US public opinion

Table 1 and Fig. 1 presented above are based on survey data collected via the CCES of the US electorate, which was conducted in October and November 2016 by YouGov/Polimetrix (YP). Administered online, it gathered a nationally stratified sample of more than 36,000 respondents. The “chemtrails” question was part of one of eighteen additional 1,000-subject pre-election studies. It came at the end of a 20-minute survey, with the latter 10 min focused on solar geoengineering.Footnote 2 Prior questions, thus, increased familiarity with solar geoengineering beyond the general public. Mahajan et al. (2017) analyzes the CCES results more broadly and provides information on general attitudes toward solar geoengineering use and research.

Online social media discourse

We use the social media analysis platform Crimson Hexagon to analyze the totality of English tweets for the decade from May 2008 through May 2017, in addition to mentions in public posts on Facebook, YouTube, Google Plus, Tumblr, and other blogs, online forums, reviews, comments, and news items. Twitter comprises the majority (77%) of the over 5 million relevant English posts, far ahead of Tumblr (4%), Facebook (3%), YouTube (3%), and Google Plus (<1%). Crimson Hexagon employs both a supervised learning method (Hopkins and King, 2010) and automated sentiment analysis of relevant online discourse on geoengineering.

“Relevant” posts include all public English posts mentioning at least one of eleven terms: “climate engineering” and “geoengineering” broadly; “solar geoengineering,” “solar radiation management” and its prominent abbreviation “SRM”, and “albedo modification” more specifically; “stratospheric aerosol injection”, “marine cloud brightening”, and “cirrus cloud thinning” as the three most promising and most commonly discussed methodologies; and, lastly, “chemtrails” and “HAARP” to zero in on the most commonly used terms in conjunction with the chemtrails conspiracy theory. We did not explicitly include word fragments or common misspellings, as Crimson Hexagon’s ‘guided’ categorization algorithm accounts for partial word mentions and detects misspellings.

Crimson Hexagon’s most significant advantage is its ‘‘guided’’ but otherwise automatic categorization of public posts into pre-determined categories (Hopkins and King, 2010). We chose five such categories, training the algorithm to categorize posts found via the eleven search terms into one of five groups: neutral science reporting (“neutral”); posts emphasizing unintended consequences and otherwise portraying geoengineering in a negative light without being conspiratorial (“negative”); posts emphasizing the potential positive impact and otherwise portraying geoengineering in a positive light (“positive”); posts espousing or otherwise helping to spread the chemtrails conspiracy (“chemtrails”); and off-topic posts, despite their mentioning one of the eleven keywords (“off-topic”). We trained posts to each category that had any presence in the data, and then used the built-in ReadMe algorithm to estimate the population proportions belonging to each category on the universe of posts fitting the keyword criterion.

A limitation of Crimson Hexagon’s Facebook data is the exclusive focus on public posts, missing “echo chambers” (Vicario et al., 2016) created in private online conversations among Facebook ‘friends’. On Twitter, Crimson Hexagon attempts to filter out bots, though some may well be included in the final analysis.

The sentiment analysis presented below takes advantage of Crimson Hexagon’s automated sentiment analysis, capturing “positive,” “negative,” and “neutral” attitudes toward a particular topic. The algorithm is able to pick up on sentiments conveyed in a post to categorize them, going well beyond mere keyword searches. For example, a tweet on 16 August 2016, saying “Expert consensus: Chemtrails aren’t actually a thing” is correctly categorized as science reporting, while a tweet saying “#Chemtrails Caldeira comes clean on chemtrails” is correctly categorized as chemtrails conspiracy. Some others are difficult to judge. For example, Crimson Hexagon categorizes a tweet saying “#Cloudseeding long-term, would negatively impact ecosystems left thirsty” as negative portrayal. Further inspection could also indicate it should be in the chemtrails conspiracy category, though either category might fit. Examples of off-topic mentions include tweets that use “SRM” in an entirely different context, for example, as abbreviation for “supplier relationship management”. Crimson Hexagon categorizes those correctly as off-topic.

Fig. 2 Monthly geoengineering monitor categories on Twitter, Facebook and other social media platforms, May 2008–17, using Crimson Hexagon’s supervised learning methods (Hopkins and King, 2010) to categorize social media discourse as “chemtrails” (61% of total), ”negative” (8%), “neutral” science reporting (6%), “positive” (<1%), and off-topic (25%) Full size image

Fig. 3 Monthly basic geoengineering sentiment trend on Twitter, Facebook and other social media platforms, May 2008–17, using Crimson Hexagon’s automated sentiment analysis Full size image

Findings

The vast majority of social media posts falls into the chemtrails conspiracy camp (61%), neutral science reporting is in the clear minority (6%), slightly trumped by negative portrayal (8%), with 25% of posts being off-topic. Positive portrayal barely registered at <1% (Fig. 2).

Fig. 4 Monthly basic geoengineering sentiment trend excluding mentions of the chemtrails conspiracy on Twitter, Facebook, and other social media platforms, May 2008–17, using Crimson Hexagon’s automated sentiment analysis Full size image

Automated sentiment analysis classifies social media mentions of geoengineering into positive, negative, and neutral categories. Figure 3 shows all social media mentions of geoengineering and assorted search terms by month from May 2008 through May 2017, including mentions of chemtrails. Figure 4 excludes chemtrails mentions. Both figures reveal a general upward trend, heavily influenced by single events. January 2015 saw the publication of the US National Academy of Science’s comprehensive set of reports on carbon and solar geoengineering (NRC, 2015a, b), leading to a spike of online discourse with and without the conspiracy theory. Similarly, the spike in April and May 2017 can be linked to the formal launch of Harvard’s Solar Geoengineering Research Program and associated media mentions (e.g., Gertner, 2017; Greenfieldboyce, 2017; Porter, 2017).Footnote 3

The proportion of non-chemtrails conspiracy social media mentions from May 2008 through May 2017 (Fig. 4) among total mentions (Fig. 3) shows no discernible trend. The ratio ranges from 18%, in March 2011, to 70%, in September 2009, with an absolute-value t-statistic of 0.61 when testing the H 0 of whether the slope of the trend line was statistically significantly different from zero. It is not. The chemtrails conspiracy appears to grow hand-in-hand with the general increase in social media discourse around geoengineering.

We compare the results between all geoengineering-focused posts (Fig. 3) to those without chemtrails (Fig. 4) instead of focusing on a chemtrails-only monitor (Fig. 5). The latter would skew results, as it excludes geoengineering posts not mentioning “chemtrails” that Crimson Hexagon’s learning mechanism subsequently classifies as pertaining to the conspiracy. It also captures too many posts that would be “off-topic” or “neutral” science reporting despite mentioning the term “chemtrails.” While Fig. 5 shows some of the same overall trends—e.g., the spike in posts in May 2015—the total numbers do not conform to the difference between all solar geoengineering-focuses posts (Fig. 3) and those without chemtrails (Fig. 4).

Fig. 5 Monthly basic geoengineering sentiment trend focused only on chemtrails conspiracy only on Twitter, Facebook, and other social media platforms, May 2008–17, using Crimson Hexagon’s automated sentiment analysis Full size image

Basic “positive” and “negative” sentiments, meanwhile, have shifted significantly. Among all social media mentions including “chemtrails” (Fig. 3), around 33% of tweets displayed a “negative” sentiment during the first 12 months of our analysis spanning mid-2008 to 2009, declining to 18% for the final 12 months from mid-2016 to 2017. That reflects a statistically significant decrease of 1.7% per year (t-statistic = 11.5). During the same time period, overall positive sentiment shows no significant trend, staying near-constant at 9.4% throughout (t-statistic = 0.92). Excluding tweets categorized as propagating the chemtrails conspiracy (Fig. 4), those classified by Crimson Hexagon’s automated sentiment analysis to have positive sentiment decreased slightly from 10.4 to 8.1% over the course of the decade, while those displaying negative sentiment decreased significantly from 30.9% to 13.0% (t-statistics = 3.8 and 9.4, respectively). Note that this only reflects relative sentiment and does not in itself convey greater acceptance of solar geoengineering over the course of the past decade. It does imply that online discourse on solar geoengineering more broadly happens in emotionally more neutral ways, despite the absolute dominance of the chemtrails conspiracy.