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Researchers use AI to analyze tweets debating vaccination and climate change

#artificialintelligence

Using artificial intelligence (AI) researchers have found that between 2007 and 2016 online sentiments around climate change were uniform, but this was not the case with vaccination. Climate change and vaccinations might share many of the same social and environmental elements, but that doesn't mean the debates are divided along the same demographics. A research team from the University of Waterloo and the University of Guelph trained a machine-learning algorithm to analyze a massive number of tweets about climate change and vaccination. The researchers found that climate change sentiment was overwhelmingly on the pro side of those that believe climate change is because of human activity and requires action. There was also a significant amount of interaction between users with opposite sentiments about climate change.


Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis

#artificialintelligence

Background: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity. Objective: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter. Methods: We collected 31,100 English tweets containing COVID-19 vaccine–related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia. Results: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion. Conclusions: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.


Artificial intelligence may be set to reveal climate-change tipping points

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Researchers are developing artificial intelligence that could assess climate change tipping points. The deep learning algorithm could act as an early warning system against runaway climate change. Chris Bauch, a professor of applied mathematics at the University of Waterloo, is co-author of a recent research paper reporting results on the new deep-learning algorithm. The research looks at thresholds beyond which rapid or irreversible change happens in a system, Bauch said. "We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point," Bauch said. "Many of these tipping points are undesirable, and we'd like to prevent them if we can."


Artificial intelligence may be set to reveal climate-change tipping points

#artificialintelligence

Chris Bauch, a professor of applied mathematics at the University of Waterloo, is co-author of a recent research paper reporting results on the new deep-learning algorithm. The research looks at thresholds beyond which rapid or irreversible change happens in a system, Bauch said. "We found that the new algorithm was able to not only predict the tipping points more accurately than existing approaches but also provide information about what type of state lies beyond the tipping point," Bauch said. "Many of these tipping points are undesirable, and we'd like to prevent them if we can." Some tipping points that are often associated with run-away climate change include melting Arctic permafrost, which could release mass amounts of methane and spur further rapid heating; breakdown of oceanic current systems, which could lead to almost immediate changes in weather patterns; or ice sheet disintegration, which could lead to rapid sea-level change.


Evidence of distrust and disorientation towards immunization on online social media after contrasting political communication on vaccines. Results from an analysis of Twitter data in Italy

arXiv.org Machine Learning

Background. Recently, In Italy the vaccination coverage for key immunizations, as MMR, has been declining, with measles outbreaks. In 2017, the Italian Government expanded the number of mandatory immunizations establishing penalties for families of unvaccinated children. During the 2018 elections campaign, immunization policy entered the political debate, with the government accusing oppositions of fuelling vaccine scepticism. A new government established in 2018 temporarily relaxed penalties and announced the introduction of flexibility. Objectives and Methods. By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public. Results. The population appeared to be mostly composed by "serial twitterers" tweeting about everything including vaccines. Tweets favourable to vaccination accounted for 75% of retained tweets, undecided for 14% and unfavourable for 11%. Twitter activity of the Italian public health institutions was negligible. After smoothing the temporal pattern, an up-and-down trend in the favourable proportion emerged, synchronized with the switch between governments, providing clear evidence of disorientation. Conclusion. The reported evidence of disorientation documents that critical health topics, as immunization, should never be used for political consensus. This is especially true given the increasing role of online social media as information source, which might yield to social pressures eventually harmful for vaccine uptake, and is worsened by the lack of institutional presence on Twitter. This calls for efforts to contrast misinformation and the ensuing spread of hesitancy.