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arxiv: 2407.12154 · v2 · submitted 2024-05-22 · 💻 cs.CY

Cyberbullying Detection: Exploring Datasets, Technologies, and Approaches on Social Media Platforms

Adamu Gaston Philipo , Doreen Sebastian Sarwatt , Jianguo Ding , Mahmoud Daneshmand , Huansheng Ning This is my paper

Pith reviewed 2026-05-24 01:02 UTC · model grok-4.3

classification 💻 cs.CY
keywords cyberbullying detectionsocial media platformssystematic reviewdatasetsdetection approacheschallengesprevention methods
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The pith

A systematic review of cyberbullying detection studies catalogs datasets, technologies, approaches, gaps, and recommendations while proposing future solutions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper conducts a systematic review of research on cyberbullying detection on social media platforms. It surveys existing studies along with their proposed solutions, the datasets employed, the technologies and approaches used, and the challenges and recommendations noted in the literature. The review identifies gaps and suggests directions for addressing them through improved detection, prevention, and prediction methods. A sympathetic reader would care because cyberbullying continues to affect users with direct psychological and physical consequences, and clearer mapping of current work could support more effective responses. The central effort is to organize what has been tried so that subsequent work can target the uncovered gaps directly.

Core claim

The paper presents a comprehensive systematic review of studies conducted on cyberbullying detection. It explores existing studies, proposed solutions, identified gaps, datasets, technologies, approaches, challenges, and recommendations, and then proposes effective solutions to address research gaps in future studies.

What carries the argument

The systematic literature review that catalogs detection techniques, datasets, and open problems across social media platforms.

Load-bearing premise

The chosen studies represent the full scope of relevant research and that the recommended solutions will succeed without separate testing of the review's completeness.

What would settle it

An independent search that locates a substantial set of studies on cyberbullying detection omitted from the review and that reveals different primary gaps or challenges.

read the original abstract

Cyberbullying has been a significant challenge in the digital era world, given the huge number of people, especially adolescents, who use social media platforms to communicate and share information. Some individuals exploit these platforms to embarrass others through direct messages, electronic mail, speech, and public posts. This behavior has direct psychological and physical impacts on victims of bullying. While several studies have been conducted in this field and various solutions proposed to detect, prevent, and monitor cyberbullying instances on social media platforms, the problem continues. Therefore, it is necessary to conduct intensive studies and provide effective solutions to address the situation. These solutions should be based on detection, prevention, and prediction criteria methods. This paper presents a comprehensive systematic review of studies conducted on cyberbullying detection. It explores existing studies, proposed solutions, identified gaps, datasets, technologies, approaches, challenges, and recommendations, and then proposes effective solutions to address research gaps in future studies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper claims to deliver a comprehensive systematic review of cyberbullying detection studies on social media. It surveys existing studies, proposed solutions, datasets, technologies, approaches, challenges, gaps, and recommendations, then proposes solutions for future work.

Significance. A properly documented systematic review on this topic could usefully synthesize a fragmented literature and guide future detection research. The manuscript's central claim of comprehensiveness, however, cannot be evaluated without evidence of a reproducible search and screening process; if that evidence is absent, the identified gaps and proposed solutions rest on an unverified subset of the literature.

major comments (1)
  1. [Abstract / Methods] Abstract and (presumed) Methods section: the claim of a 'comprehensive systematic review' is made without any description of databases searched, query strings, inclusion/exclusion criteria, screening flow (e.g., PRISMA diagram), or quality assessment. This directly undermines the representativeness of the gaps and recommendations that constitute the paper's main contribution.
minor comments (2)
  1. [Abstract] The abstract is repetitive in its description of impacts and solutions; a single concise statement would suffice.
  2. [Results / Discussion] No mention of how many papers were ultimately included or their distribution across years/platforms; adding a summary table would improve clarity.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. The primary concern raised is the absence of explicit systematic-review methodology details, which we address below. We agree this information is necessary to support the paper's claims.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and (presumed) Methods section: the claim of a 'comprehensive systematic review' is made without any description of databases searched, query strings, inclusion/exclusion criteria, screening flow (e.g., PRISMA diagram), or quality assessment. This directly undermines the representativeness of the gaps and recommendations that constitute the paper's main contribution.

    Authors: We agree that the manuscript as submitted does not contain a dedicated Methods section or the required details on search strategy, screening process, or quality assessment. This omission weakens the justification for labeling the work a comprehensive systematic review. In the revised manuscript we will insert a new Methods section that specifies: (1) the databases queried (e.g., IEEE Xplore, ACM Digital Library, Scopus, Web of Science, Google Scholar), (2) the exact Boolean query strings employed, (3) inclusion and exclusion criteria with rationale, (4) a PRISMA flow diagram showing the number of records at each stage, and (5) any quality-assessment criteria applied to the retained studies. These additions will allow readers to evaluate the representativeness of the synthesized gaps and recommendations. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive survey with no derivations or predictions

full rationale

This is a systematic review paper with no equations, fitted parameters, predictions, or derivation chain. None of the enumerated circularity patterns (self-definitional, fitted input called prediction, self-citation load-bearing, etc.) can apply because the paper performs no modeling or inference that could reduce to its inputs by construction. The central claim is a literature synthesis whose validity rests on search methodology rather than any self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

As a review paper, the work rests on the domain assumption that prior publications form a coherent body that can be systematically surveyed to reveal gaps; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Existing studies on cyberbullying detection provide a sufficient and unbiased sample for identifying research gaps and recommending solutions.
    Invoked in the abstract's description of the review's purpose and output.

pith-pipeline@v0.9.0 · 5705 in / 1260 out tokens · 42191 ms · 2026-05-24T01:02:56.006091+00:00 · methodology

discussion (0)

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Reference graph

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