In the era of data-driven journalism, data analytics can deliver tools to support journalists in connecting to new and developing news stories, e.g., as echoed in micro-blogs such as Twitter, the new citizen-driven media. In 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 that 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 appropriate hashtags is nevertheless very challenging, due to different language styles used in articles versus tweets, the streaming aspect of news and tweets, as well as the user behavior when marking certain tweet-terms as hashtags. As a case-study, we continuously track the RSS feeds of Irish Times news articles 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 and ranking article-hashtag pairs. Our empirical study shows that our system delivers high precision for this task.