Thematic Discrepancy Analysis: A Method to Gain Insights into Lurkers and Test for Non-Response Bias
Bibliographic record
Abstract
Word of mouth (WOM), long recognized as a highly influential source of information, has taken on new importance with the proliferation of online WOM. Research in online environments has focused on individuals who actively participate in generating WOM. However, over 90% of those that read WOM are non-participants, commonly called “lurkers.” This paper develops and tests a thematic discrepancy analysis (TDA) approach that combines commonly available information on Views and Replies with content analysis to provide new insights into differences between WOM participants and lurkers. TDA provides managers with market-sensing information to identify hidden opportunities and threats, as well as to test for non-response bias. Given the lack of approaches to address non-response bias due to lurkers, TDA represents a significant contribution to research methodology. We demonstrate the efficacy of TDA by applying it to a large scale WOM dataset containing over 80,000 messages from a brand-specific online forum.
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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 | 0.144 | 0.380 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".