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(05-31-2024, 01:58 PM)jaded Wrote: Can't even say I'm surprised.
Just my own experience has been well intentioned older women sharing, linking,tweeting absolute nonsense as if it was legitimate news. Everything from flat earth, Med-beds,images of weather radar bursts labeled "secret govt weapon!" Q, pizza-gate, the list never ends.
My experience tells me the same.
Although I don't have many people on my Facebook friends list, I have had to correct one of them, an older woman in her 70s, tow or three times, because she shares things that are not true.
Another case I know is my boss, also a woman in her 70s, that believes and shares many things she sees on Tik Tok. Some times she tells me about those things and I have to show her that she is following wrong or false information.
Luckily, both are intelligent enough not to keep on posting the same things, but they fall again and again in those traps.
I think one of the reasons older women are such spreaders of disinformation is because they probably have large numbers of connections to other people with large numbers of connections, so just half a dozen people can spread something to a some hundreds or even a few thousands of people.
Edited to add that, from a political point of view, one the above mentioned women is from the left (as we see it in Portugal, not the definition of "left" they use in the US), the other I do not know.
(05-31-2024, 01:48 AM)Maxmars Wrote: Needless to say, there are still many questions to be answered on how they determine what exactly is a "Fake News Site," because ever since COVID the designation has become the stereotypical trope of 'establishment support' journalism.
They say it on the research:
Quote:Similar to prior work (4, 6), we rely on a source-level definition of fake news as domains that portray as legitimate news outlets but do not have the “editorial norms and processes for ensuring the accuracy and credibility of information” (17). We rely on the manually labeled list of fake news sites by Grinberg et al. (4), updated using NewsGuard ratings, and demonstrate the robustness of the findings to different operationalizations (see supplementary materials, section S3). To focus on political news, we restrict the analysis to tweets with external links that were identified as political by a machine learning classifier that we trained and validated against human coders.
