Examining spread of emotional political content among Democratic and Republican candidates during the 2018 US mid-term elections
Bibliographic record
Abstract
Abstract Previous research investigating the transmission of political messaging has primarily taken a valence-based approach leaving it unclear how specific emotions influence the spread of candidates’ messages, particularly in a social media context. Moreover, such work does not examine if any differences exist across major political parties (i.e., Democrats vs. Republicans) in their responses to each type of emotional content. Leveraging more than 7000 original messages published by Senate candidates on Twitter leading up to the 2018 US mid-term elections, the present study utilizes an advanced natural language tool (i.e., IBM Tone Analyzer) to examine how candidates’ multidimensional discrete emotions (i.e., joy, anger, fear, sadness, and confidence) displayed in a given tweet—might be more likely to garner the public’s attention online. While the results indicate that positive joy-signaling tweets are less likely to be retweeted or favorited on both sides of the political spectrum, the presence of anger- and fear-signaling tweets were significantly associated with increased diffusion among Republican and Democrat networks, respectively. Neither expressions of confidence nor sadness had an impact on retweet or favorite counts. Given the ubiquity of social media in contemporary politics, here we provide a starting point from which to disentangle the role of specific emotions in the proliferation of political messages, shedding light on the ways in which political candidates gain potential exposure throughout the election cycle.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".