Evidence map›Paper›PMID 41180056›Full record

ArticleIndustrial psychiatry journal

Lived experiences of individuals with cyberviolence: Understanding the nature of the dark side of social media use.

Anju S Nair, Nitin Anand, Manoj Kumar Sharma

Abstract read
In one paragraph

Article in Industrial psychiatry journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anju S NairDepartment of Clinical Psychology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.
Nitin AnandDepartment of Clinical Psychology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.
Manoj Kumar SharmaDepartment of Clinical Psychology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The advancement in technology has given more people accessibility to internet and social media. Technological advances have led to the exponential rise of cybercrime, with an average of 7000 cases being reported in India every day in 2024. Given the potential harmful consequences, a thorough understanding of this phenomenon and the ways to address them is critically required. Aim: To explore the lived experience of cyberviolence and online victimization. Materials and Methods: The sample comprised of 14 individuals aged 18-30 years, who were the target of cyberviolence, recruited through snowball sampling. Data was collected through a semi-structured interview schedule that was subjected to thematic analysis. Results: The key characteristics of cyberviolence perpetration were found to be the anonymity and power of perpetrator, availability of wider audience, its continuous nature, greater accessibility to the cyber victims, the inability to escape, ease of committing the crime, and difficulty in gathering evidence. These enable the perpetrators to commit different types of cyberviolence, including cyberbullying, online sexual harassment, identity theft, catfishing, and cyber grooming. Conclusion: The study sheds light on the rapidly emerging phenomenon of cyberviolence, operationalizes its definition, and outlines its key characteristics that likely act as facilitative factors for the perpetration of online violence.

Indexed as

Cyberbullying perpetrationcyberbullying victimizationcyberviolenceonline victimizationqualitative research

Identifiers

PMID41180056
PMCPMC12574775

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LicenceCC BY-NC-SA
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.