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近年来,计算假新闻检测取得了显著进展。为了减轻其负面影响,我们认为,了解哪些用户属性可能导致用户分享假新闻至关重要。这个因果推理问题的关键是识别混杂因素——导致治疗(如用户属性)和结果(如用户易感性)之间虚假关联的变量。在假新闻传播中,混淆者可以被描述为与用户属性和在线活动内在相关的假新闻分享行为。对于那些容易在社交媒体上分享新闻的用户来说,学习这种用户行为通常会受到选择偏差的影响。基于因果推理理论,我们首先提出了一种原理性的方法来缓解假新闻传播中的选择偏差。然后,我们将习得的无偏见假新闻分享行为视为可以充分捕捉用户属性和用户易感性之间的因果关系的替代混淆物。我们从理论上和实证上描述了该方法的有效性,并发现它可能有助于保护社会免受假新闻的危害。

https://www.zhuanzhi.ai/paper/0fc63a39f9bf4933d8caa136d564fba8

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Humanitarian challenges, including natural disasters, food insecurity, climate change, racial and gender violence, environmental crises, the COVID-19 coronavirus pandemic, human rights violations, and forced displacements, disproportionately impact vulnerable communities worldwide. According to UN OCHA, 235 million people will require humanitarian assistance in 2021. Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations. Scattered data science efforts exist to address these challenges, but they remain isolated from practice and prone to algorithmic harms concerning lack of privacy, fairness, interpretability, accountability, transparency, and ethics. Biases in data-driven methods carry the risk of amplifying inequalities in high-stakes policy decisions that impact the livelihood of millions of people. Consequently, proclaimed benefits of data-driven innovations remain inaccessible to policymakers, practitioners, and marginalized communities at the core of humanitarian actions and global development. To help fill this gap, we propose the Data-driven Humanitarian Mapping Research Program, which focuses on developing novel data science methodologies that harness human-machine intelligence for high-stakes public policy and resilience planning. The proceedings of the 2nd Data-driven Humanitarian Mapping workshop at the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. August 15th, 2021

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