Finalists

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

McMaster University

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.

Judges

Na Li

Assistant Professor at the University of Calgary, Adjunct Assistant Professor at McMaster University

Saarthak Sangamnerkar

Consultant for the Data Science and Analytics Group at ICICI Bank Canada

Yinka Awoyemi

Senior Data Scientist at MPI

Sanveer Dhanju

Data Scientist at TD Bank

Hilda Azimi

Research Associate at National Research Council

Rayshoun Chambers

CEO and Co-founder at Blockchain Chamber of Commerce

Raman Pall

Senior Data Scientist at the National Research Council Data Analytics Centre

Javad YaAli

Data Scientist, Software Developer at 1QBit

Anish Verma

Chief Data Officer at STEM Fellowship, Quantitative Analyst and Quantum Researcher at 1QBit

FAQ

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.