A Study of Contact Network Generation for Cyber-bullying Detection

Li, Mingmei; Tagami, Atsushi

doi:10.1109/waina.2014.70

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…To eliminate the effects of language on cyberbully detection when comparing the reviewed studies, we excluded papers using non-English corpora; thus a further 7 papers were excluded. These included papers such as Ptazynski et al (2010a;, Honjo et al (2011), Nitta et al (2013), Li and Tagami (2014), Margono et al (2014) and Van Hee et al (2015) which were removed as they used non-English corpora.…”

Section: Data Search and Selectionmentioning

confidence: 99%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

153

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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: Data Search and Selectionmentioning

confidence: 99%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

153

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“…They also tried to find untrustworthy profile of user on the basis of age, time spent online and types of online access and then classify the risk based on these profiles and suggest safety as a result. Li et al (2014), created the contact network, by forming the undirected graph from the data and add missing links based on the hop count and similarity method, to identify the friendship relation. This study focused on the detection indirect bullying, e.g., isolates the target by disregarding the their messages.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Distinguish Between the Severity of Bullying in Messages in Social Media

Sarna

Bhatia

2016

International Journal of Rough Sets and Data Analysis

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Users on the social media can share positive as well as negative information intentionally and unintentionally in the form of multimedia content without knowing its impact on other user, which threatens the security and privacy of social media. Cyberbullying is one of the risks associated with social media. Cyberbullying is an aggressive act carried out intentionally against the victim by posting harmful material on social media to harm his/her reputation. Aggressive act creates depression, anxiety in users which may lead to diversion of attention and sometimes suicidal actions. In this paper the authors have included a survey on recent algorithms which work on detection of cyberbullying. State-of-the-art studies only focus on the detection of cyberbullying but not on the preventive measures against cyberbullying. In order to tackle this problem, the authors showed how the severity of the bullying in messages helps to find the real culprit.

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“…Previous research in the field of cyberbullying detection has been driven by two paradigms which focus on two different types of analysis: network analysis and content analysis, although many studies combine both types of analyses. The social network analysis paradigm focuses on modelling interactions among users of a social network (Li & Tagami, 2014;Honjo et al, 2011;Hosseinmardi et al, 2014), based in particular on the usage statistics of digital media, such as sender and receiver IDs, account IDs, users' names, content, time and location of communication. Content analysis focuses on detecting cyberbullying instances based on text analytics and the use of Natural Language Processing (NLP) techniques, and, 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.…”

Section: Introductionmentioning

confidence: 99%

A lexical database for public textual cyberbullying detection

Power¹,

Keane²,

Nolan³

et al. 2017

rlfe

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Public textual cyberbullying has become one of the most prevalent issues associated with online safety of young people, particularly on social networks. To address this issue, we argue that the boundaries of what constitutes public textual cyberbullying needs to be first identified and a corresponding linguistically motivated definition needs to be advanced. Thus, we propose a definition of public textual cyberbullying that contains three necessary and sufficient elements: the personal marker, the dysphemistic element and the cyberbullying link between the previous two elements. Subsequently, we argue that one of the cornerstones in the overall process of mitigating the effects of cyberbullying is the design of a cyberbullying lexical database that specifies what linguistic and cyberbullying specific information is relevant to the detection process. In this vein, we propose a novel cyberbullying lexical database based on the definition of public textual cyberbullying. The overall architecture of our cyberbullying lexical database is determined semantically, and, in order to facilitate cyberbullying detection, the lexical entry encapsulates two new semantic dimensions that are derived from our definition: cyberbullying function and cyberbullying referential domain. In addition, the lexical entry encapsulates other semantic and syntactic 1

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A Study of Contact Network Generation for Cyber-bullying Detection

Cited by 11 publications

References 6 publications

Approaches to Automated Detection of Cyberbullying: A Survey

Approaches to Automated Detection of Cyberbullying: A Survey

An Approach to Distinguish Between the Severity of Bullying in Messages in Social Media

A lexical database for public textual cyberbullying detection

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