TY - GEN
T1 - Towards Healthy Engagement with Online Debates
T2 - 30th ACM Conference on User Modeling, Adaptation and Personalization, UMAP2022
AU - Rieger, Alisa
AU - Shaheen, Qurat Ul Ain
AU - Sierra, Carles
AU - Theune, Mariet
AU - Tintarev, Nava
PY - 2022
Y1 - 2022
N2 - Online debates allow for large-scale participation by users with different opinions, values, and backgrounds. While this is beneficial for democratic discourse, such debates often tend to be cognitively demanding due to the high quantity and low quality of non-expert contributions. High cognitive demand, in turn, can make users vulnerable to cognitive biases such as confirmation bias, hindering well-informed attitude forming. To facilitate interaction with online debates, counter confirmation bias, and nudge users towards engagement with online debate, we propose (1) summaries of the arguments made in the debate and (2) personalized persuasive suggestions to motivate users to engage with the debate summaries. We tested the effect of four different versions of the debate display (without summary, with summary and neutral suggestion, with summary and personalized persuasive suggestion, with summary and random persuasive suggestion) on participants' attitude-opposing argument recall with a preregistered user study (N = 212). The user study results show no evidence for an effect of either the summary or the personalized persuasive suggestions on participants' attitude-opposing argument recall. Further, we did not observe confirmation bias in participants' argument recall, regardless of the debate display. We discuss these observations in light of additionally collected exploratory data, which provides some pointers towards possible causes for the lack of significant findings. Motivated by these considerations, we propose two new hypotheses and ideas for improving relevant properties of the study design for follow-up studies.
AB - Online debates allow for large-scale participation by users with different opinions, values, and backgrounds. While this is beneficial for democratic discourse, such debates often tend to be cognitively demanding due to the high quantity and low quality of non-expert contributions. High cognitive demand, in turn, can make users vulnerable to cognitive biases such as confirmation bias, hindering well-informed attitude forming. To facilitate interaction with online debates, counter confirmation bias, and nudge users towards engagement with online debate, we propose (1) summaries of the arguments made in the debate and (2) personalized persuasive suggestions to motivate users to engage with the debate summaries. We tested the effect of four different versions of the debate display (without summary, with summary and neutral suggestion, with summary and personalized persuasive suggestion, with summary and random persuasive suggestion) on participants' attitude-opposing argument recall with a preregistered user study (N = 212). The user study results show no evidence for an effect of either the summary or the personalized persuasive suggestions on participants' attitude-opposing argument recall. Further, we did not observe confirmation bias in participants' argument recall, regardless of the debate display. We discuss these observations in light of additionally collected exploratory data, which provides some pointers towards possible causes for the lack of significant findings. Motivated by these considerations, we propose two new hypotheses and ideas for improving relevant properties of the study design for follow-up studies.
UR - http://www.scopus.com/inward/record.url?scp=85135168898&partnerID=8YFLogxK
U2 - 10.1145/3511047.3537692
DO - 10.1145/3511047.3537692
M3 - Conference contribution
AN - SCOPUS:85135168898
T3 - UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
SP - 192
EP - 199
BT - UMAP2022 - Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
PB - Association for Computing Machinery (ACM)
Y2 - 4 July 2022 through 7 July 2022
ER -