Title :
Impactrank: A Study on News Impact Forecasting
Author :
Tikves, Sukru ; Davulcu, Hasan
Author_Institution :
Comput., Inf., & Decision Syst. Eng., Arizona State Univ., Tempe, AZ, USA
Abstract :
In this paper we developed a framework and a measure for news impact forecasting. We proved the viability of our impact forecasting approach using a SVM based forecaster on six months of NYT corpus - consisting of 16,852 articles. We experimented with different feature selection and ranking algorithms including standard frequency based methods, as well as a new method named ImpactRank. Our ImpactRank based forecaster performed as the best feature ranking technique while providing a graph suitable for browsing and identifying the most influential topics, entities and inter-relationships going into its impact predictions.
Keywords :
forecasting theory; graph theory; information resources; support vector machines; Impactrank; NYT corpus; SVM based forecaster; feature selection; frequency based methods; news impact forecasting; ranking algorithms; Context; Estimation; Forecasting; Gold; Prediction algorithms; Support vector machines; Training data;
Conference_Titel :
Social Computing (SocialCom), 2010 IEEE Second International Conference on
Conference_Location :
Minneapolis, MN
Print_ISBN :
978-1-4244-8439-3
Electronic_ISBN :
978-0-7695-4211-9
DOI :
10.1109/SocialCom.2010.77
