Analytical Look – Transparency in Online Informative Technology
Divyanshi Tyagi1, Divyansh Khare2, Disheak Ahlawat3, Mansi4, Vinod Kumar5
1Divyanshi Tyagi*, Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut, India.
2Divyansh Khare, Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut, India.
3Disheak Ahlawat, Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut, India.
4Mansi, Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut.
5Mr. Vinod Kumar, Associate Professor, Department of Computer Science, Meerut Institute of Engineering & Technology, Meerut, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 10, 2020. | PP: 364-366 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2185039520/2020©BEIESP | DOI: 10.35940/ijitee.E2185.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We are predicting the bias (left leaning or right leaning) in online news articles based on text of online articles collected and the publication. The rating (either Left or Right) was assigned by looking at the leaning of the publication as found on mediabiasfactcheck.com. As we know the importance of online news has evolved with the advancement in technology. In order to understand the biasness in online journalism related to text of an article, we used a deep Neural Net to make classifications based on the labeling assigned according to publication. If the political bias of the publisher creeps in and such a correlation is there the AI will be able to learn it. Our training produced a very accurate classification model. This shows that online media is not as transparent when presenting news. The methodology is described ahead.
Keywords: Classification, Journalism, Neural Net, Online News Articles, Polarity.
Scope of the Article: Security Technology and Information Assurance