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Computer
Article
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Computer
Article . 2021 . Peer-reviewed
License: IEEE Copyright
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DBLP
Article . 2021
Data sources: DBLP
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Data and Disinformation

Authors: Norita B. Ahmad; Nash Milic; Mohammed Ibahrine;

Data and Disinformation

Abstract

COVID-19 has accelerated the use of social data to spread fake news, misinformation, and disinformation. Technology countermeasures alone are not sufficient to address the ongoing problem of the malicious use of data. The term fake news is not new; however, it became popular during the U.S. presidential elections in November 2016.1 The first publicly known case of fake news was reported back in 1807, when U.S. President Thomas Jefferson wrote a letter to John Novell saying, “Nothing can now be believed which is seen in a newspaper.”2 Then, in 1835, there was a famous hoax by the editor of The New York Sun, who wrote that an eminent astronomer had observed life on the moon.2 The recent digitalization of news production, distribution, and con-sumption and the massive adoption and uses of social me-dia have significantly contributed to an alarming case of misleading and unreliable information. Adding to that, the COVID-19 pandemic has not only brought fear but also new types of misinformation as people have started to turn to digital platforms for infor-mation about the virus.

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    6
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%
bronze