Fake News in Social Media: Bad Algorithms or Biased Users?

Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic con...

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Main Authors: Franziska Zimmer, Katrin Scheibe, Mechtild Stock, Wolfgang G. Stockck
Format: Article
Language:English
Published: Korea Institute of Science and Technology Information 2019-06-01
Series:Journal of Information Science Theory and Practice
Subjects:
Online Access:https://doi.org/10.1633/JISTaP.2019.7.2.4
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spelling doaj-aadfb6cd753047679c206e665f85cdec2020-11-25T01:49:08ZengKorea Institute of Science and Technology InformationJournal of Information Science Theory and Practice2287-90992287-45772019-06-0172405310.1633/JISTaP.2019.7.2.4Fake News in Social Media: Bad Algorithms or Biased Users?Franziska Zimmer0Katrin Scheibe1Mechtild Stock2Wolfgang G. Stockck3Heinrich Heine UniversityHeinrich Heine UniversityStock-KerpenHeinrich Heine UniversityAlthough fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic construction of filter bubbles, and (2) Are echo chambers of fake news man-made, and if yes, what are the information behavior patterns of those individuals reacting to fake news? We discuss the role of filter bubbles by analyzing social media’s ranking and results’ presentation algorithms. To understand the roles of individuals in the process of making and cultivating echo chambers, we empirically study the effects of fake news on the information behavior of the audience, while working with a case study, applying quantitative and qualitative content analysis of online comments and replies (on a blog and on Reddit). Indeed, we found hints on filter bubbles; however, they are fed by the users’ information behavior and only amplify users’ behavioral patterns. Reading fake news and eventually drafting a comment or a reply may be the result of users’ selective exposure to information leading to a confirmation bias; i.e. users prefer news (including fake news) fitting their pre-existing opinions. However, it is not possible to explain all information behavior patterns following fake news with the theory of selective exposure, but with a variety of further individual cognitive structures, such as non-argumentative or off-topic behavior, denial, moral outrage, meta-comments, insults, satire, and creation of a new rumor.https://doi.org/10.1633/JISTaP.2019.7.2.4fake newstruthinformation behaviorsocial mediafilter bubbleecho chamber
collection DOAJ
language English
format Article
sources DOAJ
author Franziska Zimmer
Katrin Scheibe
Mechtild Stock
Wolfgang G. Stockck
spellingShingle Franziska Zimmer
Katrin Scheibe
Mechtild Stock
Wolfgang G. Stockck
Fake News in Social Media: Bad Algorithms or Biased Users?
Journal of Information Science Theory and Practice
fake news
truth
information behavior
social media
filter bubble
echo chamber
author_facet Franziska Zimmer
Katrin Scheibe
Mechtild Stock
Wolfgang G. Stockck
author_sort Franziska Zimmer
title Fake News in Social Media: Bad Algorithms or Biased Users?
title_short Fake News in Social Media: Bad Algorithms or Biased Users?
title_full Fake News in Social Media: Bad Algorithms or Biased Users?
title_fullStr Fake News in Social Media: Bad Algorithms or Biased Users?
title_full_unstemmed Fake News in Social Media: Bad Algorithms or Biased Users?
title_sort fake news in social media: bad algorithms or biased users?
publisher Korea Institute of Science and Technology Information
series Journal of Information Science Theory and Practice
issn 2287-9099
2287-4577
publishDate 2019-06-01
description Although fake news has been present in human history at any time, nowadays, with social media, deceptive information has a stronger effect on society than before. This article answers two research questions, namely (1) Is the dissemination of fake news supported by machines through the automatic construction of filter bubbles, and (2) Are echo chambers of fake news man-made, and if yes, what are the information behavior patterns of those individuals reacting to fake news? We discuss the role of filter bubbles by analyzing social media’s ranking and results’ presentation algorithms. To understand the roles of individuals in the process of making and cultivating echo chambers, we empirically study the effects of fake news on the information behavior of the audience, while working with a case study, applying quantitative and qualitative content analysis of online comments and replies (on a blog and on Reddit). Indeed, we found hints on filter bubbles; however, they are fed by the users’ information behavior and only amplify users’ behavioral patterns. Reading fake news and eventually drafting a comment or a reply may be the result of users’ selective exposure to information leading to a confirmation bias; i.e. users prefer news (including fake news) fitting their pre-existing opinions. However, it is not possible to explain all information behavior patterns following fake news with the theory of selective exposure, but with a variety of further individual cognitive structures, such as non-argumentative or off-topic behavior, denial, moral outrage, meta-comments, insults, satire, and creation of a new rumor.
topic fake news
truth
information behavior
social media
filter bubble
echo chamber
url https://doi.org/10.1633/JISTaP.2019.7.2.4
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AT mechtildstock fakenewsinsocialmediabadalgorithmsorbiasedusers
AT wolfganggstockck fakenewsinsocialmediabadalgorithmsorbiasedusers
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