Inside the Social Media Lab’s Bridging Divides Studies on Online Migration Discourse

The Social Media Lab has recently launched a new initiative to study the impact of mis- and disinformation on immigrants and immigration policies, led by the Lab’s co-directors, Anatoliy Gruzd and Philip Mai. The initiative is a part of the larger Migration Integration in the Mid-21st Century: Bridging Divides initiative, a $98.6 million grant led by Toronto Metropolitan University (TMU) with partners from universities across Canada. 

Our team is in the early stages of working on this initiative, and we wanted to give you a sneak peek into several studies that we’ve been working on. Specifically, this post features work by a group of student researchers from TMU’s M.Sc. Data Science and Analytics program, Sameer Ladha, Vaidehi Atodaria, Ricky Yu, and Pavleen Brar, all of whom are working under the supervision of Dr. Gruzd on four projects for the Bridging Divides initiative as part of their Major Research Projects.

TMU’s M.Sc. Data Science and Analytics program students: Sameer Ladha, Vaidehi Atodaria, Ricky Yu, and Pavleen Brar

“We got an email explaining this unique opportunity to work with Dr. Gruzd,” says Ladha. “My research background is different, coming from psychology at Carleton University, but I’m interested in the topic and how relevant it is today.” 

Ladha is examining the relationship between political polarization and the spread of hate speech about refugees and migrants. He’s identifying public Reddit groups (subreddits) and relevant YouTube videos from which public discussions on this topic can be analyzed, such as the r/Canada, r/CanadaPolitics, and r/ImmigrationCanada subreddits. Narratives and counter-narratives on social media inform the intersection of public policies, misinformation, and personal biases; by analyzing these narratives, the project seeks to understand the dynamics behind the spread of hate speech and its possible connection to misinformation about refugees and migrants.

While swearing and other forms of toxic language may be normalized in certain online groups or platforms as a form of slang, he’s focused on instances where language is used to propagate hate, silence or harass other users. “If someone has five toxic posts on their profile,” he says, “how likely are they to post a sixth?”

Atodaria is using CrowdTangle to collect posts shared on public Facebook groups and pages to study how these positive, negative or mixed attitudes shape public discourse on immigration. Coming from both undergraduate and graduate programs in computer science, she’s expanding her data literacy by using methods such as sentiment analysis and topic modeling in her research. 

The central aim of this case study is to uncover the potential impacts of geopolitical strains on immigration and diaspora communities in Canada. Facebook was selected as the primary source due to its diverse user base and because it is the most popular social media platform in Canada.

“India-Canada immigration-related discourse is especially relevant because of the recent [diplomatic] tensions between India and Canada,” she explains. By examining these public posts related to Indo-Canadian relations, Atodaria may be able to see “spillover” effects related to immigration topics, such as negative posts discouraging immigration to Canada. 

Yu entered the Data Science and Analytics graduate program straight from the University of Toronto, where he majored in physics and astronomy while completing a minor in data science. Working on this research as part of Bridging Divides allowed him to gain experience applying data science techniques to answer empirically significant research questions.

More specifically, he’s examining a Twitter dataset of over 1.8M posts with the #StopAAPIHate hashtag shared during the COVID-19 pandemic to counter racist and hateful messages targeting Asian Americans and Asian Canadians, a significant number of whom are immigrants.

“We’re conducting sentiment analysis on public discourse around immigration topics, with a goal of developing a way to extract common keywords depending on the sentiment expressed in a tweet,” he adds. “In the future, refining this methodology could help track the evolution of these topics.” 

By uncovering patterns of potentially coordinated hate campaigns on social media and shedding light on the intersection of anti-Asian sentiment and immigration issues, Yu believes his project can find insights to inform protective strategies against targeted and coordinated attacks towards immigrants.

Brar entered the Data Science and Analytics program to gain an in-depth understanding of machine learning after six years of experience as a data scientist at Hewlett-Packard. In her project, she is using topic modeling to analyze a dataset of over 7M posts disseminated by the official Twitter account of Russia’s Ministry of Foreign Affairs from 2011 to 2023.

This case study aims to understand the extent to which the Russian Foreign Ministry strategically employs immigration and refugee-related themes as a potential tool for propaganda against the West (including Canada). With over a decade of data, examining these tweets’ content, frequency, and framing sheds light on how pro-Kremlin communication strategies shape Western perceptions of migration issues.

Together, these four studies, which cut across four very different social media platforms, are part of the Lab’s larger research initiative to provide policymakers with a better understanding of the forces that are driving much of the anti-immigration and anti-refugee narratives online and offline. Studying and understanding these narratives can help build more effective countering strategies against online mis- and disinformation about immigration and foster more inclusive and informed public discussions about immigration and the immigration policies that will continue to shape Canadian society. 

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