Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags
This paper uses Twitter as a microblogging platform to link hashtags, which relate the message to a topic that is shared among users, to Wikidata, a central knowledge base of information relying on its members and machine bots to keeping its content up to date. The data is stored in a highly structu...
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| Published in | Journal of data intelligence Vol. 1; no. 3; pp. 351 - 377 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
01.09.2020
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| Online Access | Get full text |
| ISSN | 2577-610X 2577-610X |
| DOI | 10.26421/JDI1.3-4 |
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| Summary: | This paper uses Twitter as a microblogging platform to link hashtags, which relate the message to a topic that is shared among users, to Wikidata, a central knowledge base of information relying on its members and machine bots to keeping its content up to date. The data is stored in a highly structured format, with the added SPARQL Protocol And RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base. Our research, designs and implements a process to stream live Twitter tweets and to parse existing Wikidata revision XML files provided by Wikidata. Furthermore, we identify if a correlation exists between the top Twitter hashtags and Wikidata revisions over a seventy-seven-day period. We have used statistical evaluation tools, such as `Jaccard Ratio' and `Kolmogorov-Smirnov' to investigate a significant statistical correlation between Twitter hashtags and Wikidata revisions over the studied period. |
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| ISSN: | 2577-610X 2577-610X |
| DOI: | 10.26421/JDI1.3-4 |