Analytical Look - Transparency in Online Informative Technology

Analytical Look - Transparency in Online Informative Technology

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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of innovative technology and exploring engineering 2020-03, Vol.9 (5), p.364-366
Hauptverfasser: Tyagi, Divyanshi, Khare, Divyansh, Ahlawat, Disheak, Mansi, Yadav, Gaurav
Format: Artikel
Sprache:eng
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 366
container_issue 5
container_start_page 364
container_title International journal of innovative technology and exploring engineering
container_volume 9
creator Tyagi, Divyanshi
Khare, Divyansh
Ahlawat, Disheak
Mansi
Yadav, Gaurav
description 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.
doi_str_mv 10.35940/ijitee.E2185.039520
format Article
fullrecord <record><control><sourceid>crossref</sourceid><recordid>TN_cdi_crossref_primary_10_35940_ijitee_E2185_039520</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_35940_ijitee_E2185_039520</sourcerecordid><originalsourceid>FETCH-LOGICAL-c910-35afa3d4c5aeca6725fa2060f69c2617d937cdec0763158c551a13ebeda20c5c3</originalsourceid><addsrcrecordid>eNpN0LFOwzAUBVALgURV-gcM_oGEZzuO4wlVVYFKkbpkjx4vDrikduVESPl7UMPAdO9wdYfD2KOAXGlbwJM_-cm5fC9FpXNQVku4YSspTZUpMPr2X79nm3E8AYBQhahKu2LP24DDPHnCgdcxfvGMNwnDeMHkAs3cB34Mgw-OH0If0xkn_-144-gzxCF-zA_srsdhdJu_XLPmZd_s3rL6-HrYbeuMrIBMaexRdQVpdISlkbpHCSX0pSVZCtNZZahzBKZUQlektUCh3LvrfmekSa1ZsdxSiuOYXN9ekj9jmlsB7ZWhXRjaK0O7MKgfS_lSYg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Analytical Look - Transparency in Online Informative Technology</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Tyagi, Divyanshi ; Khare, Divyansh ; Ahlawat, Disheak ; Mansi ; Yadav, Gaurav</creator><creatorcontrib>Tyagi, Divyanshi ; Khare, Divyansh ; Ahlawat, Disheak ; Mansi ; Yadav, Gaurav ; Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut</creatorcontrib><description>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.</description><identifier>ISSN: 2278-3075</identifier><identifier>EISSN: 2278-3075</identifier><identifier>DOI: 10.35940/ijitee.E2185.039520</identifier><language>eng</language><ispartof>International journal of innovative technology and exploring engineering, 2020-03, Vol.9 (5), p.364-366</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Tyagi, Divyanshi</creatorcontrib><creatorcontrib>Khare, Divyansh</creatorcontrib><creatorcontrib>Ahlawat, Disheak</creatorcontrib><creatorcontrib>Mansi</creatorcontrib><creatorcontrib>Yadav, Gaurav</creatorcontrib><creatorcontrib>Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut</creatorcontrib><title>Analytical Look - Transparency in Online Informative Technology</title><title>International journal of innovative technology and exploring engineering</title><description>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.</description><issn>2278-3075</issn><issn>2278-3075</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNpN0LFOwzAUBVALgURV-gcM_oGEZzuO4wlVVYFKkbpkjx4vDrikduVESPl7UMPAdO9wdYfD2KOAXGlbwJM_-cm5fC9FpXNQVku4YSspTZUpMPr2X79nm3E8AYBQhahKu2LP24DDPHnCgdcxfvGMNwnDeMHkAs3cB34Mgw-OH0If0xkn_-144-gzxCF-zA_srsdhdJu_XLPmZd_s3rL6-HrYbeuMrIBMaexRdQVpdISlkbpHCSX0pSVZCtNZZahzBKZUQlektUCh3LvrfmekSa1ZsdxSiuOYXN9ekj9jmlsB7ZWhXRjaK0O7MKgfS_lSYg</recordid><startdate>20200330</startdate><enddate>20200330</enddate><creator>Tyagi, Divyanshi</creator><creator>Khare, Divyansh</creator><creator>Ahlawat, Disheak</creator><creator>Mansi</creator><creator>Yadav, Gaurav</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20200330</creationdate><title>Analytical Look - Transparency in Online Informative Technology</title><author>Tyagi, Divyanshi ; Khare, Divyansh ; Ahlawat, Disheak ; Mansi ; Yadav, Gaurav</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c910-35afa3d4c5aeca6725fa2060f69c2617d937cdec0763158c551a13ebeda20c5c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Tyagi, Divyanshi</creatorcontrib><creatorcontrib>Khare, Divyansh</creatorcontrib><creatorcontrib>Ahlawat, Disheak</creatorcontrib><creatorcontrib>Mansi</creatorcontrib><creatorcontrib>Yadav, Gaurav</creatorcontrib><creatorcontrib>Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of innovative technology and exploring engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tyagi, Divyanshi</au><au>Khare, Divyansh</au><au>Ahlawat, Disheak</au><au>Mansi</au><au>Yadav, Gaurav</au><aucorp>Pursuing Bachelor of Technology in Computer Science and Engineering from Meerut Institute of Engineering and Technology, Meerut</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analytical Look - Transparency in Online Informative Technology</atitle><jtitle>International journal of innovative technology and exploring engineering</jtitle><date>2020-03-30</date><risdate>2020</risdate><volume>9</volume><issue>5</issue><spage>364</spage><epage>366</epage><pages>364-366</pages><issn>2278-3075</issn><eissn>2278-3075</eissn><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.</abstract><doi>10.35940/ijitee.E2185.039520</doi><tpages>3</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2278-3075
ispartof International journal of innovative technology and exploring engineering, 2020-03, Vol.9 (5), p.364-366
issn 2278-3075
2278-3075
language eng
recordid cdi_crossref_primary_10_35940_ijitee_E2185_039520
source EZB-FREE-00999 freely available EZB journals
title Analytical Look - Transparency in Online Informative Technology
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-22T06%3A42%3A40IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analytical%20Look%20-%20Transparency%20in%20Online%20Informative%20Technology&rft.jtitle=International%20journal%20of%20innovative%20technology%20and%20exploring%20engineering&rft.au=Tyagi,%20Divyanshi&rft.aucorp=Pursuing%20Bachelor%20of%20Technology%20in%20Computer%20Science%20and%20Engineering%20from%20Meerut%20Institute%20of%20Engineering%20and%20Technology,%20Meerut&rft.date=2020-03-30&rft.volume=9&rft.issue=5&rft.spage=364&rft.epage=366&rft.pages=364-366&rft.issn=2278-3075&rft.eissn=2278-3075&rft_id=info:doi/10.35940/ijitee.E2185.039520&rft_dat=%3Ccrossref%3E10_35940_ijitee_E2185_039520%3C/crossref%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true