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Information is disseminating more rapidly in today's world than ever before in history. Every now and then, topics simultaneously gain massive attention in social media, dominate news headlines, and attract interest from researchers around the globe. While individual domains and networks are studied extensively, one question remains less addressed so far: How does information spread across different channels, considering dynamics between social media, news and, scientific literature? In this paper, we aim to identify frequent patterns in the dissemination of information over multiple channels. Based on an adapted pattern mining algorithm for multivariate time series, we provide strong indications for the existence of distinctive information diffusion effects between social media, news and scientific literature. We find that when all information channels simultaneously cover a certain topic, the preceding period is characterized either by a sole growth of social media coverage or a simultaneous growth of social media and news coverage.



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