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Detection of Cyberbullying Incidents on the Instagram Social Network

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arxiv 1503.03909 v1 pith:Q25APZIG submitted 2015-03-12 cs.SI

Homa Hosseinmardi , Sabrina Arredondo Mattson , Rahat Ibn Rafiq , Richard Han , Qin Lv , Shivakant Mishra This is my paper · ORCID
classification cs.SI
keywords cyberbullyingdataincidentsinstagramautomaticallydetectimageslabeled
verification ladder T0 review T1 audit T2 compute T3 formal
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Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect incidents of cyberbullying over images in Instagram, a media-based mobile social network. To this end, we have collected a sample Instagram data set consisting of images and their associated comments, and designed a labeling study for cyberbullying as well as image content using human labelers at the crowd-sourced Crowdflower Web site. An analysis of the labeled data is then presented, including a study of correlations between different features and cyberbullying as well as cyberaggression. Using the labeled data, we further design and evaluate the accuracy of a classifier to automatically detect incidents of cyberbullying.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. CATCH-ME if you RAG: a dataset of Contextually Annotated multi-Turn Counterspeech against Hate and Misinformation Exchanges

    cs.CL 2026-06 unverdicted novelty 8.0 of 10

    Presents a new expert-curated dataset of multi-turn counterspeech dialogues in five languages targeting hate against seven groups, with span annotations linking to verified external knowledge for RAG applications.

  2. Benchmark on Peer Review Toxic Detection: A Challenging Task with a New Dataset

    cs.CL 2025-02 conditional novelty 7.0 of 10

    A new 313-sentence peer-review toxicity benchmark shows GPT-4 with detailed instructions reaches a Cohen's Kappa of 0.56 with human judges.

  3. SPILLOVER: Measuring Cyberbullying NormPropagation on Social Media

    cs.SI 2026-07 conditional novelty 6.0 of 10

    A preceding cyberbullying comment raises the odds that the next comment is cyberbullying, and the copied aggression is content-specific rather than generic, across four social media platforms.

  4. Assisted Counterspeech Writing at the Crossroads of Hate Speech and Misinformation

    cs.CL 2026-05 conditional novelty 6.0 of 10

    LLMs generate adequate counterspeech for co-occurring hate and misinformation in 40% of cases, with a mixed knowledge strategy from fact-checkers and NGOs proving most effective after expert revision.

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