Second Workshop on Online Misinformation- And Harm-Aware Recommender Systems: Preface

This volume contains the proceedings with the research contributions presented at the Second Workshop on Online Misinformation-and Harm-Aware Recommender Systems (OHARS'2021) co-located with the 15th ACM Recommender Systems Conference (RecSys'2021). These proceedings describe the specific workshop goals and format, and contain the papers presented during the online event held on October 2nd, 2021

“To trust a LIAR”: does machine learning really classify fine-grained, fake news statements?

Fake news refers to deceptive online content and is a problem which causes social harm. Early detection of fake news is therefore a critical but challenging problem. In this paper we attempt to determine if state-of-the-art models, trained on the LIAR dataset can be leveraged to reliably classify short claims according to 6 levels of veracity that range from “True” to “Pants on Fire” (absolute lies). We investigate the application of transformer models BERT, RoBERTa and ALBERT that have previously performed significantly well on several natural language processing tasks including text classification. A simple neural network (FcNN) was also used to enhance each model’s result by utilising the sources’ reputation scores. We achieved higher accuracy than previous studies that used more data or more complex models. Yet, after evaluating the models’ behaviour, numerous flaws appeared. These include bias and the fact that they do not really model veracity which makes them prone to adversarial attacks. We also consider the possibility that language-based, fake news classification, on such short statements is an ill-posed problem.peer-reviewe

Sound-and-Image-informed Music Artwork Generation Using Text-to-Image Models

While some artists are involved in both domains, the creation of music and artwork require different skill sets. The development of deep generative models for music and image generation has potential to democratise these mediums and make multi-modal creation more accessible for casual creators and other stakeholders. In this work, we propose a co-creative pipeline for the generation of images to accompany a musical piece. This pipeline utilises state-of-the-art models for music-to-text, image-to-text, and subsequently text-to-image generation to recommend, via generation, visuals for a piece of music that are informed not only by the audio of a musical piece, but also a user-recommended corpus of artworks and prompts to give a meaningful grounding in the generated material. We demonstrate the potential of our pipeline using a corpus of material from artists with strongly connected visual and musical identities, and make it available in the form of a Python notebook for users to easily generate their own musical and visual compositions using their chosen corpus - available here: https://github.com/alexjameswilliams/Music-Text-To-Image-Generatio

Acquiring the Preferences of New Users in

When dealing with a new user, not only Recommender Systems (RS) must extract relevant information from the ratings given by this user for some items (e.g., films, CDs), but also it must be able to ask the good questions, i.e. give a minimum list of items which once rated will be the most informative. Previous work proposed the use of item's controversy and popularity as criteria for selecting informative items to be rated. These works intuitively pointed out at possible limitations of controversy measures with respect to the number of ratings. In this paper, we show empirically that the number of ratings is relevant; we propose a new selection measure of item's controversy; and, we demonstrate that this measure naturally also takes into account the popularity criterion. The experiments showed promising results for the new controversy measure when compared to benchmark selection criteria