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Poster
Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media
Yizhou Zhang · Defu Cao · Yan Liu

Tue Nov 29 09:00 AM -- 11:00 AM (PST) @ Hall J #902
in Poster Session 1 »

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 \textbf{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.

Keywords: Deep Learning · Causal Inference · Social Media · Counterfactual Analysis · Fake News · Temporal Point Process · Misinformation Influence
[ Paper]  [ Poster]  [ OpenReview] 

Author Information

Yizhou Zhang (University of Southern California)
Defu Cao (University of Southern California)

Cao is primarily interested in developing machine learning and data mining algorithms that demonstrate a deep understanding of the world with special structures, including time series, spatio-temporal data, and relational data. To this end, his research aims to integrate causal inference, graph neural networks, spectral domain representation, interpretability, and robustness, he is also interested in multi-task learning and pre-training model in the NLP domain. He has published his research in top conference proceedings including NeurIPS, ICRA, ICDM, PAKDD, NAACL, and TrustCom.

Yan Liu (University of Southern California)

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