数据画像和中介主题：鼓励探索政治上多样化的轮廓
Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles
摘要：
在微博平台上，人们与他人联系和互动。然而，由于认知偏见，他们倾向于与志同道合的人互动，只阅读令人愉快的信息。许多努力使人们与那些想法不同的人联系起来，但效果并不好。在本文中，我们假设，第一，以前的方法没有工作，因为他们一直是直接的-他们试图明确连接的人与那些对敏感问题有相反的观点。第二，无论是推荐还是展示信息本身都不足以鼓励行为改变。我们提出了一个平台，混合推荐算法和可视化的用户界面，以探索建议。它根据潜在主题的距离推荐政治上多样化的配置文件，并以每个用户个人内容的视觉表示形式显示这些推荐。我们对这个平台进行了“野外”评估，发现人们在使用有偏见的算法而不是我们的算法时探索了更多的建议。与我们的假设一致，我们还发现，我们的推荐算法和我们的用户界面的混合，允许对政治感兴趣的用户展示推荐配置文件的公正探索。最后，我们的研究结果在两个方面提供了见解：第一，在设计旨在改变行为的平台时，哪些个体差异是重要的;第二，哪些算法和用户界面应该混合使用，以帮助用户避免导致偏见行为的认知机制。
英文摘要：
In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
