Investigating the Role of Communication for Information Seekers’ Trust-Related Evaluations of Health Videos on the Web: Content Analysis, Survey Data, and Experiment
Bibliographic record
Abstract
Background: According to the language expectancy theory and the communication accommodation theory, health information seekers’ trust evaluations of Web-based videos are determined by interplays between content and seekers’ expectations on vloggers’ appropriate language use in specific contexts of Web-based communication. Objectives: Two investigations focused on differences both between vloggers’ language styles and between users’ general trust in specific Web-based platforms to investigate how the context of Web-based communication can be characterized (research question, RQ1). Thereafter, we investigated whether information uncertainty, vloggers’ language style, and context of Web-based communication affect seekers’ trust evaluations of videos (RQ2). Methods: With a content analysis of 36 health videos from YouTube and Vimeo, we examined the extent of trust-related linguistic characteristics (ie, first-person and second-person pronouns). Additionally, we surveyed participants (n=151) on their trust in YouTube and Moodle (academic Web-based platform; RQ1). In an experiment, further participants (n=124) watched a video about nutrition myths and were asked to evaluate the information credibility, vloggers’ trustworthiness, and accommodation of language by vloggers (RQ2). Following a 3 × 2 × 2 mixed design, vloggers’ explanations contained unambiguous (confirming or disconfirming) or ambiguous (neither confirming nor disconfirming) evidence on the myths (within factor). Furthermore, vloggers used YouTube-typical language (many first-person pronouns) or formal language (no first-person pronouns), and videos were presented on YouTube or Moodle (between factors). Results: The content analysis revealed that videos on YouTube contained more first-person pronouns than on Vimeo (F1,35=4.64; P=.04; ηp 2=0.12), but no more second-person pronouns (F1,35=1.23; P=.23). Furthermore, when asked about their trust in YouTube or Moodle, participants trusted YouTube more than Moodle (t150≤−9.63; all P≤.001). In the experiment, participants evaluated information to be more credible when information contained unambiguous rather than ambiguous evidence (F2,116=9.109; P.34). However, participants judged vloggers who used a YouTube-typical language as being more benevolent, and their language use as being more appropriate in both video platforms (F1,117≥3.41; P≤.03; ηp 2≥0.028). Moreover, participants rated the YouTube-typical (vs formal) language as more appropriate for Moodle, but they did not rate one or the other language style as more appropriate for YouTube (F1,117=5.40; P=.01; ηp 2=0.04). Conclusions: This study shows that among specific Web-based contexts, users’ typical language use can differ, as can their trust-related evaluations. In addition, health information seekers seem to be affected by providers’ language styles in ways that depend on the Web-based communication context. Accordingly, further investigations that would identify concrete interplays between language style and communication context might help providers to understand whether additional information would help or hurt seekers’ ability to accurately evaluate information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".