Automated credibility assessment on Twitter
| creativeworkseries.issn | 1508-2806 | |
| dc.contributor.author | Lorek, Krzysztof | |
| dc.contributor.author | Wiciński, Jacek | |
| dc.contributor.author | Jankowski-Lorek, Michał | |
| dc.contributor.author | Gupta, Amit | |
| dc.date.available | 2017-09-21T07:06:19Z | |
| dc.date.issued | 2015 | |
| dc.description | Bibliogr. s. 167-168. | |
| dc.description.abstract | In this paper, we make a practical approach to automated credibility assessment on Twitter. We describe the process behind the design of an automated classifier for information credibility assessment. As an addition, we propose practical implementation of TwitterBOT, a tool which is able to score submitted tweets while working in the native Twitter interface. | en |
| dc.description.placeOfPublication | Kraków | |
| dc.description.version | wersja wydawnicza | pl |
| dc.identifier.doi | https://doi.org/10.7494/csci.2015.16.2.157 | |
| dc.identifier.eissn | 2300-7036 | |
| dc.identifier.issn | 1508-2806 | |
| dc.identifier.nukat | dd2015320053 | pl |
| dc.identifier.uri | https://repo.agh.edu.pl/handle/AGH/49460 | |
| dc.language.iso | eng | |
| dc.publisher | Wydawnictwa AGH | |
| dc.relation.ispartof | Computer Science | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.access | otwarty dostęp | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/legalcode | |
| dc.subject | en | |
| dc.subject | credibility | en |
| dc.subject | Machine Learning Algorithms | en |
| dc.title | Automated credibility assessment on Twitter | en |
| dc.title.related | Computer Science | |
| dc.type | artykuł | |
| dspace.entity.type | Publication | |
| publicationissue.issueNumber | No. 2 | |
| publicationissue.pagination | pp. 157-168 | |
| publicationvolume.volumeNumber | Vol. 16 | |
| relation.isJournalIssueOfPublication | 7c168842-e29e-466a-b93e-5fbed56d4a6e | |
| relation.isJournalIssueOfPublication.latestForDiscovery | 7c168842-e29e-466a-b93e-5fbed56d4a6e | |
| relation.isJournalOfPublication | 020291ee-249b-4dcf-98a3-276a2f7981aa |
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