Joint estimation of topics and hashtag relevance in
cross-lingual tweets
Sen, Procheta, Ganguly, DebasisORCID: 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
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
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