Data Collection Interfaces in Online Communities: The Impact of Data Structuredness and Nature of Shared Content on Perceived Information Quality
Bibliographic record
Abstract
The growth of online communities has resulted in an increased availability of user-generated content (UGC). Given the varied sources of UGC, the quality of information it provides is a growing challenge. While many aspects of UGC have been studied, the role of data structures in gathering UGC and nature of to-be-shared content has yet to receive attention. UGC is created in online platforms with varying degrees of data structure, ranging from unstructured to highly-structured formats. These platforms are often designed without regard to how the structure of the input format impacts the quality of outcome. In this study, we investigate the impact of the degree of data structure on the perceived quality of information from the novel perspective of data creators. We also propose and evaluate a novel moderating effect due to the nature of content online users wish to share. The preliminary findings support our claims of the importance of these factors for information quality. We conclude the paper with directions for future research and expected contributions for theory and practice.
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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 | 0.050 | 0.259 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".