ResearchPod Summary
Young job seekers often rely on social media platforms like RedNote to navigate career uncertainty. While these platforms offer authentic peer narratives that provide essential emotional grounding, they also create a "paradox of consumption." Passive browsing of peer achievements frequently triggers upward social comparison, leading to anxiety, cognitive overload, and a sense of "pseudo-clarity" where information is saved but never effectively processed or applied.
The researchers developed JobMate to address this tension by transforming static social media posts into interactive, persona-grounded conversational agents. Unlike traditional AI tools that rely on synthetic data, JobMate uses a multi-stage pipeline to extract structured information from real peer posts. The system presents these as "persona cards" that highlight shared challenges rather than just successes, encouraging lateral comparison. When a user engages with a persona, the interface provides a split-view: the original social media post remains visible as an "authenticity anchor," while an AI agent—grounded in the peer's actual experience and augmented with relevant career resources—facilitates a personalized, active dialogue.
A between-subjects study comparing JobMate to native browsing revealed that while both methods helped users navigate career decisions, JobMate significantly lowered the cognitive effort required for sensemaking. Participants reported that the conversational format redirected their focus from "I am not as good as others" (upward comparison) toward "what should I do next" (constructive self-reframing). The study demonstrates that the negative impacts of social media consumption are not inherent to the content itself, but are largely shaped by the interaction modality. By shifting from passive scrolling to active, persona-grounded dialogue, systems can better balance the need for emotional support with the need for actionable career guidance.
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