Abstract
Sharing (and thus amplifying) information on social media has an enormous impact, so there is a strong motivation to identify the paths on which information diffuses. Yet, in practice, there are many challenges which previous work address only to a limited degree. In our work, we systematically analyze the data set limitations and their impact, derive a damage model and present an method that utilizes the social graph neighbourhood to infer the missing messages. Initial results are promising, but there are still open questions on managing the huge search space to prioritize the most suitable results and reduce the cost, for which are investigating custom sorting strategies.
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Neumann, J., Fischer, P.M. (2021). Inferring Missing Retweets in Twitter Information Cascades. In: Bellatreche, L., et al. New Trends in Database and Information Systems. ADBIS 2021. Communications in Computer and Information Science, vol 1450. Springer, Cham. https://doi.org/10.1007/978-3-030-85082-1_25
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DOI: https://doi.org/10.1007/978-3-030-85082-1_25
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