The Line Between Politics and Conspiracy Theories: Tracking Disinformation using #StopTheGreatReset
Bibliographic record
Abstract
Have you heard? In today’s pandemic, the Trudeau administration has been using the widespread lockdowns to impose socialism in Canada. This conspiracy theory has been mobilized under the hash tags #StopTheGreatReset, #Scamdemic and #CancelTheLockdown amongst others. With the COVID-19 pandemic, as with previous major events, there has been an influx of dis-and mis- information on social media platforms. This rapid spread of information can have strong influences on people’s behaviour which can impact the effectiveness of public health measures taken by governments (Cinelli et al. 2020; González-Padilla andTortolero-Blanco 2020). My research is part of an ongoing project that aims to identify and map the spread of disinformation, and its effects on Canadian society. For this sub-project, I created a database of social media posts from Twitter accounts that promote or spread disinformation narratives directed towards Canadian politics and public health measures. From this, we were able to identify some of the most common narratives of disinformation in circulation on Twitter; the hash tag #StopTheGreatReset was chosen as the focus of the project to study the fine, and often blurred, line between legitimate politics and conspiracy theories. Going forth, my aim is to conduct a qualitative analysis on the links attached to social media posts fueling disinformation to understand what kinds of information are being circulated and identify common themes. This project has been an opportunity for me to learn about how social media research is conducted and allows me to engage with urgent issues in contemporary media culture.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".