WEBVTT Kind: captions Language: en 00:00:06.330 --> 00:00:07.330 All right. 00:00:07.330 --> 00:00:08.340 Thank you so much. 00:00:08.340 --> 00:00:12.590 Sorry, I had to unmute my microphone there for a second. 00:00:12.590 --> 00:00:16.860 So we are so glad to be invited to these talks today. 00:00:16.860 --> 00:00:20.480 And I'm going to talk briefly about the NSF-funded projects that I have in collaboration with 00:00:20.480 --> 00:00:26.250 KQED Public Media, from San Francisco, called: “Influencing Young Adults’ Science Engagement 00:00:26.250 --> 00:00:28.920 and Learning with COVID-19 Media Coverage.” 00:00:28.920 --> 00:00:34.690 So this is really just part of our project team. 00:00:34.690 --> 00:00:39.180 And you can see we have members from both KQED and my group over at Texas Tech, and 00:00:39.180 --> 00:00:45.020 then Scott Burg at Rockman et al is serving as our external evaluator. 00:00:45.020 --> 00:00:49.339 So we had several project aims, the first of which was to build a better understanding 00:00:49.339 --> 00:00:54.600 of young adult audiences for science media, to provide information about future and missing 00:00:54.600 --> 00:00:59.860 audiences for this type of content, to build audience research opportunities for professional 00:00:59.860 --> 00:01:05.080 content creators, such as those in the news team at KQED, and build an academic practitioner 00:01:05.080 --> 00:01:06.080 collaboration. 00:01:06.080 --> 00:01:11.760 And this was part of a larger grant that we had funded through the NSF, pre-pandemic, 00:01:11.760 --> 00:01:19.290 and then to support the production of science content, specifically that surrounding COVID-19. 00:01:19.290 --> 00:01:21.060 So we had two primary research questions. 00:01:21.060 --> 00:01:26.221 And the first was: well, how could COVID-19 coverage, we designed to best inform, engage 00:01:26.221 --> 00:01:30.870 and educate millennials and younger adult audiences about the science of virus transmission 00:01:30.870 --> 00:01:31.870 and prevention? 00:01:31.870 --> 00:01:33.580 And we had several studies on this. 00:01:33.580 --> 00:01:35.450 And I'll just briefly go over those. 00:01:35.450 --> 00:01:39.630 And then the second part of this, and this is the part that was led by Scott from Rockman 00:01:39.630 --> 00:01:44.920 was can we develop best practices for Crisis Reporting, as journalists respond to both 00:01:44.920 --> 00:01:48.890 constantly updating information and changing audience needs? 00:01:48.890 --> 00:01:50.980 And then how can these be used by other media outlets. 00:01:50.980 --> 00:01:53.750 And this was our process evaluation. 00:01:53.750 --> 00:01:58.560 So, because we're limited on time, please do look for our QR codes. 00:01:58.560 --> 00:02:02.090 And again, these will - this will be available in the recording as well. 00:02:02.090 --> 00:02:06.270 These will link you to the full study report for each of the studies that I present. 00:02:06.270 --> 00:02:10.472 And on the final page, it'll link you to our website. 00:02:10.472 --> 00:02:15.870 So the first study that we looked at was how misinformation is disseminated on Twitter 00:02:15.870 --> 00:02:17.420 about the pandemic. 00:02:17.420 --> 00:02:22.730 And the sheer number of conversations disseminating misinformation about the virus in 2020 was 00:02:22.730 --> 00:02:26.390 very disheartening, but not particularly surprising. 00:02:26.390 --> 00:02:30.010 So we had examined the misinformation being shared online, really to kind of focus in 00:02:30.010 --> 00:02:32.970 on what those knowledge gaps were from the public. 00:02:32.970 --> 00:02:38.200 And we came to realize that news organizations really do need to create content that is informative, 00:02:38.200 --> 00:02:43.410 accurate and timely, so that when people get online to, quote, do their own research, there 00:02:43.410 --> 00:02:46.690 is information that's accurate and available there. 00:02:46.690 --> 00:02:49.780 In order to help fill these knowledge gaps in our communities about the virus. 00:02:49.780 --> 00:02:54.700 I mean this emphasizes more important, the importance I'm sorry of doing research on 00:02:54.700 --> 00:02:59.670 knowledge gaps, specifically, like those related to COVID-19. 00:02:59.670 --> 00:03:06.190 Our second set of studies that we worked on, were focusing around the idea of communicating 00:03:06.190 --> 00:03:12.280 consensus versus helping audiences better understand information by explaining, kind 00:03:12.280 --> 00:03:13.810 of, what's going on. 00:03:13.810 --> 00:03:18.280 So we all saw during the pandemic, you know, Dr. Fauci or others getting good at saying, 00:03:18.280 --> 00:03:20.450 ‘this is what the science says,’ ‘this is what the science believes’. 00:03:20.450 --> 00:03:25.220 But what we found is that presenting participants with a consensus message saying that ‘science 00:03:25.220 --> 00:03:30.690 knows this,’ that their scientific consensus that, for example, masks prevent the transmission 00:03:30.690 --> 00:03:35.420 of COVID 19 - this does not significantly influence their beliefs. 00:03:35.420 --> 00:03:41.209 But the presence of an infographic depicting how masks help to spread COVID-19 did in some 00:03:41.209 --> 00:03:43.129 circumstances, influence participants beliefs. 00:03:43.129 --> 00:03:49.930 And for this, we used the image that was published in Science, but we had edited it a little 00:03:49.930 --> 00:03:55.430 bit to be more friendly to general audiences, where we showed, you know, somebody who is 00:03:55.430 --> 00:03:58.760 infected- sorry - infected but not showing symptoms, wearing a mask versus not wearing 00:03:58.760 --> 00:03:59.760 a mask. 00:03:59.760 --> 00:04:02.790 And then when they're wearing a mask, you can really kind of intuitively understand 00:04:02.790 --> 00:04:09.630 from the image how you'd be less likely to both transmit and take in viral particles. 00:04:09.630 --> 00:04:13.620 And of course, because of the nature of the polarization around COVID-19, we did find 00:04:13.620 --> 00:04:19.390 that political party was the strongest predictor of participants' beliefs about COVID-19 risks, 00:04:19.390 --> 00:04:22.449 mask wearing, and policy support for masks. 00:04:22.449 --> 00:04:29.710 But to the benefit the infographic, this here did seem to influence Republicans and male 00:04:29.710 --> 00:04:33.700 participants' beliefs about masks wearing, which is really important because those are 00:04:33.700 --> 00:04:37.830 the audiences that seemed to reject the information most. 00:04:37.830 --> 00:04:44.460 We also had a survey about germ and vaccine attitudes and knowledge where we tested people's 00:04:44.460 --> 00:04:50.409 interests or beliefs, for example in or whether they anthropomorphize germs and viruses. 00:04:50.409 --> 00:04:54.529 So germs move to places that make it easier for people to infect them, which of course 00:04:54.529 --> 00:04:55.699 is false. 00:04:55.699 --> 00:04:59.150 But 60% of people got that incorrect. 00:04:59.150 --> 00:05:05.650 So this shows you people's understanding is still a bit - did not mean to do that - going 00:05:05.650 --> 00:05:06.680 backwards. 00:05:06.680 --> 00:05:09.020 Participants' understanding is still a little bit weak. 00:05:09.020 --> 00:05:13.719 In addition, whether understanding whether diseases are caused by viruses or bacteria 00:05:13.719 --> 00:05:16.199 is a little weak. 00:05:16.199 --> 00:05:20.770 We found that although the correct answer for what causes strep throat is bacteria, 00:05:20.770 --> 00:05:25.740 only 31% of participants in a national sample got that correct. 00:05:25.740 --> 00:05:30.180 COVID-19 - 60% understood that virus was the correct answer. 00:05:30.180 --> 00:05:33.900 It is, you know, the V stands for virus, after all, so that I think getting that information 00:05:33.900 --> 00:05:34.900 pretty consistently. 00:05:34.900 --> 00:05:40.050 But it's still surprising that 40% of people who responded to this said bacteria. 00:05:40.050 --> 00:05:44.779 We also looked at the myths and rumors such as COVID-19 can be transmitted by a 5G mobile 00:05:44.779 --> 00:05:46.129 phone signals. 00:05:46.129 --> 00:05:47.900 Most people understood that that was incorrect. 00:05:47.900 --> 00:05:54.819 So only 10% really thought that only 21% thought COVID-19 can be killed by warm water. 00:05:54.819 --> 00:06:00.210 And only 15% thought that COVID-19 is less dangerous than the flu. 00:06:00.210 --> 00:06:04.849 So most people seem to be on on the right page here, where you start to see the effects 00:06:04.849 --> 00:06:09.569 of political polarization, for example, is in the belief that hydroxychloroquine has 00:06:09.569 --> 00:06:13.960 been scientifically proven to be effective in treating COVID-19. 00:06:13.960 --> 00:06:19.180 You see a bit more of a split there. 00:06:19.180 --> 00:06:24.840 So or - the belief that COVID-19 was created as a weapon in a Chinese lab - 42% believe 00:06:24.840 --> 00:06:27.590 that, so you can still see the misinformation spreading. 00:06:27.590 --> 00:06:30.050 Also, we measured vaccine intentions. 00:06:30.050 --> 00:06:33.930 And again, we did collect this data during the limited availability of vaccines. 00:06:33.930 --> 00:06:38.309 So this is January to February in 2021. 00:06:38.309 --> 00:06:43.379 And at this point, 5% were vaccinated already, but 63%, they were likely to get vaccinated 00:06:43.379 --> 00:06:48.550 and 32% said unlikely and this is pretty consistent with what we see for who's vaccinated now, 00:06:48.550 --> 00:06:51.539 are the number of people vaccinated now. 00:06:51.539 --> 00:06:54.960 And you can see that this varies pretty strongly by political ideology. 00:06:54.960 --> 00:06:59.659 Only 16% of people who identify as politically liberal said it was unlikely that they would 00:06:59.659 --> 00:07:03.939 get a vaccine, whereas 40% of those who identify as politically conservative said it was unlikely. 00:07:03.939 --> 00:07:09.610 Finally, I just want to briefly talk about our academic practitioner collaboration. 00:07:09.610 --> 00:07:15.150 So this was one of the largest public investments in science media communication research collaborations, 00:07:15.150 --> 00:07:20.719 our process evaluation was conducted by, again, Scott Burg to assess the impact of our two 00:07:20.719 --> 00:07:25.319 groups sort of working together, and he had embedded himself in our team. 00:07:25.319 --> 00:07:29.139 We did have many accomplishments, despite some unforeseen challenges. 00:07:29.139 --> 00:07:33.750 Really, the two different timelines that journalists versus academics tend to work on was definitely 00:07:33.750 --> 00:07:39.240 something that we found to be a difficult hurdle to overcome. 00:07:39.240 --> 00:07:43.689 But it did help create a mutual respect and appreciation for each other's skills and knowledge 00:07:43.689 --> 00:07:45.660 and working methods. 00:07:45.660 --> 00:07:50.619 We got to apply both applied and basic research. 00:07:50.619 --> 00:07:54.569 You know, we - and this whole process together validated, each team's different the ways 00:07:54.569 --> 00:07:57.819 that they've come to gain knowledge and our abilities. 00:07:57.819 --> 00:08:04.399 Also, my team, for example, particularly the Ph.D. students, really benefited from having 00:08:04.399 --> 00:08:08.939 the opportunity to talk to professionals and find out what the greatest needs are in research 00:08:08.939 --> 00:08:13.830 for journalism and science communication in general. 00:08:13.830 --> 00:08:19.729 But KQED also came to understand, sort of, how we go about our research and some of the 00:08:19.729 --> 00:08:24.499 quotes from our, from the news journalists had expressed how it's really changed the 00:08:24.499 --> 00:08:29.409 way that they see when they're reporting how science is done, understanding science. 00:08:29.409 --> 00:08:31.071 And so we hope that this is a process that can be replicated. 00:08:31.071 --> 00:08:32.959 So, thank you so much. 00:08:32.959 --> 00:08:36.640 If you want to hear more about our studies, you can go to our website. 00:08:36.640 --> 00:08:38.830 And again, this QR code will link you there. 00:08:38.830 --> 00:08:40.840 And here's my email address if you have any questions. 00:08:40.840 --> 00:08:43.320 [www.kqed.org/about/program/cracking-the-code] Thank you, Ashley. 00:08:43.320 --> 00:08:45.180 It's really helpful. 00:08:45.180 --> 00:08:48.890 It's interesting to see this intersection between media and science. 00:08:48.890 --> 00:08:52.270 So I'm sure we'll get lots of questions about this from the audience. 00:08:52.270 --> 00:08:58.100 And as a reminder to our fantastic audience, if you have any questions for any of our speakers, 00:08:58.100 --> 00:09:02.460 either hang on to them for the moderated Q&A session at the end, or go ahead and drop them 00:09:02.460 --> 00:09:06.150 in the chat and we'll see if our speakers have any thoughts in between now and when 00:09:06.150 --> 00:09:08.650 we open it up to all of you. 00:09:08.650 --> 00:09:12.190 So I'd like to introduce our final speaker for today. 00:09:12.190 --> 00:09:15.860 Helena Solo-Gabriele who is based in the University of Miami. 00:09:15.860 --> 00:09:19.620 Helena, we're leaving it to you, please take it away.