Sentiment Analysis for Effective Detection of Cyber Bullying

Nahar, Vinita; Unankard, Sayan; Li, Xue; Pang, Chaoyi

doi:10.1007/978-3-642-29253-8_75

Cited by 72 publications

(46 citation statements)

References 4 publications

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“…Typically, this involved the use of emotive keywords to perform sentiment analysis on the corpus and then using the discovered sentiment as an input to the detection process. With the exception of Nahar et al (2012), who used Probabilistic Latent Semantic Analysis (PLSA) to extract sentiment features from labelled bullying posts, all the studies in this group used a lexicon of emotive words to detect the polarity (negative, positive, or neutral) of sentiments expressed within the documents. The emotive words are often based on sources such as WordNet and its variations.…”

Section: Sentiment-based Featuresmentioning

confidence: 99%

“…Serra and Venter (2011) is the earliest study in our sample using network-based features; they used total time present online using a mobile phone as a feature in their detection method. Nahar et al (2012), Huang et al (2014), and NaliniPriya and Asswini (2015) used ego networks as features to improve detection. NaliniPriya and Asswini (2015) used the ego network to compute temporal changes in the relationships between users, and uses the detected changes within the detection process.…”

Section: Network-based Featuresmentioning

confidence: 99%

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Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

161

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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: Sentiment-based Featuresmentioning

confidence: 99%

Section: Network-based Featuresmentioning

confidence: 99%

Approaches to Automated Detection of Cyberbullying: A Survey

Salawu

Lumsden

2020

IEEE Trans. Affective Comput.

161

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show abstract

“…The work incorporated topic models such as PLSA [10] and LDA [3], features that are generated under pre-defined topics, and then only features under cyberbullying-like topics are selected [18,19]. Further, in order to identify predators and victims, users involved in cyberbullying messages are ranked as the most influential predators and the most offended victims through a graph model [19]. Dinakar et al broke down the detection of cyberbullying in to the sensitive-topics detection such as, sexuality, race, intelligence, and profanity.…”

Section: Cyberbullying Detectionmentioning

confidence: 99%

Detecting cyberbullying in social networks using multi-agent system

Nahar

Zhang

et al. 2014

Web Intelligence and Agent Systems: An International Journal

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State-of-the-art studies on cyberbullying detection, using text classification, predominantly take it for granted that streaming text can be completely labelled. However, the rapid growth of unlabelled data generated in real time from online content renders this virtually impossible. In this paper, we propose a session-based framework for automatic detection of cyberbullying within the large volume of unlabelled streaming text. Given that the streaming data from Social Networks arrives in large volume at the server system, we incorporate an ensemble of one-class classifiers in the session-based framework. System uses Multi-Agent distributed environment to process streaming data from multiple social network sources. The proposed strategy tackles real world situations, where only a few positive instances of cyberbullying are available for initial training. Our main contribution in this paper is to automatically detect cyberbullying in real world situations, where labelled data is not readily available. Initial results indicate the suggested approach is reasonably effective for detecting cyberbullying automatically on social networks. The experiments indicate that the ensemble learner outperforms the single window and fixed window approaches, while the learning process is based on positive and unlabelled data only, no negative data is available for training.

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“…Nahar et al built a cyberbullying network graph with the users who had been previously labeled as cyber bullies and victims, then used a ranking method to identify the most active cyber bullies and victims [32,33]). Hosseinmardi et al [34,35] collected data from Instagram with snowball sampling method and complemented textual information (LIWC2015) with human-labeled image content tags for an enhanced cyberbullying detection model.…”

Section: Related Workmentioning

confidence: 99%

On cyberbullying incidents and underlying online social relationships

2018

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Cyberbullying is an important social challenge that takes place over a technical substrate. Thus, it has attracted research interest across both computational and social science research communities. While the social science studies conducted via careful participant selection have shown the effect of personality, social relationships, and psychological factors on cyberbullying, they are often limited in scale due to manual survey or ethnographic study components. Computational approaches on the other hand have defined multiple automated approaches for detecting cyberbullying at scale, and have largely focused only on the textual content of the messages exchanged. There are no existing efforts aimed at testing, validating, and potentially refining the findings from traditional bullying literature as obtained via surveys and ethnographic studies at scale over online environments. By analyzing the social relationship graph between users in an online social network and deriving features such as out-degree centrality and the number of common friends, we find that multiple social characteristics are statistically different between the cyberbullying and nonbullying groups, thus supporting many, but not all, of the results found in previous survey-based bullying studies. The results pave way for better understanding of the cyberbullying phenomena at scale.

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Sentiment Analysis for Effective Detection of Cyber Bullying

Cited by 72 publications

References 4 publications

Approaches to Automated Detection of Cyberbullying: A Survey

Approaches to Automated Detection of Cyberbullying: A Survey

Detecting cyberbullying in social networks using multi-agent system

On cyberbullying incidents and underlying online social relationships

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