Despite being an integral tool for finding health-related information online, YouTube has faced criticism for disseminating COVID-19 misinformation globally to its users. Yet, prior audit studies have predominantly investigated YouTube within the Global North contexts, often overlooking the Global South. To address this gap, we conducted a comprehensive 10-day geolocation-based audit on YouTube to compare the prevalence of COVID-19 misinformation in search results between the United States (US) and South Africa (SA), the countries heavily affected by the pandemic in the Global North and the Global South, respectively. For each country, we selected 3 geolocations and placed sock-puppets, or bots emulating "real" users, that collected search results for 48 search queries sorted by 4 search filters for 10 days, yielding a dataset of 915K results. We found that 31.55% of the top-10 search results contained COVID-19 misinformation. Among the top-10 search results, bots in SA faced significantly more misinformative search results than their US counterparts. Overall, our study highlights the contrasting algorithmic behaviors of YouTube search between two countries, underscoring the need for the platform to regulate algorithmic behavior consistently across different regions of the Globe.

The proliferation of online misinformation has emerged as one of the biggest threats to society. Considerable efforts have focused on building misinformation detection models, still the perils of misinformation remain abound. Mitigating online misinformation and its ramifications requires a holistic approach that encompasses not only an understanding of its intricate landscape in relation to the complex issue and topic-rich information ecosystem online, but also the psychological drivers of individuals behind it. Adopting a time series analytic technique and robust causal inference-based design, we conduct a large-scale observational study analyzing over 32 million COVID-19 tweets and 16 million historical timeline tweets. We focus on understanding the behavior and psychology of users disseminating misinformation during COVID-19 and its relationship with the historical inclinations towards sharing misinformation on Non-COVID domains before the pandemic. Our analysis underscores the intricacies inherent to cross-domain misinformation, and highlights that users' historical inclination toward sharing misinformation is positively associated with their present behavior pertaining to misinformation sharing on emergent topics and beyond. This work may serve as a valuable foundation for designing user-centric inoculation strategies and ecologically-grounded agile interventions for effectively tackling online misinformation.

Susceptibility to misinformation describes the extent to believe unverifiable claims, which is hidden in people's mental process and infeasible to observe. Existing susceptibility studies heavily rely on the self-reported beliefs, making any downstream applications on susceptability hard to scale. To address these limitations, in this work, we propose a computational model to infer users' susceptibility levels given their activities. Since user's susceptibility is a key indicator for their reposting behavior, we utilize the supervision from the observable sharing behavior to infer the underlying susceptibility tendency. The evaluation shows that our model yields estimations that are highly aligned with human judgment on users' susceptibility level comparisons. Building upon such large-scale susceptibility labeling, we further conduct a comprehensive analysis of how different social factors relate to susceptibility. We find that political leanings and psychological factors are associated with susceptibility in varying degrees.

The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9\% based on average F1 measure.

COVID-19 misinformation on social media platforms such as twitter is a threat to effective pandemic management. Prior works on tweet COVID-19 misinformation negates the role of semantic features common to twitter such as charged emotions. Thus, we present a novel COVID-19 misinformation model, which uses both a tweet emotion encoder and COVID-19 misinformation encoder to predict whether a tweet contains COVID-19 misinformation. Our emotion encoder was fine-tuned on a novel annotated dataset and our COVID-19 misinformation encoder was fine-tuned on a subset of the COVID-HeRA dataset. Experimental results show superior results using the combination of emotion and misinformation encoders as opposed to a misinformation classifier alone. Furthermore, extensive result analysis was conducted, highlighting low quality labels and mismatched label distributions as key limitations to our study.

At an ad-industry conference in New York this month, one of the key architects of Spotify's podcasting strategy outlined what she saw as the biggest challenge facing platforms: how to moderate content. Chief Content and Advertising Business Officer Dawn Ostroff, the television veteran who had helped bring U.S. podcaster Joe Rogan and other top talent to Spotify, had been asked about the backlash to COVID-19 misinformation spread on his podcast as Neil Young and other artists withdrew their music in protest. She said companies faced a "dilemma of moderation versus censorship" and that there was "no silver bullet." Content moderation has been a thorny challenge for online platforms. While social media companies like Meta's Facebook and Twitter have faced pressure to be more transparent over moderation and ramp up investment in human and artificial-intelligence review systems, podcasting has often flown under the radar.

Science fiction movies like "Blade Runner" and "The Terminator" have defined the perception of artificial intelligence within popular culture. For most people, the term AI conjures up images of a dystopian future dominated by humanoid robots that have taken over the world. This common conception leads to the dismissal of the technology as impossible, or at least faroff in the future. Few people realize that we are already delving into a world dominated by AI, and it's nothing like "The Terminator." The actual risks posed by artificial intelligence have nothing to do with killer robots; they relate to the machine-learning algorithms that recommend content on the internet.

Simon Fraser University's International CyberCrime Research Centre (ICCRC) is engaged in a new project to develop artificial intelligence tools to fight COVID-19-related misinformation campaigns on social media. Throughout the pandemic, anti-science theories on social media that portray COVID-19 as a hoax or downplay the risk of infection have contributed to unnecessary transmission and death. Some research suggests that one-in-three people have encountered false or misleading information about COVID-19 on social media. And while COVID-19 vaccines are rolling out, misinformation on social media still fuels vaccine hesitancy in Canada and resistance to public health measures such as mask wearing. To combat this, the ICCRC - in SFU's School of Criminology - has received federal funding from the Digital Citizenship Contribution Program for a six-month research project to develop an artificial intelligence tool to help social media platforms, online service providers and government agencies identify COVID-19 misinformation campaigns on social media and take appropriate action.

As machine learning has evolved, so have best practices, especially in the wake of COVID-19. Join this VB Live event to learn from experts about how machine learning solutions are helping companies respond in these uncertain times – and the lessons learned along the way. Misinformation around COVID-19 is driving human behavior across the world. Here in the information age, sensationalized clickbait headlines are crowding out actual fact-based content, and, as a result misinformation spreads virally. Conversations within small communities become the epicenter of false information, and that misinformation spreads as people talk, both online and off.

PROVIDENCE, Rhode Island – As the world races to find a vaccine and a treatment for COVID-19, there is seemingly no antidote in sight for the burgeoning outbreak of coronavirus conspiracy theories, hoaxes, anti-mask myths and sham cures. The phenomenon, unfolding largely on social media, escalated this week when U.S. President Donald Trump retweeted a false video about an anti-malaria drug being a cure for the virus and it was revealed that Russian intelligence is spreading disinformation about the crisis through English-language websites. Experts worry the torrent of bad information is dangerously undermining efforts to slow the virus, whose death toll in the U.S. hit 150,000 Wednesday, by far the highest in the world, according to the tally kept by Johns Hopkins University. Over a half-million people have died in the rest of the world. Hard-hit Florida reported 216 deaths, breaking the single-day record it set a day earlier.