Disinformation: a collective, human and technological phenomenon and the different ways to combat it
Bibliographic record
Abstract
Disinformation: a collective, human and technological phenomenon and the different ways to combat itThe World Economic Forum released the Global Risks Report 2024 3 , which identifies potential risks for humanity for the next decade, considering technological acceleration, uncertainties regarding exclusionary economic processes, the complex environmental situation, in addition to conflicts.The vector for building the results of the survey carried out with 1,400 experts in September 2023 had four main structural forces that bring together and trigger a necessary management of systemic risks in the global scenario, namely: 1.Trajectories related to global warming and consequences related to earth systems (climate change); 2.Changes in the size, growth and structure of populations around the world (demographic bifurcation); 3.Development paths for frontier technologies (technological acceleration); 4. Material evolution in the concentration and sources of geopolitical power (geostrategic shifts).In this scenario and linked to the process of technological acceleration, the phenomenon of disinformation comes first among the most serious global risks for the next two years, ranking fifth when considering the ten years ahead.The concern that spreads throughout multiple dimensions of life in society, including environmental and scientific issues, has as its main core, between 2024 and 2025, the political electoral scenario in several countries.The main concerns focus on the indiscriminate use of artificial "intelligence" in disinformation narratives that have been upgraded and now have greater impact in society.The risks involve greater manipulation and control of public opinion, in addition to the potentialization of a process of massive alienation, thinking with Freud (2020, p.50) for whom "a group is extraordinarily credulous and open to influence, it has no critical faculty, and the improbable does not exist for it.It thinks in images, which call one another up by association [...]".The current mediatization processes constitute a central axis for thinking about misinformation in 21st century societies.The multiplication of screens and content are intertwined with practices and discourses in hybrid and convergent ecosystems.Thus, it is pertinent to systematize some structural issues that function as anchor points of the contemporary scenario.Firstly, disinformation processes are part of the convergence of new and old cultural industries that are the product of creative work and are characterized by a twofold 1 PhD, Communicational Processes (UMESP-BRAZIL).Post-Doctor, Communication and Culture (
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".