Identification of cyberbullying on multi‐modal social media posts using genetic algorithm

Kumari, Kirti; Singh, Jyoti

doi:10.1002/ett.3907

Cited by 35 publications

(15 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impact of such detection tools could have been very beneficial in terms of enforcing social awareness and addressing effective ethical issues [1,10]. Furthermore, it would be interesting to investigate how our annotation protocol can be used to collect a multi-modal hate speech dataset [28], as well as how our collected balanced dataset can enhance tasks like multi-lingual hate speech detection [29].…”

Section: Discussionmentioning

confidence: 99%

ETHOS: a multi-label hate speech detection dataset

Mollas

Chrysopoulou

Karlos

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

Online hate speech is a recent problem in our society that is rising at a steady pace by leveraging the vulnerabilities of the corresponding regimes that characterise most social media platforms. This phenomenon is primarily fostered by offensive comments, either during user interaction or in the form of a posted multimedia context. Nowadays, giant corporations own platforms where millions of users log in every day, and protection from exposure to similar phenomena appears to be necessary to comply with the corresponding legislation and maintain a high level of service quality. A robust and reliable system for detecting and preventing the uploading of relevant content will have a significant impact on our digitally interconnected society. Several aspects of our daily lives are undeniably linked to our social profiles, making us vulnerable to abusive behaviours. As a result, the lack of accurate hate speech detection mechanisms would severely degrade the overall user experience, although its erroneous operation would pose many ethical concerns. In this paper, we present ‘ETHOS’ (multi-labEl haTe speecH detectiOn dataSet), a textual dataset with two variants: binary and multi-label, based on YouTube and Reddit comments validated using the Figure-Eight crowdsourcing platform. Furthermore, we present the annotation protocol used to create this dataset: an active sampling procedure for balancing our data in relation to the various aspects defined. Our key assumption is that, even gaining a small amount of labelled data from such a time-consuming process, we can guarantee hate speech occurrences in the examined material.

show abstract

Section: Discussionmentioning

confidence: 99%

ETHOS: a multi-label hate speech detection dataset

Mollas

Chrysopoulou

Karlos

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…The authors investigated the effectiveness of FD utilizing linguistically-backed pre-processing such as stop words filtering, Parts of Speech (POS), Named Entity Recognition (NER), etc., approaches for assessing classification performance and the complexity of the dataset. On the other side, some recent studies presented multi-models to detect CB in 3 various modalities of social data networking, namely visual and info-graphic and textual such as [51][54] [55]. Kumari et al [56] presented DL based model to classify various levels of cyber aggression over networking social media comments in a bilingual.…”

Section: Related Workmentioning

confidence: 99%

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

et al. 2022

View full text Add to dashboard Cite

Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for finetuning the Elman RNN's parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.

show abstract

“…5 shows the weighted F1-score. Table 4 shows experimental results of Kumari et.al, [20]. The authors initially proposed a CNN architecture to identify bullying on multi-modal data.…”

Section: Experiments Methodologymentioning

confidence: 99%

“…The proposed method was able to classify bullying tweets on three real world datasets. K kumari et al [19,20] proposed a single layer convolutional neural network for cyberbullying identification on multi-modal data. The proposed method achieved 74% of recall on bullying class.…”

Section: [2] Related Workmentioning

confidence: 99%

Cyberbullying detection on multi-modal data using pre-trained deep learning architectures

Pericherla

Ilavarasan

2021

ing. Solidar

View full text Add to dashboard Cite

Cyberbullying is a big challenging task in the social media era. The forms of bullying are increasing with the increase of digital technologies. In the past, most of the bullying happened through text messages. Now bullies take advantage of technology, they try bullying others in different forms such as images, videos, and emojis. In this paper, we proposed an approach to identify cyberbullying on both text and image data combinations. We used RoBERTa and Xception deep learning architectures to generate word embeddings from the text data and the image respectively. LightGBM classifier is used to classify bullying and non-bullying tweets. The experiments conducted on 2100 samples of combined data of text and image. The proposed approach efficiently classifies bullying data with F1-score of 80% and outperforms as compared to existing approaches.

show abstract

Identification of cyberbullying on multi‐modal social media posts using genetic algorithm

Cited by 35 publications

References 41 publications

ETHOS: a multi-label hate speech detection dataset

ETHOS: a multi-label hate speech detection dataset

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

Cyberbullying detection on multi-modal data using pre-trained deep learning architectures

Contact Info

Product

Resources

About