Responses to common arguments. We use data scraped from Grist this to train the detection of Climate Misinformation.
As the UK takes COP26 Presidency in 2021, the AI and data science sector is ramping up its research across environmental sciences and the use of technology to forecast and mitigate against climate change. Howard Covington, Chair of the Board of Trustees at The Alan Turing Institute opens this important theme.
Understanding and countering misinformation about climate change by John Cook, George Mason University, USA
While there is overwhelming scientific agreement on climate change, the public have become polarized over fundamental questions such as human-caused global warming. Communication strategies to reduce polarization rarely address the underlying cause: ideologically-driven misinformation disseminated through outlets such as social and mainstream media. In order to effectively counter online misinformation, we require foundational frameworks that provide comprehensive understanding of the techniques employed in climate misinformation, as well as inform evidence-based approaches to neutralizing misinforming content. This chapter reviews analyses of climate misinformation, outlining a range of denialist arguments and fallacies. Identifying and deconstructing these different types of arguments is necessary to design appropriate interventions that effectively neutralize the misinformation.
Interview with Professor Mike Rogerson Glaciers are melting, tropical storms are changing and some pacific islands are already under water… but to some the idea of man-made climate change is just a worldwide conspiracy to claim your tax dollars.
Climate Explained is a collaboration between The Conversation, Stuff and the New Zealand Science Media Centre to answer your questions about climate change.
The world is facing the challenge of climate crisis. Despite the consensus in scientific community about anthropogenic global warming,the web is flooded with articles spreading climate misinformation. These articles are care-fully constructed by climate change counter movement (CCCM) organizations to influence the narrative around climate change. We revisit the literature on climate misinformation in social sciences and repackage it to introduce in the community of NLP. Despite considerable work in detection of fake news, there is no misinformation dataset available that is specific to the domain.of climate change. We try to bridge this gap by scraping and releasing articles with known climate change misinformation.
- Plugins that you can download in your browser that tell you what is credible or not
- Algorithms in social media platforms that tell what stories are true or not true
- 'Our Verdict' on false image, misinformation etc. classify the verdict
- More that people are aware the better
- Fact check claim unsure of (ie. google Climate Misinformation fact check)
- False images
- 'Very easy to spread misinformation to millions of people so rapidly'
- Journalists can be victims of misinformation
- When overwhelmed by information, scared or anxious, we do not process complexity well making us more susceptible to bad information
- Validate the source to decide 'trust'
- Facts alone do not help the public understand social problems or drive them to take action.
- But facts do matter if framed well
- Balance an explanation of the problem with solutions-oriented discussions.
- Communications that explain why social or scientific phenomena matter, how they work, and what needs to be done to address them.
- Appealing to certain values—cultural beliefs and ideals—encourages people to think about social problems in new and productive ways.
This dataset contains the tweet ids of 39,622,026 tweets related to climate change. They were collected between September 21, 2017 and May 17, 2019 from the Twitter API using Social Feed Manager. There is a gap in data collection between January 7, 2019 and April 17, 2019.
A team of independent fact checkers and campaigners who find, expose and counter the harm it does.
This dataset uses Grist questions.
Skim API uses machine learning and natural language processing to instantly search, extract and identify key information from unstructured web pages.