2023 · iScience
Conspiracy spillovers and geoengineering
Verdict
UNSCIENTIFIC
Neither the paradigm nor the study inside it passes. The verdict is unscientific.
1·Study methodology · inside the paradigm
UNSCIENTIFIC
Does not follow the scientific method within Institutional Consensus.
2·Paradigm · Institutional Consensus
UNSCIENTIFIC
Fails the tests. The worse layer decides.
How the verdict is decided
The verdict grades both layers: the paradigm this study assumes, and how the study was carried out inside it. Both must be scientific for the whole thing to be scientific. A clean method inside an unscientific paradigm is unscientific. An unscientific method inside a better paradigm is unscientific. A split on either layer is mixed. The worse layer decides.
1·Study methodology
Did this study test its claim with methods that are independent, falsifiable, and non-circular?
UNSCIENTIFIC
This paper claims conspiracy theories, especially chemtrails, dominate public reactions to solar geoengineering on Twitter, spillover across countries and into broader political movements, and that online toxicity amplifies anti-SRM sentiment. All three tests fail.
Independently Verifiable
Fail
The tweets are real, but every headline finding is a score from an emotion dictionary and a toxicity classifier the authors fed the text through.
Falsifiable
Fail
Whatever the hashtag networks showed, the reading would be conspiracy spillover, and speech lacking conspiracy terms would be called coded language.
Non-Circular
Fail
The authors picked which hashtags count as conspiracy, then reported that conspiracy content dominates the debate.
Why
The tweets exist and anyone can count them. The findings do not live in the counts. The authors pushed 814,924 tweets through an emotion dictionary that assigns words to feelings, a machine-trained classifier that assigns each tweet a toxicity number between 0 and 1, and their own list of conspiracy hashtags. Then they reported what those tools said: emotions reversed after project launches, toxicity correlated with volume, conspiracies dominate. A person who believes chemtrails are real reads the same tweets and sees concerned citizens, not conspiracy theorists. The numbers on the graphs are classifier outputs, and the meaning arrives entirely from the authors' framework. Strip the classifiers and the claim disappears with them.
2·Paradigm · Institutional Consensus
Does the framework this study assumes pass the three tests?
UNSCIENTIFIC
Independently Verifiable
Fail
The tweets are visible; 'conspiracy' and 'toxic' are labels the framework applies to speech it has already judged.
Falsifiable
Fail
If the speech were polite and free of conspiracy terms, the field would call it coded language or astroturfing, so no dataset can clear the population.
Non-Circular
Fail
The framework names the false belief first, then grades millions of speakers against that name and reports the belief is widespread.
Why
The framework decides in advance which beliefs are false and which speech is disordered. Here the authors fixed the truth about chemtrails, treated every user of the hashtag as a conspiracy participant, and graded 13 years of public speech against that standard. A stranger sees words. The framework sees conspiracy, toxicity, spillover. If the speech had been polite, the field would call it coded language or manufactured consensus. If chemtrails had vanished, HAARP and weather modification would carry the label instead, and in this paper they already do. No dataset can acquit a population the framework has classified before counting. The text is visible. The diagnosis is the model.
From the paper
The emotion scores are estimated using a lexicon-based approach (NRC-lexicon) with the following embedded emotions in each category: positive emotion (joy, trust, and optimism), negative emotion (disgust, sadness, fear, and anger), and neutral emotion (anticipation and surprise)
We find a significant correlation ( R 2 = 8.12 , 99% CI) between an increase in daily Twitter volume on SG and an increase in toxicity scores.
Thus, the SG debate on Twitter is dominated by conspiracy theories and leverages local political tensions to amplify its contagion and create a spillover effect