Misinformation Analysis and Online Quality Theory (A Wittgensteinian Approach)
Bibliographic record
Abstract
Online platforms initially left content consumers to discern for themselves whether information online was true or false. Censoring of content by online platforms and fact-checking are presently the two prominent interventions. We propose here that misinformation analysis should aim to make clear what is stated by clarifying the propositions and claims in such content (declarative language/factual discourse). The early work of Ludwig Wittgenstein is relevant for such analysis. Presented here is an online content information quality check model for written (non-graphical) content. This hypothesis-driven intervention can be applied to Web browsers (as extensions) and online social media platforms. This model is inspired by Wittgenstein’s book Tractatus Logico-Philosophicus. Our hypothesis is that rating and labelling online content this way will help users discern content qualitatively (avoid being misinformed) and engage better with other users. This Wittgensteinian model (set of rules/quality check/algorithm) can also be viewed as a tentative theory of information quality anticipating future natural language processing (NLP) technology more effective against online misinformation. We introduce two new concepts: “off-information” and “non-information” as distinct information disorder variants.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".