Methods for detection of cyberbullying: A survey

Sugandhi, Rekha; Pande, Anurag; Chawla, Siddhant; Agrawal, Abhishek; Bhagat, Husen

doi:10.1109/isda.2015.7489220

Cited by 22 publications

(11 citation statements)

References 4 publications

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“…'I think Tom W. is a tw*t') These two varieties can be called 'tagged individual-directed' and 'referenced individual-directed' respectively. Most research in this area falls under cyberbullying (Sugandhi, Pande, Chawla, Agrawal, & Bhagat, 2016) although there are notable exceptions (Wulczyn, Thain, & Dixon, 2017).…”

Section: Categorizing Abusive Contentmentioning

confidence: 99%

Challenges and frontiers in abusive content detection

Vidgen¹,

Harris²,

Nguyen³

et al. 2019

Proceedings of the Third Workshop on Abusive Language Online

169

210

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Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spanning technical, social and ethical dimensions. These issues constrain the performance, efficiency and generalizability of abusive content detection systems. In this article we delineate and clarify the main challenges and frontiers in the field, critically evaluate their implications and discuss solutions. We also highlight ways in which social scientific insights can advance research.

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Section: Categorizing Abusive Contentmentioning

confidence: 99%

Challenges and frontiers in abusive content detection

Vidgen¹,

Harris²,

Nguyen³

et al. 2019

Proceedings of the Third Workshop on Abusive Language Online

169

210

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show abstract

“…But only a few try to enhance software to prohibit cyberbullying. Robust and selective representation of learning of text messages is crucial for consistent detection system [11]. Machine Learning representation and authentication makes automatic revelation of bullied messages in online media possible and ensures building a relevant and clear social media environment.…”

Section: Conclusion and Future Scopementioning

confidence: 99%

Cyberbullying Revelation in Twitter Data Using Naïve Bayes Classifier Algorithm

Kovoor

Nandakumar²,

Sreeja³

2018

ijarcs

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Cyberbullying can be visualized as a potential issue affecting children and all categories of people. One demanding concern is effective representation for learning of content messages. The proposed system deals with cyberbullying revelation in email application using Naive Bayes Classifier Algorithm. The Classification Algorithm is a baseline method for content classification; the method of analyzing documents as relating to one classification or the other with word prevalence as features. The technique deals with the identification and filtering of spam words. The denoised messages are classified with the help of Naive Bayes Classifier Algorithm. The messages are processed under feature set extraction method. The feature probabilities are found out using Naive Bayes Classifier Algorithm .The efficiency factor is compared among the two algorithms, Naive Bayes Classifier Algorithm and Support Vector Machine and a graph is plotted. Comparison on the basis of precision factor is also done with the fact that the probabilities for each feature set are calculated independently from the twitter dataset and can evaluate the performance by predicting the output variable.

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“…The hyper∖parameter settings of neural networks may depended on the dataset being used. We used rectified linear units, filter windows h of (3,4,5) and (7,8) on the two layers with 256 feature maps each. We used the chunk of size 3 for local max-pooling, and set the dropout rate (p) of 0.5 and l 2 constraint of 3.…”

Section: Hyperparameters and Trainingmentioning

confidence: 99%

“…Cyberbullying events are hard to recognize. The major problem in cyberbullying detection is the lack of identifiable parameters and clearly quantifiable standards and definitions that can classify posts as bullying 4 . As people spend increasingly more time on social networks, cyberbullying has become a social problem that needs to be solved, and it is very necessary to detect the occurrence of cyberbullying through an automated method.…”

Section: Introductionmentioning

confidence: 99%

Cyberbullying detection in social media text based on character‐level convolutional neural network with shortcuts

Zhang

et al. 2020

Concurrency and Computation

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As people spend increasingly more time on social networks, cyberbullying has become a social problem that needs to be solved by machine learning methods. Our research focuses on textual cyberbullying detection because text is the most common form of social media. However, the content information in social media is short, noisy, and unstructured with incorrect spellings and symbols, and this impacts the performance of some traditional machine learning methods based on vocabulary knowledge. For this reason, we propose a Char-CNNS (Character-level Convolutional Neural Network with Shortcuts) model to identify whether the text in social media contains cyberbullying. We use characters as the smallest unit of learning, enabling the model to overcome spelling errors and intentional obfuscation in real-world corpora. Shortcuts are utilized to stitch different levels of features to learn more granular bullying signals, and a focal loss function is adopted to overcome the class imbalance problem. We also provide a new Chinese Weibo comment dataset specifically for cyberbullying detection, and experiments are performed on both the Chinese Weibo dataset and the English Tweet dataset. The experimental results show that our approach is competitive with state-of-the-art techniques on cyberbullying detection task.

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Methods for detection of cyberbullying: A survey

Cited by 22 publications

References 4 publications

Challenges and frontiers in abusive content detection

Challenges and frontiers in abusive content detection

Cyberbullying Revelation in Twitter Data Using Naïve Bayes Classifier Algorithm

Cyberbullying detection in social media text based on character‐level convolutional neural network with shortcuts

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