variety of styles, but this style-based news was only concerned with how the false info was presented to end consumers [20] Some ways for determining whether or not a piece of news is false are shown in the Figure 1 or the techniques described below.…”

Section: Types Of Fake Newsmentioning

confidence: 99%

Analyzing Machine Learning Enabled Fake News Detection Techniques for Diversified Datasets

Mishra¹,

Shukla

Agarwal

2022

Wireless Communications and Mobile Computing

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Fake news, or fabric which appeared to be untrue with point of deceiving the open, has developed in ubiquity in current a long time. Spreading this kind of data undermines societal cohesiveness and well by cultivating political division and doubt in government. Since of the sheer volume of news being disseminated through social media, human confirmation has ended up incomprehensible, driving to the improvement and arrangement of robotized strategies for the recognizable proof of wrong news. Fake news publishers use a variety of stylistic techniques to boost the popularity of their works, one of which is to arouse the readers’ emotions. Due to this, text analytics’ sentiment analysis, which determines the polarity and intensity of feelings conveyed in a text, is now being utilized in false news detection methods, as either the system’s foundation or as a supplementary component. This assessment analyzes the full explanation of false news identification. The study also emphasizes characteristics, features, taxonomy, different sorts of data in the news, categories of false news, and detection approaches for spotting fake news. This research recognized fake news using the probabilistic latent semantic analysis approach. In particular, the research describes the fundamental theory of the related work to provide a deep comparative analysis of various literature works that has contributed to this topic. Besides this, a comparison of different machine learning and deep learning techniques is done to assess the performance for fake news detection. For this purpose, three datasets have been used.

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“…In [24] , authors have introduced Bangla BERT a monolingual model. Another use of BERT can be seen in [8] , where they use this architecture for classifying abusive comments.…”

Section: Related Workmentioning

confidence: 99%