Arkin Dharawat

Arkin Dharawat, Chris Doan

In recent years, there has been an increase in the number of devices with virtual assistants (e.g: Siri, Google Home, Alexa) in our living rooms and kitchens. As a result of this, these devices receive several queries about recipes. All these queries will contain terms relating to a "recipe-domain" i.e: they will contain dish-names, ingredients, cooking times, dietary preferences etc. Extracting these recipe-relevant aspects from the query thus becomes important when it comes to addressing the user's information need. Our project focuses on extracting ingredients from such plain-text user utterances. Our best performing model was a fine-tuned BERT which achieved an F1-score of $95.01$. We have released all our code in a GitHub repository.

Arkin Dharawat, Ismini Lourentzou, Alex Morales et al.

Given the widespread dissemination of inaccurate medical advice related to the 2019 coronavirus pandemic (COVID-19), such as fake remedies, treatments and prevention suggestions, misinformation detection has emerged as an open problem of high importance and interest for the research community. Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts. In this work, we frame health misinformation as a risk assessment task. More specifically, we study the severity of each misinformation story and how readers perceive this severity, i.e., how harmful a message believed by the audience can be and what type of signals can be used to recognize potentially malicious fake news and detect refuted claims. To address our research questions, we introduce a new benchmark dataset, accompanied by detailed data analysis. We evaluate several traditional and state-of-the-art models and show there is a significant gap in performance when applying traditional misinformation classification models to this task. We conclude with open challenges and future directions.