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95% of sponsored influencer posts on X/Twitter were not disclosed

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One hour’s screen use after going to bed increases your risk of insomnia by 59%, scientists find

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AI Writing Tools Quietly Neutralize Human Emotions

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Why dating apps are fueling cosmetic procedures

woman texting

Digital Danger: Screen Time in Preteens Linked to Manic Behavior Two Years Later

giel on social media late a t night

The Loneliness Paradox: Social Media’s Active Users Just as Isolated as Passive Scrollers

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Social Media Bots Create More Chatter But Less Meaningful Conversation, Research Shows

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How Digital Outrage Helps Misinformation Go Viral

The figure depicts a co-retweet network where nodes correspond to individual politicians and an edge is drawn between two nodes if those politicians share common retweeters (see “Methods”). Square nodes correspond to politicians classified as members of the ideological right, star nodes correspond to politicians classified as members of the ideological left, as determined using the latent ideology (see Fig. 2). The network visualization is produced using a force directed drawing algorithm (see “Methods”), in which repulsive forces are applied between pairs of nodes to push nodes apart, and attractive forces are applied to any pair of nodes which are connected by an edge. Nodes are colored according to the country of each elected politician. Canada: Pink, France: Green, Germany: Gray, Italy: Purple, Poland: Blue, Spain: Orange, Turkey: Brown, United Kingdom: Yellow, United States: Red.

Cross-Country Study Reveals Similar Patterns of Political Toxicity on Social Media

Government regimes may be learning new Twitter tactics to quash dissent

Study Shows X/Twitter Algorithm Influences News Quality Over Quantity

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Study: Combined Approach More Effective at Fighting Election Misinformation

Echo chamber formation sharpened by priority

Study Shows Verified Social Media Users Can Drive Opinion Polarization

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Small Group of Users Dominate Anti-Immigration Content on UK Twitter, Study Finds

Communication professor JungHwan Yang is a member of a research team that explored the biases associated with political polls posted on social media, how their results compare with those reported by mainstream media and their potential influence on public discourse.

Will social media polls accurately predict the winner of the U.S. presidential election?

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