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      <namePart>Karmarkar, Sathyaveer S.</namePart>
   </name>
   <titleInfo>
      <title>REVEALING LINGUISTIC BIAS</title>
   </titleInfo>
   <originInfo>
      <dateCreated keyDate="yes">2021</dateCreated>
   </originInfo>
   <note displayLabel="Degree Awarded">Summer 2021</note>
   <typeOfResource authority="aat" valueURI="http://vocab.getty.edu/page/aat/300028029">Thesis</typeOfResource>
   <name type="corporate">
      <affiliation>Illinois Institute of Technology</affiliation>
   </name>
   <name type="corporate">
      <namePart>CS / Computer Science</namePart>
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   <name authority="wikidata" authorityURI="https://www.wikidata.org" valueURI="https://www.wikidata.org/wiki/Q58007807">
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      <namePart>Argamon, Shlomo</namePart>
   </name>
   <subject>
      <topic>Linguistics</topic>
   </subject>
   <subject>
      <topic>Artificial intelligence</topic>
   </subject>
   <subject>
      <topic>Bias</topic>
   </subject>
   <subject>
      <topic>Detect Bias</topic>
   </subject>
   <subject>
      <topic>Linguistic Bias</topic>
   </subject>
   <subject>
      <topic>Natural Language Processing</topic>
   </subject>
   <subject>
      <topic>Reveal Bias</topic>
   </subject>
   <subject>
      <topic>Text Bias</topic>
   </subject>
   <language>
      <languageTerm type="code" authority="rfc3066">en</languageTerm>
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   <abstract>Readers currently face bias in articles written by writers who focus more on partiality towards any person or organization than showing the real facts. The study aims to detect and reveal such bias against them and try to portray real facts without any partiality against any person or organization. The data is fetched by selecting various articles from Google, especially those containing some bias in them. The bias was checked by measuring the subjectivity and polarity of the article using multiple libraries such as NLTK etc. We created a google form to take readers’ views showing them randomly either the biased article or the improved article after changing bias and getting their opinions.</abstract>
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