Related papers: Looking for COVID-19 misinformation in multilingua…
Scientific facts are often spun in the popular press with the intent to influence public opinion and action, as was evidenced during the COVID-19 pandemic. Automatic detection of misinformation in the scientific domain is challenging…
As of 2020 when the COVID-19 pandemic is full-blown on a global scale, people's need to have access to legitimate information regarding COVID-19 is more urgent than ever, especially via online media where the abundance of irrelevant…
The rapid growth of social media content during the current pandemic provides useful tools for disseminating information which has also become a root for misinformation. Therefore, there is an urgent need for fact-checking and effective…
Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger to people's health, particularly when false…
Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms. Existing literature has focused primarily on the fully-automated…
We demonstrate the Misinformation Concierge, a proof-of-concept that provides actionable intelligence on misinformation prevalent in social media. Specifically, it uses language processing and machine learning tools to identify subtopics of…
In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. Most approaches to misinformation detection are monolingual, focused on high-resource languages,…
The Coronavirus (COVID-19) pandemic has led to a rapidly growing 'infodemic' of health information online. This has motivated the need for accurate semantic search and retrieval of reliable COVID-19 information across millions of documents,…
Social media platforms like Facebook, Twitter, and Instagram have enabled connection and communication on a large scale. It has revolutionized the rate at which information is shared and enhanced its reach. However, another side of the coin…
In this paper, we present a first multilingual cross-domain dataset of 5182 fact-checked news articles for COVID-19, collected from 04/01/2020 to 15/05/2020. We have collected the fact-checked articles from 92 different fact-checking…
The use of content features, particularly textual and linguistic for fake news detection is under-researched, despite empirical evidence showing the features could contribute to differentiating real and fake news. To this end, this study…
In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language…
This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19…
With the growing adoption of short-form video by social media platforms, reducing the spread of misinformation through video posts has become a critical challenge for social media providers. In this paper, we develop methods to detect…
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…
We introduce a new benchmark, COVID-VTS, for fact-checking multi-modal information involving short-duration videos with COVID19- focused information from both the real world and machine generation. We propose, TwtrDetective, an effective…
This paper makes two key contributions. First, it argues that highly specialized rare content classifiers trained on small data typically have limited exposure to the richness and topical diversity of the negative class (dubbed anticontent)…
The coronavirus pandemic (COVID-19) is probably the most disruptive global health disaster in recent history. It negatively impacted the whole world and virtually brought the global economy to a standstill. However, as the virus was…
In this work, we collect a moderate-sized representative corpus of tweets (200,000 approx.) pertaining Covid-19 vaccination spanning over a period of seven months (September 2020 - March 2021). Following a Transfer Learning approach, we…
In order to combat the COVID-19 pandemic, society can benefit from various natural language processing applications, such as dialog medical diagnosis systems and information retrieval engines calibrated specifically for COVID-19. These…