Evolution Of Intelligence Theories: The Evolution Of
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In the top panel, the x-axis represents time while the y-axis shows important words. Color represents the importance of words, with darker color indicating higher importance.
In the bottom panel are word clouds for each topic. Word size corresponds to word weight higher weighted words appear larger. Credit: Los Alamos National Laboratory A new machine-learning program accurately identifies COVIDrelated conspiracy theories on social media and models how they evolved over time—a tool that could someday help public health officials combat link online.
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Because people tend to believe the first message they encounter, public health officials could someday monitor which conspiracy theories are gaining traction on social media and craft factual public information campaigns to preempt widespread acceptance of falsehoods. The four themes the study examined were that 5G cell towers spread the virus; that the Bill and Melinda Gates Foundation engineered or has otherwise malicious intent related to COVID; that the virus was bioengineered or was developed in a laboratory; Evolution Of Intelligence Theories: The Evolution Of that the COVID vaccines, which were then all still in development, would be dangerous. The study showed that misinformation tweets contain more negative sentiment when compared to factual tweets and that conspiracy theories evolve over time, incorporating details from unrelated conspiracy theories as Evolutino as real-world events.
For example, Bill Gates participated in a Reddit "Ask Me Anything" in NItelligencewhich highlighted Gates-funded research to develop injectable invisible ink that could be used to record vaccinations.
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Immediately after, there was an increase in the prominence of words associated with vaccine-averse conspiracy theories suggesting the COVID vaccine would secretly microchip individuals for population control. Furthermore, the study found that Theoroes: supervised learning here could be used to automatically identify conspiracy theories, and that an unsupervised learning approach dynamic topic modeling could be used to explore changes in word importance among topics within each theory.
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