Ruben’s data

Matches in Ruben’s data for { <https://dx.doi.org/10.1007/s10619-017-7211-3> ?p ?o }

Showing triples 1 to 100 of ±104 with 100 triples per page.

s10619-017-7211-3 author me.
s10619-017-7211-3 author tom_de_nies.
s10619-017-7211-3 author peter_fischer.
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s10619-017-7211-3 about information_diffusion.
s10619-017-7211-3 about publication.
s10619-017-7211-3 about Provenance.
s10619-017-7211-3 about Social_media.
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s10619-017-7211-3 about Facebook.
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s10619-017-7211-3 author me.
s10619-017-7211-3 author tom_de_nies.
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s10619-017-7211-3 maker peter_fischer.
s10619-017-7211-3 maker io_taxidou.
s10619-017-7211-3 maker me.
s10619-017-7211-3 title "Web-scale Provenance Reconstruction of Implicit Information Diffusion on Social Media".
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s10619-017-7211-3 name "Web-scale Provenance Reconstruction of Implicit Information Diffusion on Social Media".
s10619-017-7211-3 label "Web-scale Provenance Reconstruction of Implicit Information Diffusion on Social Media".
s10619-017-7211-3 name "Web-scale Provenance Reconstruction of Implicit Information Diffusion on Social Media".
s10619-017-7211-3 topic information_diffusion.
s10619-017-7211-3 topic publication.
s10619-017-7211-3 topic Provenance.
s10619-017-7211-3 topic Social_media.
s10619-017-7211-3 topic Research.
s10619-017-7211-3 topic Facebook.
s10619-017-7211-3 topic World_Wide_Web.
s10619-017-7211-3 subject information_diffusion.
s10619-017-7211-3 subject publication.
s10619-017-7211-3 subject Provenance.
s10619-017-7211-3 subject Social_media.
s10619-017-7211-3 subject Research.
s10619-017-7211-3 subject Facebook.
s10619-017-7211-3 subject World_Wide_Web.
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s10619-017-7211-3 topic information_diffusion.
s10619-017-7211-3 topic publication.
s10619-017-7211-3 topic Provenance.
s10619-017-7211-3 topic Social_media.
s10619-017-7211-3 topic Research.
s10619-017-7211-3 topic Facebook.
s10619-017-7211-3 topic World_Wide_Web.
s10619-017-7211-3 abstract "Fast, massive, and viral data diffused on social media affects a large share of the online population, and thus, the (prospective) information diffusion mechanisms behind it are of great interest to researchers. The (retrospective) provenance of such data is equally important because it contributes to the understanding of the relevance and trustworthiness of the information. Furthermore, computing provenance in a timely way is crucial for particular use cases and practitioners, such as online journalists that promptly need to assess particular pieces of information. Social media currently provide insufficient mechanisms for provenance tracking, publication and generation, while state-of-the-art on social media research focuses mainly on explicit diffusion mechanisms (like retweets in Twitter or reshares in Facebook).The implicit diffusion mechanisms remain understudied due to the difficulties of being captured and properly understood. From a technical side, the state of the art for provenance reconstruction evaluates small datasets after the fact, sidestepping requirements for scale and speed of current social media data. In this paper, we investigate the mechanisms of implicit information diffusion by computing its fine-grained provenance. We prove that explicit mechanisms are insufficient to capture influence and our analysis unravels a significant part of implicit interactions and influence in social media. Our approach works incrementally and can be scaled up to cover a truly Web-scale scenario like major events. The results show that (on a single machine) we can process datasets consisting of up to several millions of messages at rates that cover bursty behaviour, without compromising result quality. By doing that, we provide to online journalists and social media users in general, fine grained provenance reconstruction which sheds lights on implicit interactions not captured by social media providers. These results are provided in an online fashion which also allows for fast relevance and trustworthiness assessment.".
s10619-017-7211-3 datePublished "2018".
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