• for: polycrisis, Trumpism, Chaos, distraction, climate crisis, climate communication, complexity, adjacency climate change fossil fuel industry, adjacency climate change big oil, adjacency climate change politics big oil, quote adjacency climate change fossil fuel industry, quote adjacency climate change big oil
• key insight
  • claim
    • One big reason that big oil is funding GOP to keep the chaotic Trump story as the main headline is to foster distraction from climate change impacts
    • big news story in the US is Donald Trump and the election, climate change impacts of extreme weather is minimized
    • the distraction of politics from a chaotic GOP is perfect distraction for the masses to ignore climate change and for big oil to continue BAU
• paraphrase
• quote
  • when you take a step back and look at that part of the distraction and the chaos that Trump and these GOP trolls deliver
  • it's it's a wonderful Boon for the oil and gas industry and the Koch brothers and the guys that fund these campaigns and the federal Federalist Society that's owning the Supreme Court
  • they want to keep doing business as usual and the easiest way to do that is
    • to have this big chaotic GOP that ignores climate change and
    • to play into what they want
      • the mainstream media not focusing more on climate change let alone making those two connections
    • a lot of mainstream media is scared to make that connection because oil companies are paying the bills of CNN and every other network
• author
  • Noel Casler

In the co-share network, a cluster of websites shared more by conservatives is also shared more by users with higher misinformation exposure scores.

Nodes represent website domains shared by at least 20 users in our dataset and edges are weighted based on common users who shared them. a Separate colors represent different clusters of websites determined using community-detection algorithms29. b The intensity of the color of each node shows the average misinformation-exposure score of users who shared the website domain (darker = higher PolitiFact score). c Nodes’ color represents the average estimated ideology of the users who shared the website domain (red: conservative, blue: liberal). d The intensity of the color of each node shows the average use of language toxicity by users who shared the website domain (darker = higher use of toxic language). e The intensity of the color of each node shows the average expression of moral outrage by users who shared the website domain (darker = higher expression of moral outrage). Nodes are positioned using directed-force layout on the weighted network.

Exposure to elite misinformation is associated with the use of toxic language and moral outrage.

Shown is the relationship between users’ misinformation-exposure scores and (a) the toxicity of the language used in their tweets, measured using the Google Jigsaw Perspective API27, and (b) the extent to which their tweets involved expressions of moral outrage, measured using the algorithm from ref. 28. Extreme values are winsorized by 95% quantile for visualization purposes. Small dots in the background show individual observations; large dots show the average value across bins of size 0.1, with size of dots proportional to the number of observations in each bin. Source data are provided as a Source Data file.

Exposure to elite misinformation is associated with sharing news from lower-quality outlets and with conservative estimated ideology.

Shown is the relationship between users’ misinformation-exposure scores and (a) the quality of the news outlets they shared content from, as rated by professional fact-checkers21, (b) the quality of the news outlets they shared content from, as rated by layperson crowds21, and (c) estimated political ideology, based on the ideology of the accounts they follow10. Small dots in the background show individual observations; large dots show the average value across bins of size 0.1, with size of dots proportional to the number of observations in each bin.

We applied two scenarios to compare how these regular agents behave in the Twitter network, with and without malicious agents, to study how much influence malicious agents have on the general susceptibility of the regular users. To achieve this, we implemented a belief value system to measure how impressionable an agent is when encountering misinformation and how its behavior gets affected. The results indicated similar outcomes in the two scenarios as the affected belief value changed for these regular agents, exhibiting belief in the misinformation. Although the change in belief value occurred slowly, it had a profound effect when the malicious agents were present, as many more regular agents started believing in misinformation.

Jones, B., Wardman, L., & Tinkler, L. (2021, September 3). Coronavirus vaccine hesitancy in younger adults—Office for National Statistics. https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/healthandwellbeing/articles/coronavirusvaccinehesitancyinyoungeradults/june2021

Epstein, Z., Sirlin, N., Arechar, A. A., Pennycook, G., & Rand, D. (2021). Social Media Sharing Reduces Truth Discernment. PsyArXiv. https://doi.org/10.31234/osf.io/q4bd2

Kington, R. S., Arnesen, S., Chou, W.-Y. S., Curry, S. J., Lazer, D., & Villarruel, and A. M. (2021). Identifying Credible Sources of Health Information in Social Media: Principles and Attributes. NAM Perspectives. https://doi.org/10.31478/202107a

The YouTubers who blew the whistle on an anti-vax plot—BBC News. (n.d.). Retrieved July 28, 2021, from https://www.bbc.co.uk/news/blogs-trending-57928647?at_custom4=23264FBA-EE08-11EB-9330-21BB96E8478F&at_medium=custom7&at_custom3=%40BBCWorld&at_custom2=twitter&at_custom1=%5Bpost+type%5D&at_campaign=64

It’s too soon to declare vaccine victory—Four strategies for continued progress | TheHill. (n.d.). Retrieved May 12, 2021, from https://thehill.com/opinion/healthcare/552219-its-too-soon-to-declare-covid-vaccine-victory-four-strategies-for

Ogbunu, B. C. (2020, October 27). The Science That Spans #MeToo, Memes, and Covid-19. Wired. https://www.wired.com/story/the-science-that-spans-metoo-memes-and-covid-19/

Face Masks, Voting, and Social Media | Human Resources | The University of Texas at Austin. (n.d.). Retrieved October 27, 2020, from https://hr.utexas.edu/learning-development/programs/face-masks-voting-and-social-media

Adelani, D. I., Kobayashi, R., Weber, I., & Grabowicz, P. A. (2020). Estimating community feedback effect on topic choice in social media with predictive modeling. EPJ Data Science, 9(1), 1–23. https://doi.org/10.1140/epjds/s13688-020-00243-w

Pastor-Escuredo, D., & Tarazona, C. (2020). Characterizing information leaders in Twitter during COVID-19 crisis. ArXiv:2005.07266 [Physics]. http://arxiv.org/abs/2005.07266

Orben, A., Tomova, L., & Blakemore, S.-J. (2020). The effects of social deprivation on adolescent social development and mental health [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/7afmd