Mean world syndrome

Mean world syndrome

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At a local comic con, of all places, a cosplayer and recent sociology graduate introduced me to the term “mean world syndrome”.

Mean world syndrome is a term coined by George Gerbner to describe a phenomenon whereby violence-related content of mass media makes viewers believe that the world is more dangerous than it actually is.

Source: Mean world syndrome – Wikipedia

She says sociology, as a field, suffers from mean world syndrome as all they do today is study things that are mean or sad.

Mean world syndrome is related to “hypodermic needle theory“:

The “magic bullet” or “hypodermic needle theory” of direct influence effects was based on early observations of the effect of mass media, as used by Nazi propaganda and the effects of Hollywood in the 1930s and 1940s.[1] People were assumed to be “uniformly controlled by their biologically based ‘instincts’ and that they react more or less uniformly to whatever ‘stimuli’ came along”.[2] The “Magic Bullet” theory graphically assumes that the media’s message is a bullet fired from the “media gun” into the viewer’s “head”.[3] Similarly, the “Hypodermic Needle Model” uses the same idea of the “shooting” paradigm. It suggests that the media injects its messages straight into the passive audience.[4] This passive audience is immediately affected by these messages. The public essentially cannot escape from the media’s influence, and is therefore considered a “sitting duck”.[4]

In the pre-21st century, most propaganda messaging was large scale, targeting entire groups through mass media.

In the 21st century, we have social media and online tracking systems that enable the target of messages to individuals – which has brought some renewed interest in the “hypodermic needle” concept as modern propaganda injects messages into individuals.

The difference is that today’s massive databases allow for the mass customization of messages. So it is not one generic mass media message, but many individualized messages, coordinated by a massive algorithm

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