PropertyValue
?:about
?:abstract
  • The COVID-19 pandemic in the era of Industry 4.0 has fueled the spread of misinformation and disinformation on social media. Unregulated discussions and the 24-hour news cycle have facilitated HOAXes, often aimed at inciting public unrest. This phenomenon has led to a lack of trust in the validity of Covid-19 information, contributing to the rise in cases. Consequently, the government cannot address this issue alone and requires support from law enforcement. This study examines the problem from a police science perspective, proposing digital policing (e-policing) as a solution to identify and address circulating HOAXes. The Indonesian National Police's Priority Program (PRESISI) outlines an innovative approach by introducing the Polisinyo Urang Sawahlunto (PUAS) application. This research uses a qualitative approach to analyse the relevant findings, including interviews, observations, and literature reviews. The results show that: 1) The spread of HOAX information via social media within the Sawahlunto Police's jurisdiction is still widespread; 2) The PUAS application's HOAX checker feature can identify HOAXes related to COVID-19, helping the police gather valid information to share with the public; 3) However, when analysed through management theory, the application's implementation still lacks fulfilment of the Man, Money, Method, and Machines components. As a result, PUAS has not yet been fully implemented and requires further development. (xsd:string)
?:contributor
?:dateModified
  • 2024 (xsd:gyear)
?:datePublished
  • 2024 (xsd:gyear)
?:doi
  • 10.22178/pos.112-20 ()
?:hasFulltext
  • true (xsd:boolean)
is ?:hasPart of
?:inLanguage
  • en (xsd:string)
?:isPartOf
?:issn
  • 2413-9009 ()
?:issueNumber
  • 12 (xsd:string)
?:linksDOI
?:name
  • Application of Information Technology in Identifying HOAX Information Circulating on Social Media Related to the COVID-19 Pandemic by the Sawahlunto Police Cyber Patrol Team (xsd:string)
?:provider
?:publicationType
  • Zeitschriftenartikel (xsd:string)
  • journal_article (en)
?:sourceInfo
  • GESIS-SSOAR (xsd:string)
  • In: Path of Science, 10, 2024, 12, 2033-2044 (xsd:string)
rdf:type
?:url
?:volumeNumber
  • 10 (xsd:string)