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Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags

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Bojan BožićAuthors Info & Claims

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

Pages 166 - 175

https://doi.org/10.1145/3366030.3366048

Published: 22 February 2020 Publication History

Abstract

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.

References

[1]

Ahuja, S. and Dubey, G. (2017). Clustering and sentiment analysis on twitter data. In 2017 2nd International Conference on Telecommunication and Networks (âĎą-NET), page 1âĂŞ5.

[2]

Al Tamime, R., Giordano, R., and Hall, W. (2018). Observing burstiness in wikipedia articles during new disease outbreaks. In Proceedings of the 10th ACM Conference on Web Science - WebSci âĂ&Zacute;18, page 117âĂŞ126. ACM Press.

Digital Library

[3]

Alsaadi, H. I., Almajmaie, L. K., and Mahmood, W. A. (2017). Forecasting of twitter hashtag temporal dynamics using locally weighted projection regression. In 2017 International Conference on Engineering and Technology (ICET), page 1âĂŞ4.

[4]

Arulselvi, A. C., Sendhilkumar, S., and Mahalakshmi, S. (2017). Classification of tweets for sentiment and trend analysis. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), page 566âĂŞ573.

[5]

Bielefeldt, A., Gonsior, J., and Krötzsch, M. (2018). Practical linked data access via SPARQL: the case of wikidata. In Proceedings of the WWW2018 Workshop on Linked Data on the Web(LDOW-18), volume 2073 of CEUR Workshop Proceedings. CEUR-WS.org.

[6]

Doshi, Z., Nadkarni, S., Ajmera, K., and Shah, N. (2017). Tweeranalyzer: Twitter trend detection and visualization. In 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), page 1âĂŞ6.

[7]

DâĂ&Zacute;Alberto, P. and Dasdan, A. (2011). On the weakenesses of correlation measures used for search enginesâĂ&Zacute; results (unsupervised comparison of search engine rankings). arXiv:1107.2691 [cs, stat]. arXiv: 1107.2691.

[8]

Erxleben, F., GÃijnther, M., KrÃűtzsch, M., Mendez, J., and VrandeÄ&ENG;iÄ&Gbreve;, D. (2014). Introducing Wikidata to the Linked Data Web, volume 8796, page 50âĂŞ65. Springer International Publishing.

[9]

Goldfarb, D. and Merkl, D. (2018). Visualizing art historical developments using the getty ulan, wikipedia and wikidata. In 2018 22nd International Conference Information Visualisation (IV), page 459âĂŞ466. IEEE.

[10]

Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Keim, D. A., Haug, L., and Hsu, M. (2011). Visual sentiment analysis on twitter data streams. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), page 277âĂŞ278.

[11]

Haripriya, A. and Kumari, S. (2017). Real time analysis of top trending event on twitter: Lexicon based approach. In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), page 1âĂŞ4.

[12]

Heindorf, S., Potthast, M., Engels, G., and Stein, B. (2017). Overview of the wikidata vandalism detection task at wsdm cup 2017. arXiv:1712.05956 [cs]. arXiv: 1712.05956.

[13]

Jhandir, M. Z., Tenvir, A., On, B.-W., Lee, I., and Choi, G. S. (2017). Controversy detection in wikipedia using semantic dissimilarity. Information Sciences, 418âĂŞ419:581âĂŞ600.

[14]

Kaffee, L.-A. and Simperl, E. (2018). Analysis of editorsâĂ&Zacute; languages in wikidata. In Proceedings of the 14th International Symposium on Open Collaboration - OpenSym âĂ&Zacute;18, page 1âĂŞ5. ACM Press.

Digital Library

[15]

Li, Q., Zhou, B., and Liu, Q. (2016). Can twitter posts predict stock behavior?: A study of stock market with twitter social emotion. In 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), page 359âĂŞ364.

[16]

Medelyan, O., Milne, D., Legg, C., and Witten, I. H. (2009). Mining meaning from wikipedia. International Journal of Human-Computer Studies, 67(9):716âĂŞ754.

Digital Library

[17]

Niwattanakul, S., Singthongchai, J., Naenudorn, E., and Wanapu, S. (2013). Using of jaccard coefficient for keywords similarity. Hong Kong, page 6.

[18]

Sundar, D. S. and Kankanala, M. (2015). Analyzing and predicting lifetime of trends using social networks. In 2015 International Conference on Computer Communication and Informatics (ICCCI), page 1âĂŞ7.

[19]

Tajalizadeh, H. and Boostani, R. (2019). A novel stream clustering framework for spam detection in twitter. IEEE Transactions on Computational Social Systems, page 1âĂŞ10.

[20]

Trupthi, M., Pabboju, S., and Narasimha, G. (2017). Sentiment analysis on twitter using streaming api. In 2017 IEEE 7th International Advance Computing Conference (IACC), page 915âĂŞ919.

[21]

Weissman, S., Ayhan, S., Bradley, J., and Lin, J. (2015). Identifying duplicate and contradictory information in wikipedia. In Proceedings of the 15th ACM/IEEE-CE on Joint Conference on Digital Libraries - JCDL âĂ&Zacute;15, page 57âĂŞ60. ACM Press.

Digital Library

[22]

Wikimedia (2018). Data dumps/faq - meta.

[23]

Xie, W., Zhu, F., Jiang, J., Lim, E., and Wang, K. (2013). Topicsketch: Real-time bursty topic detection from twitter. In 2013 IEEE 13th International Conference on Data Mining, page 837âĂŞ846.

[24]

Zangerle, E., Schmidhammer, G., and Specht, G. (2015). wikipedia on twitter: Analyzing tweets about wikipedia. In Proceedings of the 11th International Symposium on Open Collaboration, OpenSym âĂ&Zacute;15, page 14:1âĂŞ14:8. ACM.

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Cited By

Taurino G(2022)Open-Data, Open-Source, Open-Knowledge: Towards Open-Access Research in Media StudiesThe Palgrave Handbook of Digital and Public Humanities10.1007/978-3-031-11886-9_4(49-68)Online publication date: 5-Nov-2022
https://doi.org/10.1007/978-3-031-11886-9_4
Wandabwa HNaeem MMirza FPears R(2021)Multi-interest semantic changes over time in short-text microblogs▪Knowledge-Based Systems10.1016/j.knosys.2021.107249228:COnline publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107249

Index Terms

Towards Linked Data for Wikidata Revisions and Twitter Trending Hashtags
1. Information systems

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cover image ACM Other conferences

iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services

December 2019

709 pages

ISBN:9781450371797

DOI:10.1145/3366030

Copyright © 2019 ACM.

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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JKU: Johannes Kepler Universität Linz
@WAS: International Organization of Information Integration and Web-based Applications and Services

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2020

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iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services

December 2 - 4, 2019

Munich, Germany

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Cited By

Taurino G(2022)Open-Data, Open-Source, Open-Knowledge: Towards Open-Access Research in Media StudiesThe Palgrave Handbook of Digital and Public Humanities10.1007/978-3-031-11886-9_4(49-68)Online publication date: 5-Nov-2022
https://doi.org/10.1007/978-3-031-11886-9_4
Wandabwa HNaeem MMirza FPears R(2021)Multi-interest semantic changes over time in short-text microblogs▪Knowledge-Based Systems10.1016/j.knosys.2021.107249228:COnline publication date: 23-Aug-2021
https://dl.acm.org/doi/10.1016/j.knosys.2021.107249

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