Social Media, Sentiments and Political Discourse ? An Exploratory Study of the 2021 Canadian Federal Election

Social media are widely used for online political discourse. Opinions shared on social media have different sentiments associated with them. Given the very high adoption rates of X (formerly known as Twitter) among adults, those who share their opinions on X not only represent a sizable segment of the society, but also influence (through emotion contagion) an even larger segment who are passive (non-contributing) users of the platform. Furthermore, the discourse that is initiated on X typically spreads to other more traditional media. As a result, X is influential, which makes it useful to understand the factors related to the sentiments expressed in tweets. Such understanding can help policymakers to take actions that align with public needs and priorities. This research focuses on identifying the drivers (keywords) of sentiments associated with political discourse on X. We also explore virality, i.e., how much a message (the tweet) spreads, and the relationship between sentiments and virality. Finally, we explore whether the clustering of tweets among sentiment and virality groups can improve the potential of social media content for predicting election results. Sentiment Analysis of 764,000 tweets related to the 2021 Canadian Federal election was followed by text clustering to identify sentiment-driving topics. We found some keywords predominantly present within a positive or negative sentiment that are suggestive of entities or ideas to invest in or mitigate by political decision makers. We were also able to find partial evidence for “negativity bias” by detecting a negative relationship between sentiment (positivity) and virality (number of retweets). Finally, we demonstrated that high positivity on the political discourse does not reflect election outcomes and examining X content in more neutral groups can improve predictive power. Our findings have implications for political decision makers and social media analytics researchers.