CFP last date
22 July 2024
Reseach Article

News Source Evaluation and Visualization System

by Abdullah Al-Barakati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 8
Year of Publication: 2016
Authors: Abdullah Al-Barakati

Abdullah Al-Barakati . News Source Evaluation and Visualization System. International Journal of Computer Applications. 154, 8 ( Nov 2016), 1-12. DOI=10.5120/ijca2016912178

@article{ 10.5120/ijca2016912178,
author = { Abdullah Al-Barakati },
title = { News Source Evaluation and Visualization System },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 8 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 1-12 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016912178 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:59:39.778248+05:30
%A Abdullah Al-Barakati
%T News Source Evaluation and Visualization System
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 8
%P 1-12
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

The advent of Web 2.0 and modern internet technologies was associated with a revolution in online contents especially those that are news-oriented. The proliferation of an array of online mediums for news dissemination and aggregation led to the availability of a constant flow of news contents to end-users. Moreover, the advent of Social Media (SM) platforms meant that the Web was transformed to the biggest live news platform on the planet. In an Era when online users are exposed to an overwhelming stream of news contents, there is an increasing need for tools to empower them to quickly glean and digest the information that they are looking for. Users also need effective tools for news intelligence and prediction to keep abreast of their interests. This paper proposes an interactive online system that will enable users to have an analytical view on the news feeds that are related to their interests. The Visual News Screener (VNS) will aim to surpass the traditional news aggregation systems by its ability to evaluate the effectiveness in which a given news source covers a certain news topic or issue. VNS will have the flexibility of analyzing a plethora of news sources and visually summarize the aggregated data within a customized dashboard (the News Screener). The news contents that VNS will analyze will include RSS feeds, SM feeds, crawled online news sources and articles. The visualization process will depend on the actual context that the user is interested in. Furthermore, the history, current status and potential development patterns of the monitored news issue(s) will also be analyzed and visualized.

  1. Norris, P. 2000. The Worldwide Digital Divide: Information Poverty, the Internet and Development, The Annual Meeting of the Political Studies Association of the UK London Harvard University.
  2. Mitchelstein, E. and Boczkowski, P. J. 2009. Between tradition and change A review of recent research on online news production journalism 562-586.
  3. Vasileios M., Spyros G., Georgios P., Walter K., Jorg S., Roeland O., Marijn H., and Franciska J. 2012. A System for the Semantic Multimodal Analysis of News Audio-Visual Content, EURASIP Journal on Advances in Signal Processing
  4. Allan S. 2006. Online News: Journalism and the Internet, Maidenhead Open University Press.
  5. Martin C., and John S. 2008. The Future of Newspapers, Journalism Studies 650-661.
  6. Flew, T. 2009. Democracy, participation and convergent media: case studies in contemporary online news journalism in Australia, Communication, Politics & Culture 87-109.
  7. Lebedev, E. 2016. The Independent,
  8. Sylvia O., Hyejoon R., and Amy Z. 2013. Mobile News Adoption among Young Adults Examining the Roles of Perceptions, News Consumption, and Media Usage, Journalism & Mass Communication Quarterly.
  9. Sriram K., and Shyam S. 2006. The Psychological Appeal of Personalized Content in Web Portals: Does Customization Affect Attitudes and Behavior?, Journal of Communication.
  10. Dragomir R., Jahna, O. and Adam W. 2005. NewsInEssence: summarizing online news topics, Communications of the ACM 95-98.
  11. Sitaram A., Bernardo H. 2010. Predicting the Future with Social Media, Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE/WIC/ACM 492 - 499 Toronto, IEEE.
  12. Hudson, A. 2012. BBC,
  13. Gill, K. 2005. Blogging, RSS and the Information Landscape: A Look at Online News, WWW 2005 2nd Annual Workshop on the Weblogging Ecosystem: Aggregation, Analysis and Dynamics 120-122.
  14. Harumi M., Masao K., Hidekazu A., Atsushi S., Hideaki T., and Toyoaki N. 1997. Weak Information Structures for Community Information Sharing, international journal of knowledge-based and intelligent engineering systems 225-234.
  15. Chris P., and David D. 2009. Making Online News: The Ethnography of New Media Production, Journal of Information Technology & Politics 189-190.
  16. Quinn, J. 2014. Associated Press v. Meltwater: Are Courts Being Fair to News Aggregators? Minnesota Journal of Law, Science & Technology 1189-1219.
  17. Young-Woo S., Joseph G., and Katia S. 2004. Financial News Analysis for Intelligent Portfolio Management, Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania.
  18. Namrata G., Manja S., and Steven S. 2009. Large-Scale Sentiment Analysis for News and Blogs, 3rd Int'l AAAI Conference on Weblogs and Social Media184-188San Jose, California Association for the Advancement of Artificial Intelligence.
  19. Nabeela A., Gaber M., and Mihaela C. 2013. SA-E: Sentiment Analysis for Education, 5th KES International Conference on Intelligent Decision Technologies 155-160, Sesimbra.
  20. Bo P., and Lillian L. 2002. Thumbs up?: sentiment classification using machine learning techniques, The ACL-02 conference on Empirical methods in natural language processing 79-86 Stroudsburg, PAACM.
  21. Nasukawa. J. Yi, T., Bunescu, R. and Niblack, W. 2003. Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques, Data Mining, ICDM 2003. Third IEEE International Conference, 427-434 San Jose, CAIEEE.
  22. Takaharu T., and Atsuhiro T. 2008. News Aggregating System with Automatic Summarization, INFOS2008, Cairo-Egypt.
  23. Levon L., Dimitrios K., and Steven S. 2005. Lydia: A System for Large-Scale News Analysis, String Processing and Information Retrieval 161-166.
  24. Dipanjan D., and Andre F.T. 2014. A Survey on Automatic Text Summarization International Journal of Computer Science and Information Technologies 7889-7893.
  25. Luke M., Jinzhu J., Brian G., Bin Y., and Laurent G. 2011. Summarizing Large-scale, Multiple-document News Data: Sparse Methods and Human Validation Berkeley, CA University of California, Berkeley.
  26. Brian G., Jinzhu J., Luke M., Laurent G., Bin Y., and Sophie C. 2010. Discovering word associations in news media via feature selection and sparse classification, The international conference on Multimedia information retrieval 211-220 New York, NYACm.
  27. Lekha C., Tat-Seng C., and Chin-Hui L. 2003. A Multi-Modal Approach to Story Segmentation for News Video, World Wide Web 187-208.
  28. Qi, W. Gu, L., Jiang, H., and Chen, X. 2000. Integrating visual, audio and text analysis for news video International Conference on Image Processing 520 - 523 Vancouver, BCIEEE.
  29. Salton, G. 1989. Automatic text processing: the transformation, analysis, and retrieval of information by computer, Boston Addison-Wesley Longman Publishing Co., In BBC.
  30. Arvind N., Jeevan S., Alexandre P., and Andrew M. 2015. Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector SpaceIthaca, NY Cornell University.
  31. Christian G., Filippo R., and Paolo T. (2006), Web crawlers compared, International Journal of Web Information Systems 85 - 94.
  32. Lapalme, J., Que., Aboulhamid, M., Nicolescu, G., and Charest, L. 2004. .NET framework - a solution for the next generation tools for system-level modeling and simulation, Design, Automation and Test in Europe Conference and Exhibition 732 - 733 Montreal, IEEE.
  33. Marston, T. 2004. The Model-View-Controller (MVC) Design Pattern for PHP, www./php-mysql/model-view-controller.html.
  34. LI Y. 2005. Improvement and Application of MVC Design Patterns,
  35. Karl A., and Dan J. 2012. Mobile e-services using HTML5, Local Computer Networks Workshops (LCN Workshops)814 - 819Clearwater, Clearwater, FL, IEEE.
Index Terms

Computer Science
Information Sciences


news analysis news visualization online news news intelligence source efficiency