Sustainable cyberbullying detection with category-maximized relevance of harmful phrases and double-filtered automatic optimization

Ptaszyński, Michał; Masui, Fumito; Nitta, Taisei; Hatakeyama, Suzuha; Kimura, Yuichi; Rzepka, Rafał; Araki, Kenji

doi:10.1016/j.ijcci.2016.07.002

Cited by 45 publications

(42 citation statements)

References 11 publications

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“…Cyberbullying (CB) is considered as a new or electronic form of traditional bullying [1]. CB is defined as a repetitive, intentional, and aggressive reaction committed by a group or an individual against another group or an individual, which is made by the utilization of Information Communication Technology (ICT) tools such as social media, Internet, and mobile phones [2].…”

Section: Introductionmentioning

confidence: 99%

Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

Yuvaraj¹,

Srihari²,

Dhiman

et al. 2021

Mathematical Problems in Engineering

140

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In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.

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Section: Introductionmentioning

confidence: 99%

Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

Yuvaraj¹,

Srihari²,

Dhiman

et al. 2021

Mathematical Problems in Engineering

140

View full text Add to dashboard Cite

show abstract

“…Secondly, we discarded any smileys and emoticons, since they rarely occurred. However, we recognise that, in other contexts and other datasets, the presence of such emoticons, such as frown, or angry faces, may be indicative of cyberbullying, as shown by Ptaszynski et al (2010) and Ptaszynski et al (2016). In addition, although not encountered in the present datasets, Unicode (2017) allows not only emoticons to be inserted in text, but also other symbols for gestures or animals that may constitute cyberbullying, such as fist-making or monkey face.…”

Section: Discussionmentioning

confidence: 71%

“…From this perspective, the task of cyberbullying detection was previously approached as a classification task (Yin et al 2009) that involves data acquisition and pre-processing, feature extraction, and classification. These techniques were used mostly in targeting explicit textual cyberbullying language and rely on detecting features such as profanities (Yin et al 2009;Dinakar et al 2012;Dadvar et al 2013;Al-garadi et al 2016), bad words (Reynolds et al 2011;Huang et al 2014), foul terms (Nahar et al 2013), bullying terms (Kontostathis et al 2013;Nandhini and Sheeba 2015), pejoratives and obscenities (Chen et al 2012), emotemes and vulgarities (Ptaszynski et al 2010;Ptaszynski et al 2016), curses (Chatzakou et al 2017) or negative words (Van Hee et al 2015).…”

Section: Related Workmentioning

confidence: 99%

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Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

Power¹,

Keane²,

Nolan³

et al. 2018

J. Comp. Assist. Linguist. Res.

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Cyberbullying is a risk associated with the online safety of young people and, in this paper, we address one of its most common implicit forms -negation-based forms. We first describe the role of negation in public textual cyberbullying interaction and identify the cyberbullying constructions that characterise these forms. We then formulate the overall detection mechanism which captures the three necessary and sufficient elements of public textual cyberbullying -the personal marker, the dysphemistic element, and the link between them. Finally, we design rules to detect both overt and covert negation-based forms, and measure their effectiveness using a development dataset, as well as a novel test dataset, across several metrics: accuracy, precision, recall, and the F1-measure. The results indicate that the rules we designed closely resemble the performance of human annotators across all measures.

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“…While cyberbullying detection has recently received increased attention, with much of the research using techniques from text analytics and Natural Language Processing (NLP), the majority of related work relies on the explicit presence of features such as as profanities (Yin et al 2009;Dinakar et al 2012;Dadvar et al 2013;Al-garadi et al 2016), bad words (Reynolds et al 2011;Huang et al 2014), foul terms (Nahar et al 2013), bullying terms (Kontostathis et al 2013;Nandhini and Sheeba 2015), pejoratives and obscenities (Chen et al 2012), emotemes and vulgarities (Ptaszynski et al 2010;Ptaszynski et al 2016), curses (Chatzakou et al 2017) or negative words (Van Hee et al 2015). Even those studies that target implicit forms of cyberbullying (Chen et al 2012;Dinakar et al 2012;Nitta et al 2013;Ptaszynski et al 2010;Ptaszynski et al 2016) do not clearly define the boundaries of what is cyberbullying and the approaches described result in a considerable amount of false positives 1 that contain rude and violent language, despite the fact that the use of this type of language does not constitute cyberbullying on its own. On the other hand, although the focus of previous research in the field of cyberbullying detection has been to reduce the number of false negatives 2 , no other study has investigated the linguistic role of prior discourse in identifying public textual cyberbullying, and the purpose of the present paper is to determine the contribution of antecedent messages in resolving the missing cyberbullying elements that we previously proposed (Power et al 2017;2018): the personal marker, the dysphemistic element, and the cyberbullying link between the personal marker and the dysphemistic element.…”

Section: Introductionmentioning

confidence: 99%

The Role of Previous Discourse in Identifying Public Textual Cyberbullying

Power

Keane

Nolan

et al. 2019

J. Comp. Assist. Linguist. Res.

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<div data-canvas-width="619.2967614992452">In this paper we investigate the contribution of previous discourse in identifying elements that are key to detecting public textual cyberbullying. Based on the analysis of our dataset, we first discuss the missing cyberbullying elements and the grammatical structures representative of discourse-dependent cyberbullying discourse. Then we identify four types of discourse dependent cyberbullying constructions: (1) fully inferable constructions, (2) personal marker and cyberbullying link inferable constructions, (3) dysphemistic element and cyberbullying link inferable constructions, and (4) dysphemistic element inferable constructions. Finally, we formalise a framework to resolve the missing cyberbullying elements that proposes several resolution algorithms. The resolution algorithms target the following discourse dependent message types: (1) polarity answers, (2) contradictory statements, (3) explicit ellipsis, (4) implicit affirmative answers, and (5) statements that use indefinite pronouns as placeholders for the</div><div data-canvas-width="146.57988069516077">dysphemistic element.</div>

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Sustainable cyberbullying detection with category-maximized relevance of harmful phrases and double-filtered automatic optimization

Cited by 45 publications

References 11 publications

Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

Detecting Discourse-Independent Negated Forms of Public Textual Cyberbullying

The Role of Previous Discourse in Identifying Public Textual Cyberbullying

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