Abstract
In this paper, we present on-going work pursued in the context of the Pheme project. There, the detection of rumors in social media is playing a central role in two use cases. In order to be able to store and to query for information on specific types of rumors that can be circulated in such media (but also in “classical” media), we started to build ontological models of rumors, disputed claims, misinformation and veracity. As rumors can be considered as unverified statements, which after a certain time can be classified as either erroneous information or as facts, there is a need to model also the temporal information associated with any statement. As we are dealing in first line with social media, our modelling work should also cover information diffusion networks and user online behavior, which can also help in classifying a statement as a rumor or a fact. We focus in this paper on the core of our rumor ontology.
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Notes
- 1.
http://www.pheme.eu/. See, also [4].
- 2.
See [9]. Some details have been reported in 2014, Deliverable D8.1: Requirements and Use Case Design document. (http://www.pheme.eu/wp-content/uploads/2014/04/PHEME-D8-1-Use-Case-Requirements.pdf).
- 3.
See http://linkeddata.org/ for more details. Declerck and Lendvai describe in details how to represent social media elements, i.e. hashtags, in the LOD format [3].
- 4.
Quoted from http://en.wikipedia.org/wiki/Pheme.
- 5.
http://www.ontotext.com/proton-ontology/. See also [2, 8].
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
See http://www.w3.org/TeamSubmission/turtle/ for more details.
- 13.
See [1] and http://rdfs.org/sioc/spec/.
- 14.
SIOC stand for “Semantically-Interlinked Online Communities” (see http://rdfs.org/sioc/spec/) and DLPO stand for “Digital.Me LivePost Ontology” (see http://www.semanticdesktop.org/ontologies/2011/10/05/dlpo/).
- 15.
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Acknowledgements
This work presented in this paper has been supported by the PHEME FP7 project (grant No. 611233).
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Declerck, T., Osenova, P., Georgiev, G., Lendvai, P. (2016). Ontological Modelling of Rumors. In: Trandabăţ, D., Gîfu, D. (eds) Linguistic Linked Open Data. RUMOUR 2015. Communications in Computer and Information Science, vol 588. Springer, Cham. https://doi.org/10.1007/978-3-319-32942-0_1
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