Everyday more users are using memes on social media platforms to convey a message with text and image combined. Although there are many fun and harmless memes being created and posted, there are also ones that are hateful and offensive to particular groups of people. In this article present a novel approach based on the CLIP network to detect misogynous memes and find out the types of misogyny in that meme. We participated in Task A and Task B of the Multimedia Automatic Misogyny Identification (MaMi) challenge and our best scores are 0.694 and 0.681 respectively.

We present an automated approach to analyze the text of an online conversation and determine whether one of the participants is a cyberpredator who is preying on another participant. The task is divided into two stages, 1) the classification of each message, and 2) the classification of the entire conversation. Each stage uses a Recurrent Neural Network (RNN) to perform the classification task.