Truth or Dare: Understanding and Predicting How Users Lie andProvide Untruthful Data Online

Marvin Ramokapane, Gaurav Misra, Jose M. Such, Soren Preibusch

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Individuals are known to lie and/or provide untruthful data when providing information online as a way to protect their privacy. Prior studies have attempted to explain when and why individuals lie online. However, no work has examined how people lie or provide untruthful data online, i.e., the specific strategies they follow to provide untruthful data, or attempted to predict whether people would be truthful or not depending on the specific question/data. To close this gap, we present a large-scale study with over 800 participants. Based on it, we show that it is possible to predict whether users are truthful or not using machine learning with a very high accuracy (89.7%). We also identify four main strategies people employ to provide untruthful data and show the factors that influence the choices of their strategies. We discuss the implications of findings and argue that understanding privacy lies at this level can help both users and data collectors.
Original languageEnglish
Title of host publicationACM SIGCHI 2021
PublisherAssociation for Computing Machinery (ACM)
Publication statusAccepted/In press - 12 Jan 2021
EventACM CHI Conference on Human Factors in Computing Systems - Online, Yokohama, Japan
Duration: 8 May 202113 May 2021
https://chi2021.acm.org/

Conference

ConferenceACM CHI Conference on Human Factors in Computing Systems
Abbreviated titleCHI 2021
CountryJapan
CityYokohama
Period8/05/2113/05/21
Internet address

Structured keywords

  • Cyber Security
  • Privacy lies
  • Untruthful data
  • Privacy Protective Behaviors
  • False information

Keywords

  • Privacy protection
  • Privacy
  • Privacy lies
  • Untruthful data

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