TY - GEN
T1 - Adapting security warnings to counter online disinformation
AU - Kaiser, Ben
AU - Wei, Jerry
AU - Lucherini, Elena
AU - Lee, Kevin
AU - Matias, J. Nathan
AU - Mayer, Jonathan
N1 - Publisher Copyright:
© 2021 by The USENIX Association. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Disinformation is proliferating on the internet, and platforms are responding by attaching warnings to content. There is little evidence, however, that these warnings help users identify or avoid disinformation. In this work, we adapt methods and results from the information security warning literature in order to design and evaluate effective disinformation warnings. In an initial laboratory study, we used a simulated search task to examine contextual and interstitial disinformation warning designs. We found that users routinely ignore contextual warnings, but users notice interstitial warnings-and respond by seeking information from alternative sources. We then conducted a follow-on crowdworker study with eight interstitial warning designs. We confirmed a significant impact on user information-seeking behavior, and we found that a warning's design could effectively inform users or convey a risk of harm. We also found, however, that neither user comprehension nor fear of harm moderated behavioral effects. Our work provides evidence that disinformation warnings can-when designed well-help users identify and avoid disinformation. We show a path forward for designing effective warnings, and we contribute repeatable methods for evaluating behavioral effects. We also surface a possible dilemma: disinformation warnings might be able to inform users and guide behavior, but the behavioral effects might result from user experience friction, not informed decision making.
AB - Disinformation is proliferating on the internet, and platforms are responding by attaching warnings to content. There is little evidence, however, that these warnings help users identify or avoid disinformation. In this work, we adapt methods and results from the information security warning literature in order to design and evaluate effective disinformation warnings. In an initial laboratory study, we used a simulated search task to examine contextual and interstitial disinformation warning designs. We found that users routinely ignore contextual warnings, but users notice interstitial warnings-and respond by seeking information from alternative sources. We then conducted a follow-on crowdworker study with eight interstitial warning designs. We confirmed a significant impact on user information-seeking behavior, and we found that a warning's design could effectively inform users or convey a risk of harm. We also found, however, that neither user comprehension nor fear of harm moderated behavioral effects. Our work provides evidence that disinformation warnings can-when designed well-help users identify and avoid disinformation. We show a path forward for designing effective warnings, and we contribute repeatable methods for evaluating behavioral effects. We also surface a possible dilemma: disinformation warnings might be able to inform users and guide behavior, but the behavioral effects might result from user experience friction, not informed decision making.
UR - http://www.scopus.com/inward/record.url?scp=85114466783&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114466783&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85114466783
T3 - Proceedings of the 30th USENIX Security Symposium
SP - 1163
EP - 1180
BT - Proceedings of the 30th USENIX Security Symposium
PB - USENIX Association
T2 - 30th USENIX Security Symposium, USENIX Security 2021
Y2 - 11 August 2021 through 13 August 2021
ER -