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

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.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, US

Explosive Percolation on Directed Networks Due to Monotonic Flow of Activity

An important class of real-world networks have directed edges, and in addition, some rank ordering on the nodes, for instance the "popularity" of users in online social networks. Yet, nearly all research related to explosive percolation has been restricted to undirected networks. Furthermore, information on such rank ordered networks typically flows from higher ranked to lower ranked individuals, such as follower relations, replies and retweets on Twitter. Here we introduce a simple percolation process on an ordered, directed network where edges are added monotonically with respect to the rank ordering. We show with a numerical approach that the emergence of a dominant strongly connected component appears to be discontinuous. Large scale connectivity occurs at very high density compared with most percolation processes, and this holds not just for the strongly connected component structure but for the weakly connected component structure as well. We present analysis with branching processes which explains this unusual behavior and gives basic intuition for the underlying mechanisms. We also show that before the emergence of a dominant strongly connected component, multiple giant strongly connected components may exist simultaneously. By adding a competitive percolation rule with a small bias to link uses of similar rank, we show this leads to formation of two distinct components, one of high ranked users, and one of low ranked users, with little flow between the two components