Ad hominem attacks are the most common way users confront content they perceive as wrong in comment sections beneath news videos, with over 40% of analyzed comments relying on reputation-based insults to oppose earlier replies

A recent study by CISPA Helmholtz Center for Information Security examines how users perceive AI labels and their impact on information credibility. The study found that while AI labels can reduce belief in false content, they also trigger skepticism and shape trust more than the content itself.

A new MIT Sloan research found that conservative users share misinformation at a greater volume than liberal users, explaining why conservatives are suspended more frequently. Conservative Twitter users shared four times more links to low-quality news outlets than liberal users.

Researchers investigated how people decide to confront or avoid unfair behavior, revealing that avoidance is driven by both not wanting to witness inequality and a desire to avoid confrontation. Even those who tend to avoid unfairness will hand out punishment if forced to observe such behavior.

Global education leaders call for collaboration, ethics, and human-centered teaching as they confront the benefits and challenges of AI in education. The discussion emphasized the need for responsible use policies, equitable access to AI tools, and preserving uniquely human qualities in education.

Researchers found that mobile users were less likely to click on phishing links compared to PC users, especially for lower-risk URLs. The study suggests that device use settings affect users' risk assessment abilities, leading to more avoidant behavior on mobile devices.

A recent study by UCL researchers reveals that people with poorer mental health are more prone to browsing negative content online, which further exacerbates their symptoms. The study found that adding 'content labels' to webpages can help users make healthier and more informed decisions about the content they consume.