Recognizing Campaign Effects on Social Media: A Computerized Text Analysis of the 2015 Canadian General Election on Facebook
Bibliographic record
Abstract
Previous research demonstrates that traditional campaign strategies such as door-to-door canvassing and advertisement have minimal persuasive effects on voters’ political attitudes and vote choice while simultaneously demonstrating strong activation effects on voters’ existing preferences. From this literature, numerous theoretical perspectives on campaign contact have emerged. The most predominant is the minimal effects thesis, which posits that campaigns have minimal effect influencing voters’ political attitudes, vote choice, and consequently, election outcomes. In contrast, the activation effects thesis posits that campaigns are consequential to election outcomes because campaign contact activates voters’ existing political preferences and mobilizes the electorate to vote. This thesis proposes to reconcile the two theoretical perspectives by demonstrating that the same type of campaign contact may have both minimal persuasive effects on voters’ political preferences and strong activation effects on voters’ emotions. The thesis hypothesizes then that campaign contact evokes emotional responses that encourage rather than discourage voting. To this end, the thesis examines campaign effects online from a unique dataset queried from Facebook consisting of federal party leaders’ campaign messages (N = 1,711) and the responses to those messages from everyday Facebook users (n = 92,813) during the 2015 Canadian general election campaign. Computational social science methods are employed to directly measure campaign contact’s persuasive and activation effects on partisan and nonpartisan Facebook users. The results demonstrate that campaign contact online has a minimal persuasive effect on Facebook users’ self-expressed political preferences as well as strong activation effects on those preferences. Activation effects manifest as emotional responses that are most pronounced when individuals react to attitude-divergent rather than attitude-consistent campaign messaging. Exposure to attitude-divergent contact evokes Facebook users to experience discrete negative emotions such as anger, which previous research has shown to increase the electorate’s propensity to vote. The efficacy of negative emotions, however, may incentivize political parties to adopt strategies that demonize political opponents and which may, therefore, contribute to negative partisanship online.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".