AI for Trustworthiness! Credible User Identification on Social Web for Disaster Response Agencies

Abstract: Although social media provides a vibrant platform to discuss real-world events, the quantity of information generated can overwhelm decision making based on that information. By better understanding who is participating in information sharing, we can more effectively filter information as the event unfolds. Fine-grained understanding of credible sources can even help develop a trusted network of users for specific events or situations. Given the culture of relying on trusted actors for work practices in the humanitarian and disaster response domain, we propose to identify potential credible users as organizational and organizational-affiliated user accounts on social media in realtime for effective information collection and dissemination. Therefore, we examine social media using AI and Machine Learning methods during three types of humanitarian or disaster events and identify key actors responding to social media conversations as organization (business, group, or institution), organization-affiliated (individual with an organizational affiliation), and non-affiliated (individual without organizational affiliation) identities. We propose a credible user classification approach using a diverse set of social, activity, and descriptive representation features extracted from user profile metadata. Our extensive experiments showed a contrasting participation behavior of the user identities by their content practices, such as the use of higher authoritative content sharing by organization and organization-affiliated users. This study provides a direction for designing realtime credible content analytics systems for humanitarian and disaster response agencies.