Damage Control: How Campaign Teams Interpret and Respond to Online Incivility
Bibliographic record
Abstract
Social media are critical to election campaigns, but they also expose candidates to incivility and abuse. While there is a growing literature on online incivility faced by politicians, little is known about how campaign teams interpret and respond to it. To address that gap, we analyze in-depth interviews with 31 candidates and campaign staff from the 2019 federal election in Canada. We find that campaign teams interpret incivility according to the intensity of messages’ content, but also their frequency, source, and target. They use these criteria to assess potential harms in three areas: security and psychological wellbeing, strategic campaign activities, and inclusive democratic discourse. Based on these assessments, campaign teams use a limited set of platform affordances to ignore, monitor, engage, or block uncivil voices. Our analysis shows that interpretations of incivility are more nuanced and multi-dimensional than most scholarship recognizes. We also reveal the often-hidden labor that campaign teams devote to content moderation, as they try to balance protecting themselves, defending their campaign messaging, and creating space for civil discussion. By paying closer attention to campaign teams’ mediation and moderation of online incivility, scholars can better understand its consequences for democratic political participation in elections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".