An official website of the United States government
Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event---Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation online.
more »
« less
"Yeah, this graph doesn't show that": Analysis of Online Engagement with Misleading Data Visualizations
Attempting to make sense of a phenomenon or crisis, social media users often share data visualizations and interpretations that can be erroneous or misleading. Prior work has studied how data visualizations can mislead, but do misleading visualizations reach a broad social media audience? And if so, do users amplify or challenge misleading interpretations? To answer these questions, we conducted a mixed-methods analysis of the public's engagement with data visualization posts about COVID-19 on Twitter. Compared to posts with accurate visual insights, our results show that posts with misleading visualizations garner more replies in which the audiences point out nuanced fallacies and caveats in data interpretations. Based on the results of our thematic analysis of engagement, we identify and discuss important opportunities and limitations to effectively leveraging crowdsourced assessments to address data-driven misinformation.
more »
« less
- Award ID(s):
- 2041136
- PAR ID:
- 10553277
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400703300
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Location:
- Honolulu HI USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Background The increasing volume of health-related social media activity, where users connect, collaborate, and engage, has increased the significance of analyzing how people use health-related social media. Objective The aim of this study was to classify the content (eg, posts that share experiences and seek support) of users who write health-related social media posts and study the effect of user demographics on post content. Methods We analyzed two different types of health-related social media: (1) health-related online forums—WebMD and DailyStrength—and (2) general online social networks—Twitter and Google+. We identified several categories of post content and built classifiers to automatically detect these categories. These classifiers were used to study the distribution of categories for various demographic groups. Results We achieved an accuracy of at least 84% and a balanced accuracy of at least 0.81 for half of the post content categories in our experiments. In addition, 70.04% (4741/6769) of posts by male WebMD users asked for advice, and male users’ WebMD posts were more likely to ask for medical advice than female users’ posts. The majority of posts on DailyStrength shared experiences, regardless of the gender, age group, or location of their authors. Furthermore, health-related posts on Twitter and Google+ were used to share experiences less frequently than posts on WebMD and DailyStrength. Conclusions We studied and analyzed the content of health-related social media posts. Our results can guide health advocates and researchers to better target patient populations based on the application type. Given a research question or an outreach goal, our results can be used to choose the best online forums to answer the question or disseminate a message.more » « less
-
Social media companies wield power over their users through design, policy, and through their participation in public discourse. We set out to understand how companies leverage public relations to influence expectations of privacy and privacy-related norms. To interrogate the discourse productions of companies in relation to privacy, we examine the blogs associated with three major social media platforms: Facebook, Instagram (both owned by Facebook Inc.), and Snapchat. We analyze privacy-related posts using critical discourse analysis to demonstrate how these powerful entities construct narratives about users and their privacy expectations. We find that each of these platforms often make use of discourse about "vulnerable" identities to invoke relations of power, while at the same time, advancing interpretations and values that favor data capitalism. Finally, we discuss how these public narratives might influence the construction of users' own interpretations of appropriate privacy norms and conceptions of self. We contend that expectations of privacy and social norms are not simply artifacts of users' own needs and desires, but co-constructions that reflect the influence of social media companies themselves.more » « less
-
Redbird, Beth; Harbridge-Yong, Laurel; Mersey, Rachel Davis (Ed.)In our analysis, we examine whether the labelling of social media posts as misinformation affects the subsequent sharing of those posts by social media users. Conventional understandings of the presentation-of-self and work in cognitive psychology provide different understandings of whether labelling misinformation in social media posts will reduce sharing behavior. Part of the problem with understanding whether interventions will work hinges on how closely social media interactions mirror other interpersonal interactions with friends and associates in the off-line world. Our analysis looks at rates of misinformation labelling during the height of the COVID-19 pandemic on Facebook and Twitter, and then assesses whether sharing behavior is deterred misinformation labels applied to social media posts. Our results suggest that labelling is relatively successful at lowering sharing behavior, and we discuss how our results contribute to a larger understanding of the role of existing inequalities and government responses to crises like the COVID-19 pandemic.more » « less
-
Wherever you look you are likely to see people on their phones, noses inches from the screen, browsing the internet, and especially spending time on social media. If you have used social media before, you know you can “like” posts, share them with friends, comment on them, and respond to what others have said. How do our brains remember social media? Do we remember social media posts better if we interact with them in some way? To study this, we asked people to view real Instagram posts and comment on some of them. Later, we tested their memory for these posts. We found that people were much more likely to remember the posts that they commented on. These results are important to consider when using social media. When we actively engage with social media, it is more likely to stick in memory, so we may need to choose wisely what we interact with to keep ourselves healthy.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3613904.3642448
