Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags (pp351-377)
         Paula Dooley and Bojan Bozic
         doi: https://doi.org/10.26421/JDI1.3-4
Abstracts: 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.
Key words: Wikidata, Twitter, Hashtags, SPARQL, Trending, Microblogging, Kolmogorov-Smirnov, Jaccard Ratio