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Compared to existing methods based on cascade representation, CasSIM simulates information diffusion processes by exploring users\u2019 dual roles in information propagation with three basic factors: users\u2019 <jats:italic>susceptibilities<\/jats:italic>, <jats:italic>influences<\/jats:italic> and <jats:italic>message contents<\/jats:italic>. With effective user profiling, we are the first to capture the topic-specific property of susceptibilities and influences. In addition, the use of graph neural networks allows CasSIM to capture the dynamics of susceptibilities and influences during information diffusion. We evaluate the effectiveness of CasSIM on three real-life datasets and the results show that CasSIM outperforms the state-of-the-art methods in popularity and final adopter prediction.<\/jats:p>","DOI":"10.1007\/s10618-023-00953-5","type":"journal-article","created":{"date-parts":[[2023,8,27]],"date-time":"2023-08-27T13:01:56Z","timestamp":1693141316000},"page":"79-109","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A tale of two roles: exploring topic-specific susceptibility and influence in cascade prediction"],"prefix":"10.1007","volume":"38","author":[{"given":"Ninghan","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xihui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Zhiqiang","family":"Zhong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4521-4112","authenticated-orcid":false,"given":"Jun","family":"Pang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,27]]},"reference":[{"key":"953_CR1","doi-asserted-by":"crossref","unstructured":"Bourigault S, Lamprier S, Gallinari P (2016) Representation learning for information diffusion through social networks: an embedded cascade model. 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