Comparative Study for Predicting the Severity of Cyberbullying Across Multiple Social Media Platforms

Aggarwal, Akshita; Maurya, Kavita; Chaudhary, Anshima

doi:10.1109/iciccs48265.2020.9121046

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…Other studies have leveraged NLP tools to classify a user’s response to misinformation or to automate its detection [ 52 , 53 ]. Less work has been done within the stigma space, although some work exists that automates the detection of cyberbullying [ 54 ]. Many of these studies rely on similar features that are used in this work, most notably word embeddings [ 50 , 51 , 54 ].…”

Section: Discussionmentioning

confidence: 99%

“…Less work has been done within the stigma space, although some work exists that automates the detection of cyberbullying [ 54 ]. Many of these studies rely on similar features that are used in this work, most notably word embeddings [ 50 , 51 , 54 ]. Although these studies relied on context-independent models, such as Word2Vec or GLOVE, more recent work (especially within the COVID-19 space) has leveraged the bidirectionality of BERT to generate context-dependent embeddings [ 55 , 56 ].…”

Section: Discussionmentioning

confidence: 99%

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Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study

Pollack¹,

Emond²,

O’Malley³

et al. 2022

J Med Internet Res

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Background Reddit is a popular social media platform that has faced scrutiny for inflammatory language against those with obesity, yet there has been no comprehensive analysis of its obesity-related content. Objective We aimed to quantify the presence of 4 types of obesity-related content on Reddit (misinformation, facts, stigma, and positivity) and identify psycholinguistic features that may be enriched within each one. Methods All sentences (N=764,179) containing “obese” or “obesity” from top-level comments (n=689,447) made on non–age-restricted subreddits (ie, smaller communities within Reddit) between 2011 and 2019 that contained one of a series of keywords were evaluated. Four types of common natural language processing features were extracted: bigram term frequency–inverse document frequency, word embeddings derived from Bidirectional Encoder Representations from Transformers, sentiment from the Valence Aware Dictionary for Sentiment Reasoning, and psycholinguistic features from the Linguistic Inquiry and Word Count Program. These features were used to train an Extreme Gradient Boosting machine learning classifier to label each sentence as 1 of the 4 content categories or other. Two-part hurdle models for semicontinuous data (which use logistic regression to assess the odds of a 0 result and linear regression for continuous data) were used to evaluate whether select psycholinguistic features presented differently in misinformation (compared with facts) or stigma (compared with positivity). Results After removing ambiguous sentences, 0.47% (3610/764,179) of the sentences were labeled as misinformation, 1.88% (14,366/764,179) were labeled as stigma, 1.94% (14,799/764,179) were labeled as positivity, and 8.93% (68,276/764,179) were labeled as facts. Each category had markers that distinguished it from other categories within the data as well as an external corpus. For example, misinformation had a higher average percent of negations (β=3.71, 95% CI 3.53-3.90; P<.001) but a lower average number of words >6 letters (β=−1.47, 95% CI −1.85 to −1.10; P<.001) relative to facts. Stigma had a higher proportion of swear words (β=1.83, 95% CI 1.62-2.04; P<.001) but a lower proportion of first-person singular pronouns (β=−5.30, 95% CI −5.44 to −5.16; P<.001) relative to positivity. Conclusions There are distinct psycholinguistic properties between types of obesity-related content on Reddit that can be leveraged to rapidly identify deleterious content with minimal human intervention and provide insights into how the Reddit population perceives patients with obesity. Future work should assess whether these properties are shared across languages and other social media platforms.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study

Pollack¹,

Emond²,

O’Malley³

et al. 2022

J Med Internet Res

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show abstract

“…Recall = True Positive Actual number of instances as True (6) The F1-score is also known as the F-score or F-measure. It is used as a benchmark to calculate the weighted average of precision and recall.…”

Section: F Performance Evaluationmentioning

confidence: 99%

“…Determining the severity of cyberbullying content can assist in avoiding cyberbullying incidents and helping victims feel safe [1], [6].…”

Section: Introductionmentioning

confidence: 99%

Cyberbullying Detection and Severity Determination Model

Obaid,

Guirguis,

Elkaffas

2023

IEEE Access

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Some teenagers actively participate in cyberbullying, which is a pattern of online harassment of others. Many teenagers are unaware of the risks posed by cyberbullying, which can include depression, self-harm, and suicide. Because of the serious harm it can cause to a person's mental health, cyberbullying is an important problem that needs to be addressed. This research aimed to develop a technique to identify the severity of bullying using a deep learning algorithm and fuzzy logic. In this task, Twitter data (47,733 comments) from Kaggle were processed and analyzed to flag cyberbullying comments. The comments embedded by Keras were fed into a long short-term memory network, composed of four layers, for classification. After that, fuzzy logic was applied to determine the severity of the comments. Experimental results suggest that the proposed framework provides a suitable solution to detect bulling with values of 93.67%, 93.64%, 93.62% achieved for the accuracy, F1-score, and recall, respectively.INDEX TERMS Deep learning algorithm, severity of bullying, LSTM.

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“…Cyberbullying can occur over social media, email, mobile phone SMS, encrypted messaging apps such as WhatsApp or Kik, and chat platforms such as Discord (Aggarwal et al , 2020; Caliskan Pala et al , 2021; Ronis and Slaunwhite, 2019). A study in Israel of over 5,000 youth aged 9–14 found that 97% of participants used WhatsApp and that this was the most common forum for cyberbullying (Aizenkot, 2020).…”

Section: Digital Harmmentioning

confidence: 99%

Digital displacement of youth offending: scoping and understanding the issue

McCord

Birch

Bizo

2022

JCRPP

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Purpose Global evidence suggests that youth offending has reduced; however, this study aims to suggest a more complex picture, with youth crime potentially being displaced to the digital space. Historically, young people and crime have been synonymous with public spaces and being visible. A shift or expansion to online offending requires revision of how the justice and educational systems respond to youth offending. Design/methodology/approach A systematic literature review explored keywords related to age, digital offence or harm and criminal or harmful nature, using a search, appraisal, synthesis and analysis framework. Findings Three emergent areas of digital youth crime are discussed: digitally assisted crime, digitally dependent crime and digital harm. Practical implications The shift in youth offending requires response adjustment from prevention to detection. Opportunities may exist to disrupt or redirect youth before they offend. Further data specific to digital offending is needed. These findings seek to provide a possible direction for future research. Originality/value The concept of digital displacement of youth offending is progressively emerging. This paper examines types of offending categorised into three areas of interest.

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Comparative Study for Predicting the Severity of Cyberbullying Across Multiple Social Media Platforms

Cited by 10 publications

References 9 publications

Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study

Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study

Cyberbullying Detection and Severity Determination Model

Digital displacement of youth offending: scoping and understanding the issue

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