Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media-猫眼课题宝

DOI：

10.48550/arxiv.2210.07518

发表时间：

2022-10

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Yizhou Zhang;Defu Cao;Y. Liu

近年来，在社交媒体上传播特定叙事，以操纵政治和医疗等不同领域的公众舆论的虚假信息运动兴起。因此，需要一种有效和高效的自动方法来估计错误信息对用户信念和活动的影响。然而，现有的错误信息影响评估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，本文建立了一个因果框架，从时间点过程的角度对错误信息的因果效应进行建模。为了适应大规模数据，我们设计了一种有效而精确的方法，通过神经时间点过程和高斯混合模型来估计个体治疗效应（ITE）。在合成数据集上的大量实验验证了该模型的有效性和高效性。我们进一步将我们的模型应用于关于COVID-19疫苗的社交媒体帖子和参与的真实数据集。实验结果表明，我们的模型识别了伤害人们对疫苗主观情绪的错误信息的可识别因果效应。

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.

Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media

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