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Engagement and Abuse on Toronto's Digital Campaign Trail: The 2023 Toronto Mayoral By-election Report

Exploring engagement, toxicity, and civic conversations across online and offline spaces in the Toronto by-election.

April 9th, 2024

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At a glance

The Samara Centre for Democracy monitored activity on Twitter* during the 2023 Toronto Mayoral By-election as part of our SAMbot project, a multi-year machine learning initiative that measures abusive content received by Canadian political candidates online. We found that online abuse was prevalent during this election period (May 13 to June 26, 2023).

We monitored a total of 124,730 tweets during the election period.

3,988 of these tweets were identified as abusive.

90% of abusive tweets targeted top 9 candidates

30 Twitter users accounted for 10% of abusive tweets

Key Takeaways

  • A handful of Twitter users accounted for a significant percentage of all abusive tweets, suggesting that a small number of people are exerting an outsized effect on our online political conversations.
  • Content related to LGBTQ+ rights was correlated with online abuse and some of the highest periods of engagement, a trend we’ve observed in our past research.
  • Legacy media has a powerful ability to affect both online engagement and polling results, as lower-polling candidates rose in both online activity and in polling after being featured in debates or media profiles. This power can be used to either empower or exclude candidates from achieving wider reach and appeal.
  • Twitter engagement and electoral success wasn’t correlated, but online engagement was entwined with fiscal, political, and social capital regarding success in the Toronto by-election. Candidates were able to extend their reach when: they received endorsements and support from previously held political relationships; they were included in exposure opportunities because of their public personas; they had large pre-existing audiences on or offline; and they bought attention through advertising. All of these privileges or advantages could play a role in increasing organic online engagement.


Our findings emphasize that online spaces and offline spaces are not separate worlds. What happens in the online realm is intertwined with the physical world, and a part of one shared civic dialogue. As we grapple with how technology is influencing our democracy, we must consider how the working conditions for candidates on the local digital campaign trail are shaping who runs for, and who wins, municipal office.  

*The social media platform formerly known as “Twitter” has rebranded as “X” since we monitored this election. The platform was still called “Twitter” when we collected this data, so we refer to the platform as “Twitter” throughout this report.
**Candidate images are sourced from candidates’ by-election campaign materials.

Background

John Tory was elected mayor of Toronto in the fall of 2022, in an election where only 29.7% of eligible voters cast their ballots. Tory resigned from the mayoral chair in February 2023 and a mayoral by-election was called for Toronto in the spring of 2023. This election saw an increase in turnout from the 2022 election — the by-election had a voter turnout of 38.5%.

The 2023 Toronto mayoral by-election remarkably had 102 candidates of diverse personal and professional backgrounds. Some had formerly held significant political positions, some were long-time local advocates, and others were largely unknown candidates who did not provide much public information about their candidacies. 

For 45 days, from the end of the candidate nomination period to the end of election day (May 13 to June 26, 2023), we monitored 53 mayoral candidates on Twitter. The remaining candidates did not have public or active Twitter accounts as of the end of the election’s nomination period.

Candidates

Olivia Chow

269,372
votes
Elected Mayor
37%
of votes
33,930
tweets
960
abusive tweets

Bio

Olivia Chow is a former school trustee and Toronto city councillor. She served as the Member of Parliament (MP) for Trinity-Spadina from 2006 to 2014. She placed third in the 2014 Toronto mayoral race, and was ultimately elected mayor of Toronto in the 2023 mayoral by-election.

#1
in votes
#1
in tweets
#2
in abusive tweets

Ana Bailão

235,175
votes
32%
of votes
8,108
tweets
143
abusive tweets

Bio

Ana Bailão served three terms on the Toronto city council from 2010-2022, two as a councillor and one as deputy mayor. She retired from council in 2022 and did not run in that year’s municipal election. She was endorsed by outgoing Mayor of Toronto John Tory during her 2023 mayoral campaign.

#2
in votes
#6
in tweets
#7
in abusive tweets

Mark Saunders

62,167
votes
9%
of votes
22,209
tweets
612
abusive tweets

Bio

Mark Saunders formerly served as the chief of police for the Toronto Police Service. He ran as the Progressive Conservative candidate for Member of Provincial Parliament (MPP) in Don Valley West during the 2022 Ontario election. He was endorsed by Premier of Ontario Doug Ford during his 2023 mayoral campaign.

#3
in votes
#3
in tweets
#3
in abusive tweets

Anthony Furey

35,899
votes
5%
of votes
16,870
tweets
412
abusive tweets

Bio

Anthony Furey is a former Toronto Sun columnist, talk radio host, and news media executive.

#4
in votes
#4
in tweets
#4
in abusive tweets

Josh Matlow

35,572
votes
5%
of votes
11,310
tweets
165
abusive tweets

Bio

Josh Matlow is a Toronto city councillor for Ward 12, and has been on city council since 2010. While still a councillor, Matlow ran in the mayoral by-election. He remains a councillor as of publication.

#5
in votes
#5
in tweets
#6
in abusive tweets

Mitzie Hunter

21,229
votes
3%
of votes
2,856
tweets
50
abusive tweets

Bio

Mitzie Hunter served as the MPP for Scarborough—Guildwood from 2013-2023 and Ontario’s minister of education from 2016-2018. She resigned from the Ontario legislature to participate in the 2023 Toronto mayoral by-election.

#6
in votes
#9
in tweets
#12
in abusive tweets

Chloe Brown

18,831
votes
3%
of votes
3,045
tweets
19
abusive tweets

Bio

Chloe Brown is a policy analyst, activist, and former Toronto mayoral candidate. She placed third in the 2022 Toronto mayoral race.

#7
in votes
#8
in tweets
#14
in abusive tweets

Brad Bradford

9,254
votes
1%
of votes
7,052
tweets
183
abusive tweets

Bio

Brad Bradford is a Toronto city councillor for Ward 19, and has served in that role since 2018. While still a councillor, Bradford ran in the mayoral by-election. He remains a councillor today.

#8
in votes
#7
in tweets
#5
in abusive tweets

Chris Saccoccia

8,001
votes
1%
of votes
28,693
tweets
1,449
abusive tweets

Bio

Chris Saccoccia, commonly known as “Chris Sky,” is a Canadian property developer. He has been active in advocating against public health measures since the outbreak of COVID-19.

#8
in votes
#2
in tweets
#1
in abusive tweets

Analysis

We have analyzed the online engagement and abuse received by mayoral candidates in order to share insights about where online abuse manifested and compare the online engagement different candidates received on Twitter during the election period. While we focus on particular candidates in the following pieces, their experiences are used as a lens to better understand the process and experience of running for municipal office in Canada.

Conclusion

Our analysis tells us that online abuse is significant in big-city mayoral by-elections, just as it has been in recent Canadian municipal, provincial, and federal elections. We’re continuing to see topics related to LGBTQ+ rights attracting higher levels of both abuse and engagement online. Abusive rhetoric is mostly directed at the most popular candidates, and a significant portion of abusive tweets come from a handful of accounts. Online abuse is prevalent, but more concentrated around certain figures and topics than is properly understood, and this requires further research.

Evaluating Twitter engagement alongside election outcomes informs our perspective on how our municipal elections function. We see that candidates without much political capital but who have notable social or financial capital can make a significant impression on the electorate with advertising alone (Gong). “Outsider” candidates with limited political or social or financial capital can be barred from institutional opportunities like debates and media profiles (Brown), but outsider candidates with more connections can eventually enter into those institutional spaces, and see dividends in both online engagement and voter turnout because of it (Furey).

Ultimately, having access to both significant political and financial or social capital can ensure greater success at the polls, even if a candidate’s online engagement is much lower than that of fellow candidates (Bailão). These findings help us understand how issues of equity apply to big-city municipal elections, and the significant advantages certain candidates have, based on their political, social, or fiscal backgrounds.

We share these stories and experiences from the digital campaign trail to illustrate wider trends and experiences that candidates face in Canadian politics. Political candidates face difficult working conditions while running for office in the forms of online and offline abuse. This can be particularly difficult for equity-deserving Canadians to navigate as they may face increased abuse, threats, or violence based on their identity, which can limit the communities of people who feel comfortable running for office. These conditions need to be considered in tandem with the barriers created by the financial requirement of running a successful campaign for office, and the way political experience and social capital may limit new candidates from throwing their hat in the ring. We need to consider how all these factors shape participation and representation in our local democracies. A stronger understanding of the intertwined nature of the digital and physical campaign trail is crucial for building a more robust, participatory culture of municipal politics in Canada.

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Methodology

We monitored the 2023 Toronto mayoral by-election for 45 days, from May 13, 2023 at 00:00 ET to June 26, 2023 at 23:59 ET.

We tracked the Twitter mentions of 54 of 102 total mayoral candidates. Candidates that were not tracked did not possess a public or active Twitter account as of the end of the election’s nomination deadline.

Our SAMbot project has tracked abuse in federal, provincial, and municipal races since 2021. However, since data from each election is collected during different time periods, with different lengths, and with different totals of tracked candidates, it is not useful, nor advisable, to compare SAMbot data across elections.

We do not collect data on retweets, as counting the same tweet more than once can distort the analysis. We only evaluate text within a tweet; content such as images, audio, or videos that may spread abuse cannot be evaluated by the machine learning tools that we use.

In our SAMbot project, we use machine learning models to assess abusive language. These models are ever-evolving, which means that during each deployment, our data is more accurate and informed.

Please note that compared to our previous SAMbot deployments, we have evaluated abusive tweets significantly differently in this election.

Our machine learning model makes a confidence prediction to assess whether a tweet should be considered “abusive.” When measuring abuse, our model gives each tweet a score from 0% to 100% for each category, based on how confident it is that the tweet is abusive in nature.

Previously with SAMbot deployments, we used a 51% confidence prediction to evaluate abuse; we have changed to now use a 70% confidence prediction. This change means that our analysis will be more accurate, and that cumulative results will paint a better picture of how abuse is distributed across the entire election and across all candidates. 

Simultaneously, this change also means that some nuanced and subtle forms of abusive language may be missed by our machine learning model, and will make it appear at first glance as if there is comparatively less abusive content present, which is not necessarily the case. Machine learning models will never be able to monitor all abusive language across an election, as the subjective nature of what constitutes “abuse” does not permit the possibility of 100% accuracy. This methodological change allows us to more accurately represent how abuse is distributed overall.

We have made this change as part of our ever-evolving intention to strive for more accurate and ethical methodological practices within the field of social media and machine learning research. Using confidence intervals in this way is in line with recommendations for social science research.

This change makes abuse volumes look considerably lower than in previous elections we have tracked (however, SAMbot data should never be compared across elections regardless). Please consider these significant methodological changes while interpreting the data in this report.

SAMbot deployments use machine learning tools — software applications that run automated tasks. Using machine learning allows us to analyze tweets at a massive scale. Through our SAMbot project, we can evaluate millions of tweets for how likely abusive they are. We track all English and French tweets sent to candidates. Each tweet tracked, whether a reply, quote tweet or mention, was analyzed against five abuse categories using a machine learning tool called Perspective API:

Abuse Category
Abuse Category
Toxicity
A rude, disrespectful, or unreasonable comment that is likely to make people leave a discussion.
Insults
Insulting, inflammatory, or negative comment towards a person or a group of people.
Threats
Describes an intention to inflict pain, injury, or violence against an individual or group.
Identity attacks
Negative or hateful comments targeting someone because of their identity.
Sexually explicit
Contains references to sexual acts, body parts, or other lewd content.

Perspective API provides us with a confidence prediction to assess whether a tweet meets an abuse category. When a tweet is evaluated, it’s given a score from 0% to 100% for each category, based on how certain the machine learning model is that the tweet meets that abuse category. If the tweet is assessed as >=70% likely to meet an abuse category, we determine that the tweet has met the criteria. If a tweet meets at least one of the five abuse categories at the >=70% confidence interval, it is counted as an abusive tweet. The abusive tweet category serves to aggregate all tweets that meet at least one abuse category.

How to Cite

The Samara Centre for Democracy, Engagement and Abuse on Toronto's Digital Campaign Trail: The 2023 Toronto Mayoral By-election Report (Toronto: The Samara Centre for Democracy, 2024), https://www.samaracentre.ca/engagement-and-abuse-on-torontos-digital-campaign-trail-the-2023-toronto-mayoral-by-election-report.

Data Release

For access to the data included in this report, including Twitter engagement data, abuse data, candidate debate attendance, polling results, and more, please refer to the SAMbot 2023 Toronto Mayoral By-election Data Release.

If you have any other questions about our data or the SAMbot project, please reach out to us at hello@samaracentre.ca.

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Exploring engagement, toxicity, and civic conversations across online and offline spaces in the Toronto by-election.

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