Champions for social good: How can we discover social sentiment and attitude-driven patterns in prosocial communication?

• Main Authors: MUKKAMALA, Raghava Rao, KAUFFMAN, Robert J., HENRIKSEN, Helle Zinner
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Data Science for Social Good; Deep Learning; Influencers; Machine Learning; Natural Language Processing; Prosocial Behavior; Sentiment Analytics; Social Informedness Theory; Social Outreach; Twitter; Databases and Information Systems; Organizational Communication; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/8376; https://ink.library.smu.edu.sg/context/sis_research/article/9379/viewcontent/Champions_for_Social_Good__How_Can_We_Discover_Social_Sentiment_a__1_.pdf
• Institution: Singapore Management University

The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strategy to inform people about displaced populations and refugee emergencies. It is actively engaging public figures to increase awareness through its prosocial communications and improve social informedness and support for policy changes in its services. We studied the Twitter communications of UNHCR social media champions and investigated their role as high-profile influencers. In this study, we offer a design science research and data analytics framework and propositions based on the social informedness theory we propose in this paper to assess communication about UNHCR’s mission. Two variables—refugee-emergency and champion type—relate to the informedness of UNHCR champions’ followers. Based on a Twitter sentiment and attitude corpus, we applied a five-step design science analytics framework involving machine learning and natural language processing to test how the emergency type and champion type impact social communication patterns. Positive and neutral sentiment dominated the tweets of the champions and their followers for most refugee-emergency types. High participation-intensity champions emphasized high-intensity emergencies with tweet patterns reflecting dominant positive or neutral sentiment and sharing/liking attitudes. However, we found that sports figures effects were limited in spreading UNHCR’s message, despite their millions of followers. We demonstrate the power of data science for prosocial policy based on refugee crisis awareness and instantiate our methods and knowledge contributions in a research framework that derives knowledge, decisions, and actions from behavioral, design, and economics of information systems perspectives.