Automatically predicting information quality in news documents

Authors:
Rong Tang;Kwong Bor Ng;Tomek Strzalkowski;Paul B. Kantor
Affiliations:
University at Albany, Albany, NY;Graduate School of Library and Information Studies, Queens College, CUNY, New York, NY;Univerity at Albany, Albany, NY;Rutgers University, New Brunswick, NJ
Venue:
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Year:
2003

Citing 4
Cited 2

Incorporating quality metrics in centralized/distributed information retrieval on the World Wide Web

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Does “authority” mean quality? predicting expert quality ratings of Web documents

SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
10 Potholes in the Road to Information Quality

Computer
Beyond accuracy: what data quality means to data consumers

Journal of Management Information Systems

'Helpfulness' in online communities: a measure of message quality

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Comparing the performance of us college football teams in the web and on the field

Proceedings of the 20th ACM conference on Hypertext and hypermedia

Quantified Score

Hi-index	0.01

Visualization

Abstract

We report here empirical results of a series of studies aimed at automatically predicting information quality in news documents. Multiple research methods and data analysis techniques enabled a good level of machine prediction of information quality. Procedures regarding user experiments and statistical analysis are described.