Matches in Ruben’s data for { <https://dx.doi.org/10.1145/2806416.2806642> ?p ?o }
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- 2806416.2806642 author me.
- 2806416.2806642 author tom_de_nies.
- 2806416.2806642 author rik_van_de_walle.
- 2806416.2806642 author peter_fischer.
- 2806416.2806642 author io_taxidou.
- 2806416.2806642 author erik_mannens.
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- 2806416.2806642 creator io_taxidou.
- 2806416.2806642 creator erik_mannens.
- 2806416.2806642 creator me.
- 2806416.2806642 about Provenance.
- 2806416.2806642 about Social_media.
- 2806416.2806642 author me.
- 2806416.2806642 author tom_de_nies.
- 2806416.2806642 author rik_van_de_walle.
- 2806416.2806642 author peter_fischer.
- 2806416.2806642 author io_taxidou.
- 2806416.2806642 author erik_mannens.
- 2806416.2806642 author me.
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- 2806416.2806642 type ScholarlyArticle.
- 2806416.2806642 type Article.
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- 2806416.2806642 maker erik_mannens.
- 2806416.2806642 maker me.
- 2806416.2806642 title "Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media".
- 2806416.2806642 isPartOf proceedings_of_the_24th_acm_international_conference_on_information_and_knowledge_management.
- 2806416.2806642 name "Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media".
- 2806416.2806642 label "Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media".
- 2806416.2806642 name "Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media".
- 2806416.2806642 topic Provenance.
- 2806416.2806642 topic Social_media.
- 2806416.2806642 subject Provenance.
- 2806416.2806642 subject Social_media.
- 2806416.2806642 authorList b0_b2693.
- 2806416.2806642 topic Provenance.
- 2806416.2806642 topic Social_media.
- 2806416.2806642 abstract "In order to assess the trustworthiness of information on social media, a consumer needs to understand where this information comes from, and which processes were involved in its creation. The entities, agents and activities involved in the creation of a piece of information are referred to as its provenance, which was standardized by W3C PROV. However, current social media APIs cannot always capture the full lineage of every message, leaving the consumer with incomplete or missing provenance, which is crucial for judging the trust it carries. Therefore in this paper, we propose an approach to reconstruct the provenance of messages on social media on multiple levels. To obtain a fine-grained level of provenance, we use an existing approach to reconstruct information cascades with high certainty, and map them to PROV using the PROV-SAID extension for social media. To obtain a coarse-grained level of provenance, we adapt a similarity-based, fuzzy provenance reconstruction approach – previously applied on news. We illustrate our approach by providing the reconstructed provenance of a limited social media dataset gathered during the 2012 Olympics, for which we were able to reconstruct a significant amount of previously unidentified connections.".
- 2806416.2806642 datePublished "2015".
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