Content-Driven Detection of Cyberbullying on the Instagram Social Network / 3952
Haoti Zhong, Hao Li, Anna Squicciarini, Sarah Rajtmajer, Christopher Griffin, David Miller, Cornelia Caragea

We study detection of cyberbullying in photo-sharing networks, with an eye on developing early warning mechanisms for the prediction of posted images vulnerable to attacks. Given the overwhelming increase in media accompanying text in online social networks, we investigate use of posted images and captions for improved detection of bullying in response to shared content. We validate our approaches on a dataset of over 3000 images along with peer-generated comments posted on the Instagram photo-sharing network, running comprehensive experiments using a variety of classifiers and feature sets. In addition to standard image and text features, we leverage several novel features including topics determined from image captions and a pretrained convolutional neural network on image pixels. We identify the importance of these advanced features in assisting detection of cyberbullying in posted comments. We also provide results on classification of images and captions themselves as potential targets for cyberbullies.

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