This study focused on "misleading content," one of the six categories of fake news identified by the UK House of Commons Digital, Culture, Media and Sport (DCMS) Committee. Using fictional scenarios designed to re-semble misleading content, we examined how interpersonal evaluations—such as trust, likability, and warmth—influence the intention to help others. Specifically, we analyzed how impressions of five fictional individuals (Individuals A to E) affected participants' willingness to engage in three types of helping intentions: lending lunch money (Wallet Help), helping to find lost items (Contact Help), and agreeing to share a table (Shared Table). Multiple regression analyses revealed that interpersonal evaluations significantly and positively predict-ed all types of helping intentions for all five individuals. The findings indicate that the more favorably a person is evaluated, the more likely they are to receive helping behavior, regardless of the situation, especially in contexts that mimic real-world online interactions.
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