With the overarching objective to assist users in determining the trustworthiness of information in social media using an intermediary-free approach, EUNOMIA employs a decentralised architecture Mastodon instance and implements AI technology to generate information cascade of the posts to facilitate the discovery and visualisation of the source of information, how information is shared and changed over time to provide users with provenance information when they are determining a post’s trustworthiness. The information cascade is generated not only based on the text content of a post via paraphrase identification using natural language processing (NLP) technique, but also the image content of the post via image verification using computer vision technique.
Image verification algorithm is implemented with the aim to determine whether a given pair of images are similar or not in terms of images similarity. The advancements in image verification field is in two broad areas: image embedding and metric learning based. In image embedding, a robust and discriminative descriptor is learnt to represent each image as a compact feature vector/embedding. EUNOMIA employs current state-of-the-art feature descriptors generated by existing convolutional neural network (CNN) which learns features on its own. In metric based learning, a distance metric is utilised to learn from CNN-embeddings in an embedding space to effectively measure the similarity of images. Identical images obtain 100% in similarity; similar images gain higher similarity score; different images and some of the adversarial images would have lower similarity score as shown below.
With the implementation of image similarity functionality, EUNOMIA generates the information cascade by considering both text and image information of a social media post. EUNOMIA platform also has the potential to involve in fetching similar look images give a reference image to a EUNOMIA user.