Towards Understanding and Detecting Cyberbullying in Real-world Images

Nishant Vishwamitra (University at Buffalo), Hongxin Hu (University at Buffalo), Feng Luo (Clemson University), Long Cheng (Clemson University)

Cyberbullying has become widely recognized as a critical social problem plaguing today's Internet users. This problem involves perpetrators using Internet-based technologies to bully their victims by sharing cyberbullying-related content. To combat this problem, researchers have studied the factors associated with such content and proposed automatic detection techniques based on those factors. However, most of these studies have mainly focused on understanding the factors of textual content, such as comments and text messages, while largely overlooking the misuse of visual content in perpetrating cyberbullying. Recent technological advancements in the way users access the Internet have led to a new cyberbullying paradigm. Perpetrators can use visual media to bully their victims through sending and distributing images with cyberbullying content. As a first step to understand the threat of cyberbullying in images, we report in this paper a comprehensive study on the nature of images used in cyberbullying. We first collect a real-world cyberbullying images dataset with 19,300 valid images. We then analyze the images in our dataset and identify the factors related to cyberbullying images that can be used to build systems to detect cyberbullying in images. Our analysis of factors in cyberbullying images reveals that unlike traditional offensive image content (e.g., violence and nudity), the factors in cyberbullying images tend to be highly contextual. We further demonstrate the effectiveness of the factors by measuring several classifier models based on the identified factors. With respect to the cyberbullying factors identified in our work, the best classifier model based on multimodal classification achieves a mean detection accuracy of 93.36% on our cyberbullying images dataset.