Nov 28, 2022
Recent years have witnessed the rise of misinformation campaign which 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 impact of the misinformation on user beliefs and activities. 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 ITE via neural temporal point process and gaussian mixture models. Extensive experiments on synthetic dataset and real-world dataset verify the effectiveness and efficiency of our model.Recent years have witnessed the rise of misinformation campaign which 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 impact of the misinformation on user beliefs and activities. However, existing works on misinformation impact estimation either rely on small-scale psychological experiments or can only discover the correlation between user b…
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