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- Publisher Website: 10.1145/2737095.2742154
- Scopus: eid_2-s2.0-84954115737
- WOS: WOS:000493278400030
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Conference Paper: Link weight based truth discovery in social sensing
Title | Link weight based truth discovery in social sensing |
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Authors | |
Keywords | Link weight Social sensing Truth discovery |
Issue Date | 2015 |
Citation | IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week), 2015, p. 326-327 How to Cite? |
Abstract | This paper presents a link weight based maximum likelihood estimation framework to solve the truth discovery problem in social sensing applications. Social sensing has emerged as a new paradigm of data collection, where a group of individuals collect and share observations or measurements about the physical world at scale. A key challenge in social sensing applications lies in ascertaining the correctness of reported observations from unvetted data sources with unknown reliability. We refer to this problem as truth discovery. In this paper, we develop a new link weight based truth discovery scheme that solves the truth discovery problem by explicitly considering different degrees of confidence that sources may express on the reported data. The preliminary results show that our new scheme significantly outperforms the-state-of-the-art baselines and improves the accuracy of the truth estimation results in social sensing applications. |
Persistent Identifier | http://hdl.handle.net/10722/308863 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Huang, Chao | - |
dc.contributor.author | Wang, Dong | - |
dc.date.accessioned | 2021-12-08T07:50:17Z | - |
dc.date.available | 2021-12-08T07:50:17Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week), 2015, p. 326-327 | - |
dc.identifier.uri | http://hdl.handle.net/10722/308863 | - |
dc.description.abstract | This paper presents a link weight based maximum likelihood estimation framework to solve the truth discovery problem in social sensing applications. Social sensing has emerged as a new paradigm of data collection, where a group of individuals collect and share observations or measurements about the physical world at scale. A key challenge in social sensing applications lies in ascertaining the correctness of reported observations from unvetted data sources with unknown reliability. We refer to this problem as truth discovery. In this paper, we develop a new link weight based truth discovery scheme that solves the truth discovery problem by explicitly considering different degrees of confidence that sources may express on the reported data. The preliminary results show that our new scheme significantly outperforms the-state-of-the-art baselines and improves the accuracy of the truth estimation results in social sensing applications. | - |
dc.language | eng | - |
dc.relation.ispartof | IPSN 2015 - Proceedings of the 14th International Symposium on Information Processing in Sensor Networks (Part of CPS Week) | - |
dc.subject | Link weight | - |
dc.subject | Social sensing | - |
dc.subject | Truth discovery | - |
dc.title | Link weight based truth discovery in social sensing | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1145/2737095.2742154 | - |
dc.identifier.scopus | eid_2-s2.0-84954115737 | - |
dc.identifier.spage | 326 | - |
dc.identifier.epage | 327 | - |
dc.identifier.isi | WOS:000493278400030 | - |