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TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets

  • Pavlos Fafalios
  • Vasileios Iosifidis
  • Eirini Ntoutsi
  • Stefan Dietze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)

Abstract

Publicly available social media archives facilitate research in a variety of fields, such as data science, sociology or the digital humanities, where Twitter has emerged as one of the most prominent sources. However, obtaining, archiving and annotating large amounts of tweets is costly. In this paper, we describe TweetsKB, a publicly available corpus of currently more than 1.5 billion tweets, spanning almost 5 years (Jan’13–Nov’17). Metadata information about the tweets as well as extracted entities, hashtags, user mentions and sentiment information are exposed using established RDF/S vocabularies. Next to a description of the extraction and annotation process, we present use cases to illustrate scenarios for entity-centric information exploration, data integration and knowledge discovery facilitated by TweetsKB.

Keywords

Twitter RDF Entity linking Sentiment analysis Social media archives 

Notes

Acknowledgements

The work was partially funded by the European Commission for the ERC Advanced Grant ALEXANDRIA under grant No. 339233 and the H2020 Grant No. 687916 (AFEL project), and by the German Research Foundation (DFG) project OSCAR (Opinion Stream Classification with Ensembles and Active leaRners).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.L3S Research CenterUniversity of HannoverHannoverGermany

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