Vying for votes: a comparison of off- and online election campaign strategies
Bibliographic record
Abstract
Elections are a fundamental part of the political process. Today, election campaigns not only focus on traditional strategies to attract voters but also use social media as a tool. I analyse and compare campaign strategies from three different angles in this thesis. The first paper examines how Get Out The Vote (GOTV) leaflets can influence turnout for the neighbours of households which receive flyers. Focusing on a GOTV campaign during a UK election, I show that spillover effects for party supporters are lower when the share of rival party supporters is high. At the same time, turnout spills over to rival party supporters in mixed partisan neighbourhoods. Turning to online election campaigns, the second paper analyses social media usage from the lens of parties in Switzerland. Using data from party-affiliated Twitter accounts during the 2015 Federal Election, I study how cohesively parties organise their members and how coherent parties’ programmatic messaging is. The results show that smaller-sized and newer parties have higher organisational cohesion and that most parties exhibit low levels of programmatic coherence. Switching the lens to candidates, I analyse social media use by candidates during the 2019 European Parliament elections. The third paper introduces a comprehensive dataset of parties, candidates, and their Facebook and Twitter accounts and describes how the data was collated. To show the range of potential applications of the dataset, I outline an analysis of social media adoption and discuss other research areas in which this data could be useful. The final paper studies whether electoral systems guide if and how individual candidates use social media. The results show that when the electoral system favours person- over party-based campaigning, candidates do not use Twitter more but adapt their communication style to engage voters instead of broadcasting information.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".