Two large panel surveys examined Americans’ perceptions of both the prevelance of uncivil behaviors on different platforms and key platform features.

Specifically, every social media platform that a participant reported using regularly was examined, meaning that perceptions that are due to the platform could be disentangled from perceptions due to the user.

First, perceptions were not stable – idiosyncratic user experiences made more of a difference than any “objective” differences between platforms.

Second, network association – which measures two key building blocks of community – was positively associated with perceptions of incivilility. This might suprise you. Platforms characterized by network association look more like offline communities: Your connections to other people are visible; your connections can easily meet and interact with one another. Your might expect that under these conditions, people would be on their best bahvior. Not so. In fact, people anticipate more uncivil behaviors on these platforms.

An additional major contribution of this work is that it offers an intuitive definition of incivility: Uncivil acts (are perceived to) expresss a commitment to conflict. Civil acts imply that sitting down to talk is still a worthwhile conflict management strategy.

https://doi.org/10.1093/jcmc/zmac035

Cite (APA style) as:

Sude. D. J., & Dvir Gvirsman, S. (2022). Different platforms, different uses: Testing the effect of platforms and individual differences on perception of incivility and self-reported uncivil behavior. Journal of Computer Mediated Communication. Advance online publication. https://doi.org/10.1093/jcmc/zmac035

In this open-access book chapter (free on Kindle and Nook), myself and Dr. Silvia Knobloch-Westerwick take a “multimotive” approach to the confirmation bias – the tendency to seek out and be ammenable to information that supports what we already believe.

In summary: It’s not just about wanting to be right. We also do this to get along with others. For full details, please see the chapter itself. It uses plain language and should be accessible to non-academics (or, at least, I tried :). It concludes with some suggestions for how to weaken this bias in others and promote more “ideal” information processing.

Do also take a look at the rest of the chapters in this fantastic volume on “Knowledge Resistance” featuring some of the best researchers in communication science.

Sude, D.J., & Knobloch-Westerwick, S. (2022). Selective exposure and attention to attitude-consistent and attitude-discrepant information: Reviewing the evidence. In Strömbäck, J., Wikforss, A., Glüer, K., Lindholm, T., and Oscarsson, H. (Eds.) Knowledge resistance in high-choice information environments.Routledge. doi: 10.4324/9781003111474

We had students browse a custom-programmed news aggregator site featuring political content taking pro- and contra-stances on a variety of perenial political and politicized science topics: everything from gun control to teaching evolution in public schools. Journalistic bylines featured either common male or common female first names, by random assignment. To get these names, we used a search of the Social Security Database.

How did gender impact browsing behavior? If these students indicated that their gender was personally important to them (compared to, say, their religion or their race), they spent more time reading content by same-gender authors.

Further, students who though their gender was esteemed by society also spent more time reading work by same-gender authors. This suggested that representation matters, but only for students who already thought that their group was societally esteemed. In other words, while a few instances of exposure to representation in the news media did not shift students’ overall attitudes-it would take a lot more than a few exposures to do that-these instances likely helped these students maintain their positive beliefs.

Importantly, these social-identity related processes in turn diversified information exposure, leading these students to spend more time on attitude-challenging political content, when it had a same-gender author.

Westerwick, A., Sude, D.J., Brooks, D., Kaplan, B. & Knobloch-Westerwick, S. (2022) Self-consistency and self-enhancement motivation impacts on selective exposure to politics — A SESAM Model Application. Mass Communication and Society. https://doi.org/10.1080/15205436.2022.2056854

At this week’s ICA I presented my paper with my co-authors, Axel Westerwick and Silvia Knobloch-Westerwick.

Click here to view.

I was also a co-author on a submission with Nicholas Mathews and Joe Bayer examining moral relativism, willingness to communicate with morally dissimilar others, social network cognition, and social network diversity.

Read this Vox article on productivity and remote work: https://www.vox.com/recode/22435905/wework-ceo-sandeep-mathrani-remote-work-from-home-engagement-productivity-pandemic

One virtue of this article is the interrogation of the methods behind the data. What do they actually tell us?

One thing left out is whether the nature of the productive work matters.

Even momentary interruptions (from colleagues, from children, from pets, from spouses) can kill “deep” work – work that requires thinking carefully about complex topics.

When a topic is sufficiently complex, any conclusions are inherently ambiguous. Both rushing (e.g., to meet a deadline) and getting distracted (e.g., by other tasks, conversations) can limit your progress. These topics require slow, iterative, engagement.

At the same time, they also require thinking about how other minds would view the problem. The solutions to complex problems emerge when you have multiple perspectives that manage to converge on intersubjectively accessible solutions. A good, cognitively diverse, team of colleagues can help push “deep” work forward.

So, in my conclusion, working from home, for people committed to “deep work,” can be an asset. As much or more work can be done when going for a walk in the neighborhood as when sitting in cubical. But, balance is achieved when people can punctuate these isolated, exploratory, activities with critical brainstorming as a team.

Budak, C., Garrett, R. K., & Sude, D. (in press). Better crowdcoding: Strategies for promoting accuracy in crowdsourced content analysis. Communication Methods and Measures.

In this work, we evaluate different instruction strategies to improve the quality of
crowdcoding for the concept of civility. We test the effectiveness of training,
codebooks, and their combination through 2×2 experiments conducted on two
different populations—students and Amazon Mechanical Turk workers. In
addition, we perform simulations to evaluate the trade-off between cost and
performance associated with different instructional strategies and the number of
human coders. We find that training improves crowdcoding quality, while
codebooks do not. We further show that relying on several human coders and
applying majority rule to their assessments significantly improves performance.

A useful resource for building up intuitions about machine learning (in this case, supervised machine learning) https://healthcare.ai/visual-tour-lasso-random-forest/….

People in my field will try to categorize social media posts as civil {uncivil} or low {high} in deliberative quality using these techniques. The goal, there, is to train machines that can, with a high but not perfect degree of accuracy, sort through hundreds of thousands of posts. This can help not only generate a pool of ecologically valid stimuli for use in experiments but help peole to understand emergent crises.

Organizations like http://moonshotcve.com/ (CVE = countering violent extremism) will identify individuals who are being radicalized using similar techniques. They can then try introducing “alternative content” (content that challenges extremist narrative). According to one of their representative’s talk at New America’s 2020 Future Security Forum, these potential extremists do click on that alternative content. Note: These “low effort” tech solutions are not perfect. Some people are flagged for more effortful counter and deradicalization efforts.

These efforts succeed in part with the coopertion of social media companies. They can also be pursued independently (for good or for ill. Imagine extremists using machine learning to identify good targets for their messages).

However, my own view is that introducing new content is not enough. Content may serve as an exemplar – impacting perceptions of public opinion (within groups) as well as of normative behaviors. It may also, of course, serve as a source of {mis}{dis}information. Each of these functions can have a downside that is hard for the individual to correct.

Perceptions of public opinion and norms based on exemplars may in fact be quite inaccurate (as when participants in Sude et al. 2019 shifted their perceptions of public opinion after reading a single article). Exemplification is largely an automatic process, people are not necessarily aware of it and it can persist even in the presence of base-rate information (e.g. https://doi.org/10.1177/009365094021005003). When people “like” the revised perception of public opinion – e.g. when it implies that their opinions are popular – this can also lead to a motivation to embrace and defend these revised estimates, even in the face of counter evidence.

A social media company, aware of this impact, could consciously gather evidence about the “true” baserates (on their site, and, based on polling data, nationaly). The least it could do is provide base-rates that partially counter the exemplification effect. Alternatively, they could prompt users to “anchor” these base-rates on specific groups, both by presenting a finer grained profile of users that resemble that exemplar (individuating them) and by punctuating the social media experience with survey questions like: “What percentage of Republicans in your social network on Twitter do you think share this poster’s views?” accompanied by “What percentage of Republicans nationally do you think share this poster’s views?”: The goal would be to highlight the potential differences between the “answers” to these questions. It’s more likely that the exemplar provides meaningful information about your social network than about the national public.

Similarly, with regards to asking people to evaluate source and content quality before endorsing or sharing, putting the onus on the individual to be an “A student” is impractical. Affordances that facilitate external citations and links, and thus a stronger web of evidence, shift the burden away from the individual and towards a social media company which is better able to handle it. Importantly, the social media company could also categorize different types of sources, provide a transparent justification for why “mainstream” sources are more likely to be accurate (e.g. the legal and institutional processes that ensure higher accuracy), and then provide a more objective rubric by which individuals could evaluate alternative or non-institutional sources of information. This might actually be a good way for individuals who are doing really high quality work to get recognized: being scored consistently well on the more objective rubric could garner you a badge. Importantly, these rubrics would have to require providing concrete evidence from the text being evaluated (screen shots, for example).

From an economic perspective, of course, social media companies need to provide plenty of emotional rewards for putting the effort in. Making it easier to get an A is not enough, the A has to light up your heart (or the “addiction pathways” in your brain). Now that I’ve cleared my head of the ideas rustling around, Happy Sunday!

https://news.osu.edu/liking-an-article-online-may-mean-less-time-spent-reading-it/

“Liking” an article online may mean less time spent reading it, according to a study of 235 U.S. college students. Views on controversial topics became stronger after voting on articles that agreed with their views, even when study participants spent less time reading them. from science

This article garnered interest from normal science news sites (https://www.sciencedaily.com/releases/2020/09/200930094732.htm) to lifestyle magazines (https://www.equinox.com/landing/check-in-10-25?emmcid=EMM-1025CRMDailyCheckin-M-USCANUK10252020#learn-then-like) to career-building sites (https://www.theladders.com/career-advice/this-study-shows-how-the-internet-is-ruining-our-reading-habits).

Sude, D. J., Pearson, G. D. H., Knobloch-Westerwick, S. (2021). Self-expression just a click away: Source interactivity impacts on confirmation bias and political attitudes. Computers in Human Behavior. 114: (106571) https://doi.org/10.1016/j.chb.2020.106571

Abstract: Information is now commonly consumed online, often displayed in conjunction with self-expression affordances (i.e., likes, votes) that create a sense of “self as source.” Sundar et al.’s (2015) theory of interactive media effects (TIME) conceptualizes such affordances as source interactivity (SI). An experiment examined medium effects of SI as well as message effects on attitudes. It tracked selective exposure to attitude-consistent vs. –discrepant political messages, to capture confirmation bias, and manipulated SI presence (affordance to up-vote or down-vote articles present or absent) as within-subjects factors. SI use and attitude change were captured. SI reduced selective exposure to attitude-consistent content. However, use of SI affected attitude reinforcement independently as well. Hence, users shaped their own attitudes both by selectively reading articles and expressing their views through SI. Directions for theory development are offered.

At this year’s School of Communication, “Comm Day,” I was delighted to receive the 2020 Peer Mentor Award. Nominated and voted upon by my peers, this award recognize the informal mentoring that occurs within a department. It was nice to know that the long talks about statistical analyses, research ideas, and navigating intradepartmental relationships were helpful!

Informal support – whether one is functioning as teacher, sympathetic shoulder, or cheerleader – helps a department function, and I was one among many supportive graduate colleages in the School.