INTRODUCING SOCIAL SUPPORT THEORY TO POLITICAL COMMUNICATION: AN ASSESSMENT OF THE COMMUNICATION DYNAMICS OF POLITICAL SOCIAL SUPPORT AND ITS EFFECTS
Bibliographic record
Abstract
There is a separation between interpersonal political communication research and traditional interpersonal communication theory. The current study bridges this divide by introducing social support theory to political communication. Social support has been shown to aid coping, build self-efficacy, and enhance psychological well-being. It is argued that politics can be a source of stress and individuals exchange political social support (PSS) when facing challenges in their political environments. The current study defines the concept of PSS and its applications in political communication through an initial survey-based proof of concept study and an experiment. The first study, a nationwide cross-sectional survey (N = 2004), was conducted through a Qualtrics panel in September 2018 to assess the internal structure of providing and receiving PSS in citizen-to-citizen relationships. A little more than a quarter of the sample (n = 563, 28.1%) reported having received some type of PSS in the past 18 months, and an even larger percentage (n = 728, 36.3%) reported providing PSS during the same time period. Results from a cross-sectional survey reveal a solid percentage of U.S. adults exchanging PSS across many channels (e.g., face-to-face, social media) with a wide range of political phenomena sparking these communicative activities. Building on the survey’s gender and political-specific communicative dynamics, the second study, an online experiment, was conducted in October 2021. In the online experiment, a 2 (politician’s gender: male vs. female) x 2 (politician’s party identification: Democrat vs. Republican) x 4 (message levels) x 2 (citizen’s gender: Male vs. female) x 2 (citizen’s party identification: Democrat vs. Republican) between-subject design, provides a theoretical rationale on how the gender and the political identification in citizen-to-politician relationships are associated with the level of perceived social support. Results from the experiment reveal that the level of social support messages that include different numbers of social support components does not have an effect on the level of perceived PSS. However, the characteristics of politicians and the gender of politicians and citizens were significant in predicting the higher PSS. Theoretical and practical implications for the theory advancement and future research are explored.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".