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| Content Provider | ACM Digital Library |
|---|---|
| Author | Fu, Wai-Tat Liao, Q. Vera Mamidi, Sri Shilpa |
| Abstract | Selective exposure, the preferential seeking of confirmatory information, can potentially exacerbate fragmentation of online opinions and lead to biased decisions. We tested whether features that allowed users to better distinguish information about different issue aspects would encourage them to take different perspectives, thereby moderating the negative influence of pre-existing beliefs on information seeking. Using an information aggregator that provided drug related comments, we conducted an experiment to study the impact of aspect indicators (indicating whether the comment was about effectiveness or side effects) on moderating selective exposure. We found that, when participants were asked to decide between medications for high-risk diseases, and had preexisting biased beliefs in their effectiveness (one medication was less effective than the other), without aspect indicators they exhibited selective exposure to both types of comments (effectiveness and side effects) and were biased to choose the medication in confirmation of their pre-existing beliefs. With aspect indicators, we found reduced selective exposure to information about side effects of the medications, and as a result their overall decision bias was mitigated. However, the effect of aspect indicators in reducing selective exposure was moderated by the decision contexts, including the perceived risk of the diseases and whether the aspect was perceived to be critical to the decision. |
| Starting Page | 1439 |
| Ending Page | 1448 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450331456 |
| DOI | 10.1145/2702123.2702570 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-04-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Biases Information seeking Decision making Health Selective exposure Confirmation bias |
| Content Type | Text |
| Resource Type | Article |
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