Facebook Watchdog: A Research Agenda for Detecting Online Grooming and Bullying Activities

Rybnicek, Marlies; Poisel, Rainer; Tjoa, Simon

doi:10.1109/smc.2013.487

Cited by 27 publications

(16 citation statements)

References 17 publications

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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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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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“…The accuracy of predicting the level of cyberbullying attack using classification methods is studied in [9]. A Facebook watchdog application is developed in [10] that make use of image analysis, social media analytics, and text mining techniques to detect cyberbullying activity.…”

Section: Introductionmentioning

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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“…They believe adults are unable to know everything about the language adolescents use. Indeed, writing styles have changed revolutionary and especially teenager language is developing at a fast pace (Rybnicek, Poisel, & Tjoa, 2013). Consequently, the adolescents generally believed they would be able to recognize an adult based on his language use, even when using youth language characteristics.…”

Section: Main Findings and Practical Implicationsmentioning

confidence: 99%

Strategies and cues adolescents use to assess the age of an online stranger

Groenestein

Baas

Deursen

et al. 2017

Information, Communication & Society

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A common risk among adolescents is sexual solicitation, in which an adolescent is asked to provide sexual information, engage in sexual talk, or in sexual activities. Although scholars increasingly address this topic from an intrapersonal perspective, there is little attention to factors of language use and message content. In two focus group studies, we investigated whether adolescent girls consider themselves capable of assessing whether an online stranger is an adult or a peer, the extent to which adolescent girls actually succeed in making this assessment, the strategies they apply to do so, and the content-and language-related cues focused on. Our findings suggest that most of the adolescent girls are confident in their ability to assess whether the stranger is a peer or an adult with possible sexual intentions. However, we also found that only 43% were able to correctly make this assessment. Most of the adolescents seem to apply the passive strategy of uncertainty reduction: They scan the profile page of the stranger, check contact information, and the profile picture. They may choose to apply the interactive strategy to find out more about the online stranger. Important content-related cues that alarm adolescent girls are: ignoring personal questions, showing an exaggerated amount of interest, acting as a friend, and being sexually oriented. Language cues mentioned related to word usage, abbreviations, sentence length, and tone.ARTICLE HISTORY

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Facebook Watchdog: A Research Agenda for Detecting Online Grooming and Bullying Activities

Cited by 27 publications

References 17 publications

Multilingual Cyberbullying Detection System

Multilingual Cyberbullying Detection System

Cyberbullying Revelation in Twitter Data Using Naïve Bayes Classifier Algorithm

Strategies and cues adolescents use to assess the age of an online stranger

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