Who tweets climate change papers? investigating publics of research through users’ descriptions

Results General results Our final dataset included 19 783 unique Twitter accounts. We assigned at least one cate- gory to 69.9% of the accounts (n = 13 821) by using our code to detect expressions in Twitter bios, and 36.2% (n = 7 155) matched to only one category, and 33.7% (n = 6 666) to multiple categories (Table 2). Academia is the largest category across all papers at 5 545 users, repre- senting 28% of our dataset. Personal assignations represent a quarter of the dataset. Publish- ers and bots are the less visible categories, both under 2%. However, accounts posting automated content do not usually identify as such. Therefore, a low representation of auto- mated accounts is mostly an artefact of the method used in our study, as it is probably much higher (24). The number of unique users was slightly higher in 2015 (n = 11 745) than 2016 (n = 10 467). We also notice a higher uptake by Personal, Professional, Political and Com- munication public in 2015, potentially indicating higher engagement outside of academia. Academia, Organizations, Publishers and Robots are proportionally more represented in 2016. Academia and Organizations engaged with the most papers in our dataset, whereas Political, Publishers and Bots assigned users engaged with the least. Bots and Academia have the lowest median in number of followers, Communication and Publishers the highest. This may indicate that communicators and publishers tend to fulfill a role of sharing research with a larger network of persons than other group of users. These trends are similar

for 2015 and 2016, although number of followers of users tweeting the papers of our dataset were higher in 2015. Looking at overlaps between categories, Academia/Personal and Academia/Organizations are the most present in users’ descriptions (Table 3). The large number of overlaps across all categories indicate that the assignations of based on unique identity markers significantly reduces the complex ways in which users present themselves to others on Twitter. As such, the complexity of identifying who share research papers on Twitter may be best documented by looking at multiple categories and in the context of individual papers. Table 2. Summary of results across all papers. All papers 2015 2016 Type of publics Total n of users % w unique assignations N of papers Median n of followers Total n of users % w unique assignations N of papers Median n of followers Total n of users % w unique assignations N of papers Median n of followers Academia 5 545 32.2% 1 613 502 3 193 31.1% 816 603 3 343 34.4% 797 504 Personal 4 939 32.0% 1 460 655 3 071 32.2% 760 757 2 387 29.6% 700 636 Professional 2 963 24.0% 1 045 819 1 782 22.1% 527 950 1 506 24.4% 518 790 Political 2 564 28.0% 921 863 1 651 27.0% 469 990 1 240 28.0% 452 833 Communication 2 237 26.2% 1 020 1 128 1 393 25.9% 505 1245 1 140 25.9% 515 1 111 Organization 4 201 37.6% 1 656 726 2 462 38.1% 836 870 2 378 37.0% 820 701 Publishers 357 38.1% 650 1 499 213 38.5% 327 1778 217 39.2% 323 1 688

Bots 101 61.4% 466 440 61 65.6% 202 482 68 38.8% 264 592 Unassigned 5 962 1 680 632 3 457 859 724 3033 821 616 Total 19 783 36.2% 11 745 36.1% 10 467 37.0% The above table presents the absolute number of profiles assigned to each category (Total n of users), the% of profiles with only one assignation for each category (% w unique assignations), the number of papers tweeted by at least one user per category (N of papers), and the median number of followers of the users assigned to each category (Media n of followers). Results are presented for the whole dataset investigated in this study as well as splited between years. https://doi.org/10.1371/journal.pone.0268999.t002 PLOS ONE Who tweets climate change research papers? PLOS ONE | https://doi.org/10.1371/journal.pone.0268999 June 3, 2022 8 / 17