Bibliographic record
Abstract
Purpose The purpose of this paper is to treat disinformation and misinformation (intentionally deceptive and unintentionally inaccurate misleading information, respectively) as a socio-cultural technology-enabled epidemic in digital news, propagated via social media. Design/methodology/approach The proposed disinformation and misinformation triangle is a conceptual model that identifies the three minimal causal factors occurring simultaneously to facilitate the spread of the epidemic at the societal level. Findings Following the epidemiological disease triangle model, the three interacting causal factors are translated into the digital news context: the virulent pathogens are falsifications, clickbait, satirical “fakes” and other deceptive or misleading news content; the susceptible hosts are information-overloaded, time-pressed news readers lacking media literacy skills; and the conducive environments are polluted poorly regulated social media platforms that propagate and encourage the spread of various “fakes.” Originality/value The three types of interventions – automation, education and regulation – are proposed as a set of holistic measures to reveal, and potentially control, predict and prevent further proliferation of the epidemic. Partial automated solutions with natural language processing, machine learning and various automated detection techniques are currently available, as exemplified here briefly. Automated solutions assist (but not replace) human judgments about whether news is truthful and credible. Information literacy efforts require further in-depth understanding of the phenomenon and interdisciplinary collaboration outside of the traditional library and information science, incorporating media studies, journalism, interpersonal psychology and communication perspectives.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".