Detection of Cyberbullying Incidents on the Instagram Social Network

By Homa Hosseinmardi, S. Mattson, R. Rafiq, Richard O. Han, Q. Lv, Shivakant Mishra. 2015

187 citations recorded by the source corpus.

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.

Subfields: Natural Language Processing, Safety, Robustness & Interpretability

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Citations in the corpus

A Multimodal Framework for the Detection of Hateful Memes (2020, methodology): “[11] discuss the problem of cyberbullying and cyberaggression in Instagram posts and comments and evaluate the performance of a Naive Bayes classifier and a linear SVM classifier using a range of features, such as word n-grams and image categories, as well as metadata such as the number of followers”
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “[31] collect a dataset of Instagram images and their associated comments, which they then label with the help of Crowdflower workers.”
A Measurement Study of Hate Speech in Social Media (2017, background): “There are reports of cases of hateful messages in many other social media independently of the level in which the online identity is bonded to an offline identity – e.g., in Whisper [25], Twitter [24], Instagram [15], and Facebook [17].”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “Several hate speech datasets are publicly available, usually including a binary annotation, i.e. whether the content is hate-ful or not (Reynolds et al., 2011; Raﬁq et al., 2015; Hosseinmardi et al., 2015; de Gibert et al., 2018; ElSherief et al., 2018).”
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022, methodology): “Many benchmarks for automatic HS detection are currently available (Mathew et al., 2021; Cao et al., 2020; Kumar et al., 2018; Hosseinmardi et al., 2015; Waseem, 2016; Burnap and Williams, 2016).”
Toward Multimodal Cyberbullying Detection (2017, methodology): “…API which analyzes the images for the dominant colors, number of people present, category, adult content, etc. Based on existing literature [26, 51, 28, 43, 45], the following features were obtained using the API and used for cyberbullying detection: • Age and gender of people in the image…”
Detecting Online Hate Speech Using Context Aware Models (2017, methodology): “…models have been used in several prior hate speech detection studies Chen et al. (2012); Burnap and Williams (2014); Van Hee et al. (2015); Hosseinmardi et al. (2015); Burnap and Williams (2015); Waseem and Hovy (2016); Wulczyn et al. (2016); Nobata et al. (2016) and various features have…”