Safeguarding Privacy in the Age of Everyday XR

Prepared by: Pejman Saeghe, Mark McGill & Mohamed Khamis

Abstract: The commercialisation of extended reality (XR) devices provides new capabilities for its user, such as the ability to continuously capture their surroundings. This introduces novel privacy risks and challenges for XR users and bystanders alike. In this position paper, we use an established taxonomy of privacy to highlight its limitations when dealing with everyday XR. Our aim is to highlight a need for an update in our collective understanding of privacy risks imposed by everyday XR technology.

Paper available for download here.

MuMiN: A Large Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset

Prepared by: Dan S. Nielsen & Ryan McConville

Abstract: Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learning to detect such content. Training these machine learning models require datasets of sufficient scale, diversity and quality. However, datasets in the field of automatic misinformation detection are predominantly monolingual, include a limited amount of modalities and are not of sufficient scale and quality. Addressing this, we develop a data collection and linking system (MuMiN-trawl), to build a public misinformation graph dataset (MuMiN), containing rich social media data (tweets, replies, users, images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade. The dataset is made available as a heterogeneous graph via a Python package (mumin). We provide baseline results for two node classification tasks related to the veracity of a claim involving social media, and demonstrate that these are challenging tasks, with the highest macro-average F1- score being 62.55% and 61.45% for the two tasks, respectively. The MuMiN ecosystem is available at https://mumin-dataset.github.io/, including the data, documentation, tutorials and leaderboards.

Paper available for download here.