Is it Fact or Fake? COVID-19 Misinformation Tweets and Network Structures are Highly Similar to Factual Tweets
Adam Radek Martinez, John Zhang, Simha Kalimipalli, Karan Manku
University of Toronto and University of Waterloo
Are You Spreading Misinformation? Analyzing How Influential Twitter Users Contribute to the Spread of COVID-19 Information
Bizhan Alatif, Jennifer Tram Su, Maggie Wang, Sarina Xi
McGill University and University of Toronto
The Plebeian Algorithm A Democratic Approach to Censorship and Moderation
Benjamin D. Fedoruk, Harrison S. Nelson, Kai A. Fucile Ladouceur, Russell M. Frost
Univerity of Ontario Instituite of Technology, Queen’s University, Confederation College and Lakehead University
You’ve Reddit All: Popular COVID-19 Topics and Public Sentiment Trends in Vancouver, British Columbia
Cathy Yan, Melanie Law, Stephanie Nguyen, Janelle G. Cheung
University of British Columbia
Introducing ADMIT : A First Step in Uncovering FDA’s Deceit
Jérémie Babeu, Zoé Benoit, Andréanne Boulanger, Antoine Turcotte
College de St-Hyacinthe, Université de Montréal, Concordia University
Investigating the impact of claims on AstraZeneca vaccine causing blood-clotting on the public perception of vaccination
Jeyoung Oh, Trista Tian , Thao Tran, Max Vu
University of Waterlooo
Data Exploration and Classification of News Article Reliability: A Deep Learning Study
Kevin Zhan, Rafay Osmani, Yutong Li, Xiaoyu Wang
University of Alberta
What pandemic events have had the most influence on public sentiment towards the AstraZeneca, Pfizer and Moderna COVID-19 Vaccines?
Muskaan Kaur Bajwa, Angelica Ramoutar, Neha Purakan
McMaster University and University of Toronto Mississauga
Classifying Fake COVID-19 Tweets With Supervised Learning and Deep Learning Models
Mashiyat Saif, Marcus Chung, Oluwatitomi Adebajo, Oluwatobi Adebajo
University of Toronto, University of Western Ontario and McMaster University
Pipeline for identifying vaccine-specific infodemic insights from Youtube and Reddit and leveraging NLP deep learning models to predict misinformation
Nikhil Saini, Nirupama Tamvada, Mansi Patel
Machine learning-based predictive modelling of COVID-19 vaccination uptake within U.S. counties
Queena Cheong, Martin Au-Yeung, Stephanie Quon, Katsy Concepcion
University of British Columbia
Predicting Vaccine Uptake Rate Using Machine Learning: An infodemiological Study in the United States
Xingzuo Zhou, Yiang Li, Shuheng Yang, Xiyan Shi
University College London and University of Toronto
Predicting Falsehood of a Tweet Concerning COVID-19 Using Location, Socioeconomic Data, and Tweet Sentiment
Danish Baig, KaHo Wong, Sabina Henry
University of Waterloo and University of Ottawa
About This Year's Event
STEM Fellowship hosted the world’s first infodemiology themed hackathon through the 2021 Undergraduate Big Data Challenge, culminating in the national finale event of the Big Data Day. The top 13 teams from across Canada defended their work, and were able to showcase their solutions to a national audience. The winning teams won cash prizes, the opportunity to publish their manuscripts in the STEM Fellowship Journal, and internship opportunities with our partners.
On top of showcasing their work, the students attended a keynote presentation by Dr. Gunther Eysenbach – the Founder and CEO of JMIR Publications and the researcher who coined the term “infodemiology”. The event also featured a Roundtable Discussion featuring Adrian Stanley (Chief Innovation & Development Officer – JMIR Publications), Bushra Ebadi (Executive Committee Member – CC UNESCO), Dr. Michael Duong (Head of Innovation – Roche Canada), and Moez Ali (Founder & Creator – PyCaret), moderated by Dr. Sacha Noukhovitch (President & Founder – STEM Fellowship). In the discussion, these four experts explored their views on Public Health and Diversity Aspects of Real-world Infodemic Cases.
Assistant Professor at the University of Calgary, Adjunct Assistant Professor at McMaster University
Consultant for the Data Science and Analytics Group at ICICI Bank Canada
Senior Data Scientist at MPI
Data Scientist at TD Bank
Research Associate at National Research Council
CEO and Co-founder at Blockchain Chamber of Commerce
Senior Data Scientist at the National Research Council Data Analytics Centre
Data Scientist, Software Developer at 1QBit
Chief Data Officer at STEM Fellowship, Quantitative Analyst and Quantum Researcher at 1QBit
WHAT IS THE BIG DATA CHALLENGE?
The Big Data Challenge (BDC) for undergraduate students is an inquiry-driven experiential learning program that invites students from across the country to strengthen their problem-solving and critical thinking skills while gaining familiarity with the fundamentals of data science.
DO I NEED PREVIOUS PROGRAMMING EXPERIENCE?
You do not need previous experience with programming, although it is recommended. We welcome all students who are eager to put effort into learning and expanding their skillsets, as well as those who show any level of interest in data science or the challenge topic.
HOW DO I FORM OR JOIN A TEAM?
We encourage participants to start forming teams before the event. You may also register and participate on your own or request to be placed into a team after registration.
DO I NEED TO HAVE AN IDEA FOR MY PROJECT?
Think about what interested you the most in the field of the provided topic. Reflect on your day-to-day; talk to your friends and professional network from academia and industry; explore emerging technologies and platforms; read the internet and research articles. In hackathons like these, many teams come up with their topics in the first few days of the challenge, rather than beforehand.
I RECENTLY GRADUATED, AM I ELIGIBLE TO PARTICIPATE IN THE UNDERGRADUATE BIG DATA CHALLENGE?
Yes, anyone who has graduated within 12 months is eligible to register for our Big Data Challenge.