Beyond Topics: From Weak Signals to Persistent Issues in Evolving Public Discourse

Important collective problems may begin as sparse and heterogeneous observations long before they become obvious topics. Existing discourse-analysis methods can identify recurring semantic patterns and track their prevalence over time, but they do not explicitly determine when distributed evidence is sufficient to form a collective issue, or whether subsequent observations reflect the same evolving problem or a genuinely distinct one. We formulate this challenge as Continual Emerging Issue Discovery (CEID), an open-world task that models how collective issues emerge from fragmented evidence, persist as their manifestations change, and differentiate over time. We instantiate CEID with an issue-centric continual GraphRAG that separates Discourse, Evidence, and Issue representations, retains weak signals under insufficient support, and preserves issue identities across temporal updates. We construct a controlled temporal benchmark grounded in real-world public-service feedback and evaluate issue formation, discourse grounding, temporal identity preservation, and emergence latency. Our method improves issue discovery and discourse grounding, substantially strengthens temporal identity preservation, and detects emerging issues earlier than topic-, LLM-, retrieval-, and graph-based baselines. These results suggest that evolving public feedback is better modeled as an evidence-accumulation process over persistent collective issues than as a sequence of independently discovered topics.