Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment Accuracy
Who's in the Crowd Matters: Cognitive Factors and Beliefs Predict Misinformation Assessment Accuracy
复制标题
人群中的人很重要：认知因素和信念预测错误信息评估的准确性
DOI：
10.1145/3555611
复制
发表时间：
2022
影响因子：
--
通讯作者：
Dow, Steven P.
中科院分区：
文献类型：
--
作者：
Kaufman, Robert A.;Haupt, Michael Robert;Dow, Steven P.
错误信息在社交媒体上泛滥，并与疫苗犹豫等不良健康行为有关。众包可以成为检测和阻止网上错误信息传播的一种手段。然而，过去的研究并没有深入研究个人特征——例如认知因素和偏见——来预测众包工作者识别错误信息的准确性。在我们的研究 (n = 265) 中，Amazon Mechanical Turk (MTurk) 工作人员和大学生评估了与 COVID-19 相关的推文的真实性和情绪，并回答了多项有关个人特征的调查。结果支持众包评估与 COVID-19 大流行等持续存在的政治话题相关的错误信息和内容立场（即情绪）的可行性，但是，与专家的一致性取决于人群中的人员。具体来说，我们发现，认知反思测试（CRT）得分高、尽责性高、对医学科学家信任的受访者更倾向于专家，而认知封闭需求（NFCC）高的受访者和政治上倾向保守的受访者则不太倾向于专家。我们也看到了招聘平台之间的差异，因为我们的数据显示，大学生平均比 MTurk 员工更接近专家，这很可能是由于每个平台上参与者特征的总体差异。结果提供了人群构成如何影响错误信息和立场评估的透明度，并对未来的人群招募和过滤实践产生影响。
Misinformation runs rampant on social media and has been tied to adverse health behaviors such as vaccine hesitancy. Crowdsourcing can be a means to detect and impede the spread of misinformation online. However, past studies have not deeply examined the individual characteristics - such as cognitive factors and biases - that predict crowdworker accuracy at identifying misinformation. In our study (n = 265), Amazon Mechanical Turk (MTurk) workers and university students assessed the truthfulness and sentiment of COVID-19 related tweets as well as answered several surveys on personal characteristics. Results support the viability of crowdsourcing for assessing misinformation and content stance (i.e., sentiment) related to ongoing and politically-charged topics like the COVID-19 pandemic, however, alignment with experts depends on who is in the crowd. Specifically, we find that respondents with high Cognitive Reflection Test (CRT) scores, conscientiousness, and trust in medical scientists are more aligned with experts while respondents with high Need for Cognitive Closure (NFCC) and those who lean politically conservative are less aligned with experts. We see differences between recruitment platforms as well, as our data shows university students are on average more aligned with experts than MTurk workers, most likely due to overall differences in participant characteristics on each platform. Results offer transparency into how crowd composition affects misinformation and stance assessment and have implications on future crowd recruitment and filtering practices.
登录
查看更多内容
影响因子：
--
作者：
Sukrit Venkatagiri;Jacob Thebault-Spieker;Rachel Kohler;John Purviance;Rifat Sabbir Mansur;Kurt Luther
通讯作者：
Sukrit Venkatagiri;Jacob Thebault-Spieker;Rachel Kohler;John Purviance;Rifat Sabbir Mansur;Kurt Luther
影响因子：
4
作者：
De keersmaecker, Jonas;Dunning, David;Roets, Arne
通讯作者：
Roets, Arne
DOI：
--
发表时间：
2018
期刊：
CIKM Workshops
影响因子：
--
作者：
Eddy Maddalena;D. Ceolin;Stefano Mizzaro
通讯作者：
Stefano Mizzaro
将按需事实核查与公共对话相结合
DOI：
10.1145/2531602.2531677
发表时间：
2014
期刊：
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
影响因子：
--
作者：
Travis Kriplean;C. Bonnar;A. Borning;Bo Kinney;Brian T. Gill
通讯作者：
Brian T. Gill
影响因子：
8.5
作者：
Smith M;Alvarez F
通讯作者：
Alvarez F