III:Small: A novel machine learning framework for combating misinformation in real life

社交媒体上一直存在广泛的错误信息，给社会带来了重大挑战。错误信息涉及迅速变化的主题和不良行为者之间的复杂协调。现有的打击错误信息的技术取得了有限的成功。他们努力及时有效地消除虚假和误导性内容。另一个挑战是难以量化错误信息的影响。在这个项目中，将利用机器学习的最新发展来开发一个新的机器学习框架。它将促进对错误信息传播模式的理解，产生有效的错误信息检测算法，并帮助建立一个安全和值得信赖的网络空间。将开展一系列外联活动，以扩大妇女和其他代表性不足的群体对计算的参与。该项目将提供教学和课程来传播研究成果。该项目将推进机器学习方法和社会科学见解，从而以及时、可扩展和有效的方式减少错误信息和操纵。该项目将追求三个研究方向。第一个推力开发了一个基于学习的解决方案，使用新闻源的可信度分析，以尽量减少人类标记的努力，在构建大规模的错误信息数据集。第二个重点是开发一种基于知识通知的机器学习模型的无监督协调检测方法，以识别不良行为者之间的协调行为。第三个重点是开发新的反事实分析模型，以确定因果因素和评估错误信息的估计影响。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Widespread misinformation has been persistently observed on social media, raising significant challenges to society. Misinformation involves rapidly evolving topics and sophisticated coordination among bad actors. Existing techniques to combat misinformation have achieved success to a limited extent. They struggle to eliminate false and misleading content in a timely and effective manner. Another challenge is the difficulty of quantifying the impacts of misinformation. In this project, a novel machine-learning framework will be developed by leveraging recent developments in machine learning. It will advance the understanding of misinformation propagation patterns, produce effective algorithms for misinformation detection, and help build a secure and trustworthy cyberspace. A range of outreach activities will be pursued to broaden participation in computing for women and other underrepresented groups. Tutorials and courses will be provided to broadcast the research outcomes.The project will advance both machine-learning methodologies and social science insights, leading to effective solutions to mitigate misinformation and manipulation in a timely, scalable, and effective manner.Three research thrusts will be pursued in this project. The first thrust develops a reinforcement-learning-based solution using news-source credibility analysis to minimize human labeling efforts in constructing large-scale misinformation datasets. The second thrust develops an unsupervised coordination detection method based on knowledge-informed machine-learning models to identify coordinated behaviors among bad actors. The third thrust develops novel counterfactual analysis models to identify causal factors and evaluate the estimated effects of misinformation. A collection of large-scale datasets of misinformation on a variety of topics will be shared with the research community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.