Datas 1

A research team analyzed COVID disinformation trust in 16 countries to predict online misinformation trust and explore ways to enhance caution in filtering online content. In this setting, information explosion, convenient communication, diverse channels, rich history, and contradictions/inaccuracies affect authenticity judgment.

a Directed causal models use nodes for variables and edgesfor direct effects. Low institutional trustand misinformation both cause bad outcomes, with cross-lagged effects.

b Research leveraging randomization, on average, controls for spurious factors and allows causal identification for MisinformationR.

Datas 2

A hypothetical graph shows varying misinformation effects: high-depth bullshit minimally affects beliefs but increases polarization and mistrust, while paltering alters beliefs without affecting trust due to lack of recognition.

Often encountered false information

Troubled by mixed false info in complexity

Affected/Lost due to false info belief

Verify/screen some potentially false info

Overwhelmed by excessive info (esp. false ones) on storage

Survey 1

The five charts on the left offer a compact yet comprehensive overview of the various challenges survey participants confront when navigating the vast landscape of false information online.

These charts show that disinformation is widespread and far-reaching among people, not only confusing and misleading the public, but also directly harming individuals and society. Although some people try to protect themselves, on the whole, it is difficult for the public to effectively deal with the challenge of disinformation in the flood of information.

Specific aspects of being troubled

Survey 2

This bar chart presents the specific aspects of the distress experienced by the surveyed group when they encounter and are deeply troubled by false information.

As illustrated by the chart, the most prominent issue among these disturbances is the overwhelming information overload, which makes it difficult for individuals to sift through and identify false information.

Datas 1

Datas 2

Survey 1

Survey 2

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Spot false information

Background

Datas/Surveys

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