Open AccessAnalytical Look - Transparency in Online Informative Technology
Author(s) -
Divyanshi Tyagi,
Divyansh Khare,
Disheak Ahlawat,
Mansi,
Gaurav Yadav
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.e2185.039520
Subject(s) - transparency (behavior) , journalism , order (exchange) , computer science , data science , artificial intelligence , sociology , media studies , business , computer security , finance
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.