Abstract
In recent years, research in information diffusion in social media has attracted a lot of attention, since the data produced is fast, massive and viral. The provenance of such data is equally important because it helps to judge the relevance and trustworthiness of the information enclosed in the data. However, social media currently provide insufficient mechanisms for provenance, while models of information diffusion use their own concepts and notations, targeted to specific use cases. In this paper, we present and extend our model for information diffusion and provenance, based on the W3C PROV Data Model. PROV provides a Web-native and interoperable format that allows easy publication of provenance data, and minimizes the integration effort among different systems making use of PROV. We provide computational methods for provenance reconstruction of user interactions based on the investigation of human behaviour on social media.
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Notes
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For detailed specification and formal constraints, see http://semweb.datasciencelab.be/ns/prov-said/.
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Note here that a quote in Twitter is a prov-said:RevisedMessage and a retweet is a prov-said:CopiedMessage.
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Taxidou, I., De Nies, T., Fischer, P.M. (2019). Provenance of Explicit and Implicit Interactions on Social Media with W3C PROV-DM. In: Atzmueller, M., Chin, A., Lemmerich, F., Trattner, C. (eds) Behavioral Analytics in Social and Ubiquitous Environments. MUSE MSM MSM 2015 2015 2016. Lecture Notes in Computer Science(), vol 11406. Springer, Cham. https://doi.org/10.1007/978-3-030-34407-8_7
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