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However, the sheer volume of crisis-related messages on social media can make it challenging for officials to find pertinent information and understand the public\u2019s perception of evolving risks. To address this issue, crisis informatics researchers have proposed a variety of technological solutions, but there has been limited examination of the cognitive and perceptual processes and subsequent responses of the affected population. Yet, this information is critical for the crisis response officials to gauge public\u2019s understanding of the event, their perception of event-related risk, and perception of incident response and recovery efforts, in turn enabling the officials to craft crisis communication messaging more effectively. Taking cues from the Protective Action Decision Model, we conceptualize a metric (resonance+) that prioritizes the cognitive and perceptual processes of the affected population, quantifying shifts in collective attention and information exposure for each tweet. Based on resonance+, we develop a principled, scalable pipeline that recommends content relating to people\u2019s cognitive and perceptual processes. Our results suggest that resonance+ is generalizable across different types of natural hazards. We have also demonstrated its applicability for near-real-time scenarios. According to the feedback from the target users, the local public information officers in emergency management, the messages recommended by our pipeline are useful in their tasks of understanding public perception and finding hopeful narratives, potentially leading to more effective crisis communications.<\/jats:p>","DOI":"10.1145\/3708492","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T11:25:02Z","timestamp":1736853902000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Resonance+: Operationalizing Protective Action Decision Model for Finding Information Useful for Public Information Officers"],"prefix":"10.1145","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9141-1273","authenticated-orcid":false,"given":"Di","family":"Wang","sequence":"first","affiliation":[{"name":"Kahlert School of Computing, The University of Utah, Salt Lake City, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9200-6914","authenticated-orcid":false,"given":"Marina","family":"Kogan","sequence":"additional","affiliation":[{"name":"Kahlert School of Computing, The University of Utah, Salt Lake City, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"FEMA. 2020. 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