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Although social media provides a vibrant platform to discuss real-worldevents, the quantity of information generated can overwhelm decision makingbased on that information. By better understanding who is participating ininformation sharing, we can more effectively filter information as the eventunfolds. Fine-grained understanding of credible sources can even help develop atrusted network of users for specific events or situations. Given the cultureof relying on trusted actors for work practices in the humanitarian anddisaster response domain, we propose to identify potential credible users asorganizational and organizational-affiliated user accounts on social media inrealtime for effective information collection and dissemination. Therefore, weexamine social media using AI and Machine Learning methods during three typesof humanitarian or disaster events and identify key actors responding to socialmedia conversations as organization (business, group, or institution),organization-affiliated (individual with an organizational affiliation), andnon-affiliated (individual without organizational affiliation) identities. Wepropose a credible user classification approach using a diverse set of social,activity, and descriptive representation features extracted from user profilemetadata. Our extensive experiments showed a contrasting participation behaviorof the user identities by their content practices, such as the use of higherauthoritative content sharing by organization and organization-affiliatedusers. This study provides a direction for designing realtime credible contentanalytics systems for humanitarian and disaster response agencies.

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