Cyberbullying Detection Using Deep Learning and Word Embeddings

Al-Hashedi, Mohammed; Soon, Lay-Ki; Goh, Hui-Ngo

doi:10.1145/3372422.3373592

Cited by 28 publications

(14 citation statements)

References 12 publications

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“…For both unsupervised and supervised ML, we do not consider Deep Learning. While the use of Deep Learning was proposed in recent literature on ML for social good ( Al-Hashedi et al, 2019 , Khatua et al, 2018 , Sawhney et al, 2018 ), the problem considered in this paper is characterized by a limited amount of data. Therefore, considering that Deep Learning provides accurate models when a vast amount of training data is available, we do not rely on this ML technique.…”

Section: Methodsmentioning

confidence: 99%

Using artificial intelligence to overcome over-indebtedness and fight poverty

Ferreira

Pinto

Herter

et al. 2021

Journal of Business Research

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This research examines how artificial intelligence may contribute to better understanding and to overcome over-indebtedness in contexts of high poverty risk. This research uses Automated Machine Learning (AutoML) in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning using Self-Organizing Maps generated three over-indebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). Second, supervised machine learning with exhaustive grid search hyperparameters (32,730 predictive models) suggests that Nu-Support Vector Machine had the best accuracy in predicting families’ over-indebtedness risk factors (89.5%). By proposing an AutoML approach on over-indebtedness, our research adds both theoretically and methodologically to current models of scarcity with important practical implications for business research and society. Our findings also contribute to novel ways to identify and characterize poverty risk in earlier stages, allowing customized interventions for different profiles of over-indebtedness.

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Section: Methodsmentioning

confidence: 99%

Using artificial intelligence to overcome over-indebtedness and fight poverty

Ferreira

Pinto

Herter

et al. 2021

Journal of Business Research

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“…This dataset was first annotated by three human annotators for the presence of harassment and later for the two cyberbullying roles -bully and victim. More recent studies by Dadvar and Eckert (2018), Iwendi et al (2020), Gada, Damania, and Sankhe (2021), Al-Hashedi, Soon, andGoh (2019), Al-Garadi, Varathan, andRavana (2016), Paul and Saha (2020), and others have demonstrated accurate and precise binary cyberbullying classifications by leveraging above mentioned datasets and recent DL approaches to NLP such as Recurrent Neural Networks (Medsker and Jain, 2001), Long-term Short-term Memory (Sundermeyer, Schlüter, and Ney, 2012), Gated Recurrent Units (GRUs) (Cho et al, 2014) and Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al, 2018). Though such studies have reported high accuracy and precision in their classification predictions, they are unable to detect the key elements of cyberbullying -as understood from a social science perspective.…”

Section: Binary Cyberbullying Detection Researchmentioning

confidence: 96%

Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents

Verma

Popović²,

Poulis³

et al. 2022

Nat. Lang. Eng.

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Cyberbullying is the wilful and repeated infliction of harm on an individual using the Internet and digital technologies. Similar to face-to-face bullying, cyberbullying can be captured formally using the Routine Activities Model (RAM) whereby the potential victim and bully are brought into proximity of one another via the interaction on online social networking (OSN) platforms. Although the impact of the COVID-19 (SARS-CoV-2) restrictions on the online presence of minors has yet to be fully grasped, studies have reported that 44% of pre-adolescents have encountered more cyberbullying incidents during the COVID-19 lockdown. Transparency reports shared by OSN companies indicate an increased take-downs of cyberbullying-related comments, posts or content by artificially intelligen moderation tools. However, in order to efficiently and effectively detect or identify whether a social media post or comment qualifies as cyberbullying, there are a number factors based on the RAM, which must be taken into account, which includes the identification of cyberbullying roles and forms. This demands the acquisition of large amounts of fine-grained annotated data which is costly and ethically challenging to produce. In addition where fine-grained datasets do exist they may be unavailable in the target language. Manual translation is costly and expensive, however, state-of-the-art neural machine translation offers a workaround. This study presents a first of its kind experiment in leveraging machine translation to automatically translate a unique pre-adolescent cyberbullying gold standard dataset in Italian with fine-grained annotations into English for training and testing a native binary classifier for pre-adolescent cyberbullying. In addition to contributing high-quality English reference translation of the source gold standard, our experiments indicate that the performance of our target binary classifier when trained on machine-translated English output is on par with the source (Italian) classifier.

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“…Deep learning algorithms, coupled with word embeddings in detecting cyberbullying texts, are the topic of much research work [17]. In a matrix of choices, three deep learning algorithms, namely GRU, LSTM, and BiLSTM, in conjunction with word embeddings models, including word2vec, GloVe, Reddit, and ELMO models, are used to examine the effectiveness and accuracy of a possible configuration for cyberbullying detection.…”

Section: Literature Review and Backgroundmentioning

confidence: 99%

Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection

Yazdizadeh¹,

Shi²

2022

Data Science and Machine Learning

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Communication using modern internet technologies has revolutionized the ways humans exchange information.. Despite the numerous advantages offered by such technology, its applicability is still limited due to problems stemming from personal attacks and pseudoattacks. On social media platforms, these toxic contents may take the form of texts (e.g., online chats, emails), speech, and even images and movie clips. Because the cyberbullying of an individual via the use of such toxic digital content may have severe consequences, it is essential to design and implement, among others, various techniques to automatically detect, using machine learning approaches, cyberbullying on social media. It is important to use word embedding techniques to represent words for text analysis, typically in the form of a real-valued vector that encodes the meaning of words. The extracted embeddings are used to decide if a digital input contains cyberbullying contents. Supplying strong word representations to classification methods is a key facet of such detection approaches. In this paper, we evaluate the ELMo word embedding against three other word embeddings, namely, TF-IDF, Word2Vec, and BERT, using three basic machine learning models and four deep learning models. The results show that the ELMo word embeddings have the best results when combined with neural network-based machine learning models.

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Cyberbullying Detection Using Deep Learning and Word Embeddings

Cited by 28 publications

References 12 publications

Using artificial intelligence to overcome over-indebtedness and fight poverty

Using artificial intelligence to overcome over-indebtedness and fight poverty

Leveraging machine translation for cross-lingual fine-grained cyberbullying classification amongst pre-adolescents

Performance Evaluation for the use of ELMo Word Embedding in Cyberbullying Detection

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