"Joint estimation of topics and hashtag relevance in cross-lingual twee" by Procheta Sen, Debasis Ganguly et al.
 

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

Document Type

Conference Article

Publication Title

ICTIR 2016 - Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval

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.

First Page

291

Last Page

294

DOI

10.1145/2970398.2970425

Publication Date

9-12-2016

Comments

Open Access; Green Open Access

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