IO1-A. 11 types of disinformation
All the information which is - intentionally or not - created to mislead and possibly harm, can be covered by the following 11 types of disinformation which we constructed as a foundation for the De Facto comprehensive model. In 2019 we suggested a new and expanded classification of content types (based on Wardle, 2017). While the classification has to be applicable on all kinds of disinformation, we were looking specifically into an extension to accommodate education and training contexts:
- written information
- oral information
- visual information
- virtual reality (VR) information
- artificial intelligence (AI) information
Further in our model (check other resources on our webspace) we deal with the information carriers, or information mediums, and compare them with the types of disinformation which are presented and described below.
You can preview examples of each content type used for mis- or disinformation by clicking on the title in the left column of the table.
Information types
Description
the use of irony, sarcasm, ridicule, or the like, in exposing, denouncing, or deriding vice, folly, etc.
paid announcements and activities
classification according to rating or rank, or critical article in order to evaluate
wrongly interpreted, explained or incorrectly understood content - with or without intention
a collection of beliefs or practices mistakenly regarded as being based on scientific method
a humorous or malicious deception
a belief that some covert but influential organisation is responsible for an unexplained event
ideological content that includes interpretation of facts or assumptions, although it is claimed to be neutral, and is often meant to harm ideological opponents
genuinely pretending to be someone else in order to deceive others, especially for fraudulent gain
entirely fabricated content, mostly by computer software, spread intentionally to mislead; AI bogus also relates to content which is intentionally created by humans but presented as an output of an impartial and reliable AI system
deep fake combines artificial intelligence and deep learning to mix existing images and videos onto source images or videos in order to mislead
* There is an ongoing debate within the partnership about the distinction between these two types. One argument is that “entirely fabricated content” is not a precise descriptor, and that deliberate manipulation of images and videos and mixing them with truthful elements (deep fake) results in an “entirely fabricated content” as well. So the argument is that the two categories should merge, also because deep fake is typically AI-generated. The other argument goes that there is a clear distinction between doctored content and entirely fabricated content - though the result may be similar, the processes by which they arise are notably different - hence the two types have their idiosyncrasies, and the distinction is justified.
Moreover, some on this side of the argument point that there is the case and real possibility that human actions (and motivations) lead to production of these two types, so the technology argument is not entirely valid.
A compromise for the time being is being found in stating the nature of the debate whenever the two categories are mentioned.
