Session-based Cyberbullying Detection in Social Media: A Survey

Yi, Peiling; Zubiaga, Arkaitz

doi:10.48550/arxiv.2207.10639

Cited by 3 publications

(4 citation statements)

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“…Despite the substantial body of research in cyberbullying detection [29,30], research into session-based detection has been more scarce [45], which we discuss next. We then follow with a discussion of work tackling long texts with transformers.…”

Section: Related Workmentioning

confidence: 99%

“…Bullying is defned as the repeated, deliberate aggressive behaviour by a group or individual towards a more vulnerable person [24]. In the literature, there are two common traits of cyberbullying that are consistently referred to [45]: (1) repeatedly harming someone either physically or emotionally, and (2) a power imbalance between the parties. These are in turn used as the key criteria for identifying cyberbullying behaviour and to develop cyberbullying detection models [8,9,16,21,44].…”

Section: Introductionmentioning

confidence: 99%

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Learning like human annotators: Cyberbullying detection in lengthy social media sessions

Zubiaga

2023

Proceedings of the ACM Web Conference 2023

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The inherent characteristic of cyberbullying of being a recurrent attitude calls for the investigation of the problem by looking at social media sessions as a whole, beyond just isolated social media posts. However, the lengthy nature of social media sessions challenges the applicability and performance of session-based cyberbullying detection models. This is especially true when one aims to use state-of-the-art Transformer-based pre-trained language models, which only take inputs of a limited length. In this paper, we address this limitation of transformer models by proposing a conceptually intuitive framework called LS-CB, which enables cyberbullying detection from lengthy social media sessions. LS-CB relies on the intuition that we can efectively aggregate the predictions made by transformer models on smaller sliding windows extracted from lengthy social media sessions, leading to an overall improved performance. Our extensive experiments with six transformer models on two session-based datasets show that LS-CB consistently outperforms three types of competitive baselines including state-of-the-art cyberbullying detection models. In addition, we conduct a set of qualitative analyses to validate the hypotheses that cyberbullying incidents can be detected through aggregated analysis of smaller chunks derived from lengthy social media sessions (H1), and that cyberbullying incidents can occur at diferent points of the session (H2), hence positing that frequently used text truncation strategies are suboptimal compared to relying on holistic views of sessions. Our research in turn opens an avenue for fne-grained cyberbullying detection within sessions in future work. CCS CONCEPTS• Artifcial intelligence → Natural language processing; • Humancentered computing → Social media.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning like human annotators: Cyberbullying detection in lengthy social media sessions

Zubiaga

2023

Proceedings of the ACM Web Conference 2023

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“…To date, there has been a substantial body of research in abusive language [26], [27], hate speech [13], [28] and cyberbullying detection [29], [30], but few efforts have gone beyond this detection task to identify the targets of online abuse. In the OffensEval shared task [31], one of the most popular tasks of the SemEval 2019, participants were asked to identify offensive tweets, and their targets.…”

Section: A Targets In Abusive Language Detectionmentioning

confidence: 99%

Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis

Alharthi

Shekhar

et al. 2023

IEEE Access

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Despite a body of research revolving around online abusive language, aiming at different objectives such as detection, diffusion prediction, and mitigation, existing research has seldom looked at factors motivating this behaviour. To further research in this direction, we investigate the motivations behind online abuse by looking at the characteristics of the targets of such abuse, i.e. is the abuse more prominent for specific characteristics of the targets? To enable target-oriented research into online abuse, we introduce the Online Abusive Attacks (OAA) dataset, the first benchmark dataset providing a holistic view of online abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations. Further, we conduct an in-depth statistical analysis of online abuse centred around the targets' characteristics. We identify two types of abusive attacks: those motivated by characteristics of the targets (identity-based attacks) and others (behavioural attacks). We find that online abusive attacks are predominantly motivated by the targets' identities (97%), behavioural attacks accounting for a much smaller proportion (3%). Abuse is also more likely to target users who are popular and have a verified status. Interestingly, an analysis of the user bios shows no clear indication that keywords used in the bios are likely to trigger abuse. Additionally, we also look at the frequency with which perpetrators perform online abusive attacks. Our analysis shows a large number of infrequent perpetrators, with only a few recurrent perpetrators. Findings from our study have important implications for the development of abusive language detection models that incorporate an awareness of the targets to improve their potential for prediction.

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“…The authors identify social platforms where cyberbullying occurs, such as X, Facebook, and email. The absence of age restrictions on many social media networks is flagged as a harmful policy with a chilling effect on youths [5]. The misuse of these platforms not only contributes to cyberbullying but also fosters other antisocial acts, rendering them potentially unsafe even for adults.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Ensemble Model for the Detection of Cyberbullying

Alqahtani,

Ilyas

2024

IJAIA

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The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms. Motivated by this necessity, we present this paper to contribute to developing an automated system for detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to previous experiments on the same dataset. We employed the stacking ensemble machine learning method, utilizing four various feature extraction techniques to optimize performance within the stacking ensemble learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we achieved superior results compared to traditional machine learning classifier models. The stacking classifier achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.

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Session-based Cyberbullying Detection in Social Media: A Survey

Cited by 3 publications

References 0 publications

Learning like human annotators: Cyberbullying detection in lengthy social media sessions

Learning like human annotators: Cyberbullying detection in lengthy social media sessions

Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis

A Machine Learning Ensemble Model for the Detection of Cyberbullying

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