  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.
