SafeChat: A tool to shield children's communication from explicit messages

Fahrnberger, Günter; Nayak, Deveeshree; Martha, Venkata Swamy; Ramaswamy, Srini

doi:10.1109/i4cs.2014.6860557

Cited by 20 publications

(7 citation statements)

References 9 publications

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“…(2014) SafeChat focuses on validating a user's identity. It uses the SecureString 2.0 cryptographic system (Fahrnberger, 2014) to encrypt messages before sending them to the recipient. The system uses a blacklist sourced from WordNet and substitutes bad words detected within the message with safer alternatives.…”

Section: Lexicon-based Approachesmentioning

confidence: 99%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

154

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Abstract-Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field.

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Section: Lexicon-based Approachesmentioning

confidence: 99%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

154

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“…There are many content filtering software systems available in the market including BullyBlocker [20], SafeChat [21], Facebook WatchDog [22], and Rethink [23]. These software systems can be used by parents to monitor the social media activity of children and thus can help detect and prevent cyberbullying.…”

Section: Content Filtering Softwarementioning

confidence: 99%

Multilingual Cyberbullying Detection System

Pawar

Raje

2019

2019 IEEE International Conference on Electro Information Technology (EIT)

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“…According to the authors, their rule-based approach was able to improve the results obtained with SVM and Naïve Bayes classifiers. Additionally, Fahrnberger et al [2014] aimed at not only detecting the aggressive content in chat rooms, but also to replace it with alternatives suitable for minors extracted from WordNet 3 .…”

Section: Related Workmentioning

confidence: 99%

An experimental study on feature engineering and learning approaches for aggression detection in social media

Tommasel¹,

Rodríguez²,

Godoy³

2019

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With the widespread of modern technologies and social media networks, a new form of bullying occurring anytime and anywhere has emerged. This new phenomenon, known as cyberaggression or cyberbullying, refers to aggressive and intentional acts aiming at repeatedly causing harm to other person involving rude, insulting, offensive, teasing or demoralising comments through online social media. As these aggressions represent a threatening experience to Internet users, especially kids and teens who are still shaping their identities, social relations and well-being, it is crucial to understand how cyberbullying occurs to prevent it from escalating. Considering the massive information on the Web, the developing of intelligent techniques for automatically detecting harmful content is gaining importance, allowing the monitoring of large-scale social media and the early detection of unwanted and aggressive situations. Even though several approaches have been developed over the last few years based both on traditional and deep learning techniques, several concerns arise over the duplication of research and the difficulty of comparing results. Moreover, there is no agreement regarding neither which type of technique is better suited for the task, nor the type of features in which learning should be based. The goal of this work is to shed some light on the effects of learning paradigms and feature engineering approaches for detecting aggressions in social media texts. In this context, this work provides an evaluation of diverse traditional and deep learning techniques based on diverse sets of features, across multiple social media sites.

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SafeChat: A tool to shield children's communication from explicit messages

Cited by 20 publications

References 9 publications

Approaches to Automated Detection of Cyberbullying: A Survey

Approaches to Automated Detection of Cyberbullying: A Survey

Multilingual Cyberbullying Detection System

An experimental study on feature engineering and learning approaches for aggression detection in social media

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