Shi, BichenBichenShiIfrim, GeorgianaGeorgianaIfrimHurley, Neil J.Neil J.Hurley2017-02-222017-02-222016-09-19http://hdl.handle.net/10197/8373ECML/PKDD PhD Track 2016 : European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Riva del Garda, Italy, 19 September 2016In this paper we propose a framework for tracking and automatically connecting news articles to Twitter conversations as captured by Twitter hashtags. For example, such a system could alert journalists about news that get a lot of Twitter reaction, so they can investigate those conversations for new developments in the story, promote their article to a set of interested consumers, or discover general sentiment towards the story. Mapping articles to hashtags is nevertheless challenging, due to different language style of articles versus tweets, the streaming aspect, and user behavior when marking tweet-terms as hashtags. We track the Irish Times RSS-feed and a focused Twitter stream over a two months period, and present a system that assigns hashtags to each article, based on its Twitter echo. We propose a machine learning approach for classifying article hashtag pairs. Our empirical study shows that our system delivers high precision for this task.enMachine learningStatisticsNews trackingSocial mediaTwitterHashtag recommendationConference Publication2016-11-17https://creativecommons.org/licenses/by-nc-nd/3.0/ie/