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.

View More Papers

WeepingCAN: A Stealthy CAN Bus-off Attack

Gedare Bloom (University of Colorado Colorado Springs) Best Paper Award Winner ($300 cash prize)!

Read More

FlowLens: Enabling Efficient Flow Classification for ML-based Network Security...

Diogo Barradas (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Nuno Santos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Luis Rodrigues (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Salvatore Signorello (LASIGE, Faculdade de Ciências, Universidade de Lisboa), Fernando M. V. Ramos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), André Madeira (INESC-ID, Instituto Superior Técnico, Universidade de…

Read More

Does Every Second Count? Time-based Evolution of Malware Behavior...

Alexander Küchler (Fraunhofer AISEC), Alessandro Mantovani (EURECOM), Yufei Han (NortonLifeLock Research Group), Leyla Bilge (NortonLifeLock Research Group), Davide Balzarotti (EURECOM)

Read More

Detecting Kernel Memory Leaks in Specialized Modules with Ownership...

Navid Emamdoost (University of Minnesota), Qiushi Wu (University of Minnesota), Kangjie Lu (University of Minnesota), Stephen McCamant (University of Minnesota)

Read More