论文标题
反事实的神经时间点过程，用于估计错误信息对社交媒体的因果影响
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
论文作者
论文摘要
近年来，误导运动的兴起，这些运动在社交媒体上传播了特定的叙述，以操纵在政治和医疗保健等不同领域的公众舆论。因此，需要一种有效而有效的自动方法，以估计错误信息对用户信念和活动的影响。但是，现有关于错误信息影响估计的工作要么依赖于小规模的心理实验，要么只能发现用户行为与错误信息之间的相关性。为了解决这些问题，在本文中，我们建立了一个因果框架，该框架是从时间点过程的角度对错误信息的因果效应进行建模的。为了适应大规模数据，我们设计了一种有效而精确的方法，以通过神经时间点过程和高斯混合模型来估计单个治疗效果（ITE）。关于合成数据集的广泛实验验证了我们模型的有效性和效率。我们进一步将模型应用于社交媒体帖子的现实数据集以及有关Covid-19疫苗的参与。实验结果表明，我们的模型识别出错误信息的可识别因果关系，这会损害人们对疫苗的主观情绪。
Recent years have witnessed the rise of misinformation campaigns that spread specific narratives on social media to manipulate public opinions on different areas, such as politics and healthcare. Consequently, an effective and efficient automatic methodology to estimate the influence of the misinformation on user beliefs and activities is needed. However, existing works on misinformation impact estimation either rely on small-scale psychological experiments or can only discover the correlation between user behaviour and misinformation. To address these issues, in this paper, we build up a causal framework that model the causal effect of misinformation from the perspective of temporal point process. To adapt the large-scale data, we design an efficient yet precise way to estimate the Individual Treatment Effect(ITE) via neural temporal point process and gaussian mixture models. Extensive experiments on synthetic dataset verify the effectiveness and efficiency of our model. We further apply our model on a real-world dataset of social media posts and engagements about COVID-19 vaccines. The experimental results indicate that our model recognized identifiable causal effect of misinformation that hurts people's subjective emotions toward the vaccines.
