Ifrim, Georgiana

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Ifrim, Georgiana
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Ifrim, Georgiana

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  • Publication
    Be In The Know: Connecting News Articles to Relevant Twitter Conversations
    (2016-09-19)
    Shi, Bichen 
    ;
    Ifrim, Georgiana 
    ;
    Hurley, Neil J. 
    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 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.
      201
  • Publication
    Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering
    (ACM, 2014-04-08)
    Ifrim, Georgiana 
    ;
    Shi, Bichen 
    ;
    Brigadir, Igor 
    Twitter has become as much of a news media as a social network, and much research has turned to analysing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in [16]. We show that aggressive lettering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.
      1614
  • Publication
    Insight4News: Connecting News to Relevant Social Conversations
    (Springer, 2014-09-19)
    Shi, Bichen 
    ;
    Ifrim, Georgiana 
    ;
    Hurley, Neil J. 
    We present the Insight4News system that connects news articles to social conversations, as echoed in microblogs such as Twitter. Insight4News tracks feeds from mainstream media, e.g., BBC, Irish Times, and extracts relevant topics that summarize the tweet activity around each article, recommends relevant hashtags, and presents complementary views and statistics on the tweet activity, related news articles, and timeline of the story with regard to Twitter reaction. The user can track their own news article or a topic-focused Twitter stream. While many systems tap on the social knowledge of Twitter to help users stay on top of the information wave, none is available for connecting news to relevant Twitter content on a large scale, in real time, with high precision and recall. Insight4News builds on our award winning Twitter topic detection approach and several machine learning components, to deliver news in a social context.
      426Scopus© Citations 6