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Joint estimation of topics and hashtag relevance in cross-lingual tweets

Sen, Procheta, Ganguly, Debasis ORCID: 0000-0003-0050-7138 and Jones, Gareth J.F. ORCID: 0000-0003-2923-8365 (2016) Joint estimation of topics and hashtag relevance in cross-lingual tweets. In: ACM on International Conference on the Theory of Information Retrieval, ICTIR 2016, 12- 6 Sept 2016., Newark, DE, USA. ISBN 978-1-4503-4497-5

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Abstract

Twitter is a widely used platform for sharing news articles. An emerging trend in multi-lingual communities is to share non-English news articles using English tweets in order to spread the news to a wider audience. In general, the choice of relevant hashtags for such tweets depends on the topic of the non-English news article. In this paper, we address the problem of automatically detecting the relevance of the hashtags of such tweets. More specifically, we propose a generative model to jointly model the topics within an English tweet and those within the non-English news article shared from it to predict the relevance of the hashtags of the tweet. For conducting experiments, we compiled a collection of English tweets that share news articles in Bengali (a South Asian language). Our experiments on this dataset demonstrate that this joint estimation based approach using the topics from both the non-English news articles and the tweets proves to be more effective for relevance estimation than that of only using the topics of a tweet itself.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Cross-lingual Tweet tagging; bilingual topic modelling; joint estimation of topic and tag relevance
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Carterette, Ben and Fang, Hui and Lalmas, Mounia and Nie, Jian-Yun, (eds.) Proceedings of the 2016 ACM on International Conference on the Theory of Information Retrieval, ICTIR 2016. . ACM. ISBN 978-1-4503-4497-5
Publisher:ACM
Official URL:http://dx.doi.org/10.1145/2970398.2970425
Copyright Information:© 2016 ACM
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland (SFI) as a part of the ADAPT Centre at DCU (Grant No: 13/RC/2106).
ID Code:23387
Deposited On:30 May 2019 15:31 by Thomas Murtagh . Last Modified 30 May 2019 15:31

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