In health emergencies, clear and concise information is crucial. Social listening, both online and offline, helps health organisations capture the concerns and needs of communities. Telephone surveys, an important method for offline social listening, provide direct feedback from individuals and shed light on their understanding, behaviour, and challenges related to health emergencies. By actively listening through these channels, health organisations can better align their strategies, ensure effective communication, and build trust in the community. While telephone surveys are undeniably effective in gaining rich first-hand insights, they also come with several challenges. The nature of these surveys often results in a wealth of unstructured information despite the interviewer's adherence to a script. Respondents may recount detailed anecdotes, express emotions, or digress into related topics in their eagerness to share. This complexity can make the analysis phase daunting. Sifting through the myriad responses to identify recurring themes, feelings, and actionable insights requires great attention and expertise. It also requires ensuring that the essence of each respondent's perspective is captured and accurately represented. While telephone surveys are a treasure trove of information, they also underscore the importance of robust data processing and analysis mechanisms. This is where artificial intelligence (AI) comes in, fundamentally changing the field of data analysis. In the complicated task of deciphering vast amounts of unstructured data from telephone surveys, AI can be a formidable ally.

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