Individual vulnerabilities scale when they move through social networks. Peer endorsement, identity cues, group belonging, and reputational incentives strongly shape what people believe and share. Humans, far more than bots, are the key drivers of false news virality,although bots amplify reach (Vosoughi et al. 2018). Corrections often have modest effects and sometimes backfire when they threaten identity or come from distrusted sources (Nyhan and Reifler 2010–2015). Behavioral interventions such as accuracy nudges, reputational cues, trusted messengers, and inoculation or pre-bunking campaigns can reduce sharing and improve resilience, although their scale and persistence vary (Rand et al.). Fact checking networks, platform labels, and blockchain based provenance tools are widely used, but their effects remain mixed and can at times trigger reactance.

Students encounter information within platforms whose algorithms, recommendation systems, engagement incentives, and network structures influence what receives attention and how widely it spreads.

Bots and coordinated accounts can amplify information and create perceptions of popularity or consensus. Existing interventions, including fact-checking, content labels, provenance tools, bot detection, accuracy prompts, and algorithmic interventions, can help, but their effectiveness varies across contexts.