Community Segmentation and Inclusive Social Media Listening

Abstract

Social media analytics provide a generalized picture of situational awareness from the conversations happening among communities present in social media channels that are that are, or risk being affected by crises. The generalized nature of results from these analytics leaves underrepresented communities in the background. When considering social media analytics, concerns, sentiment, and needs are perceived as homogenous. However, offline, the community is diverse, often segmented by age group, occupation, or language, to name a few. Through our analysis of interviews from professionals using social media as a source of information in public service organizations, we argue that practitioners might not be perceiving this segmentation from the social media conversation. In addition, practitioners who are aware of this limitation, agree that there is room for improvement and resort to alternative mechanisms to understand, reach, and provide services to these communities in need. Thus, we analyze current perceptions and activities around segmentation and provide suggestions that could inform the design of social media analytics tools that support inclusive public services for all, including persons with disabilities and from other disadvantaged groups.