Thematic Discrepancy Analysis: A Method to Gain Insights into Lurkers and Test for Non-Response Bias
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it