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During humanitarian crises, people face dangers and need a large amount of information in a short period of time. Such need creates the base for misinformation such as rumors, fake news or hoaxes to spread within and outside the affected community. It could be unintended misinformation with unconfirmed details, or intentional disinformation created to trick people for benefits. It results in information harms that can generate serious short term or long-term consequences. Although some researchers have created misinformation detection systems and algorithms, examined the roles of involved parties, examined the way misinformation spreads and convinces people, very little attention has been paid to the types of misinformation harms. In the context of humanitarian crises, we propose a taxonomy of information harms and assess people’s perception of risk regarding the harms. Such a taxonomy can act as the base for future research to quantitatively measure the harms in specific contexts. Furthermore, perceptions of related people were also investigated in four specifically chosen scenarios through two dimensions: Likelihood of occurrence and Level of impacts of the harms. 

Manifold learning based methods have been widely used for non-linear dimensionality reduction (NLDR). However, in many practical settings, the need to process streaming data is a challenge for such methods, owing to the high computational complexity involved. Moreover, most methods operate under the assumption that the input data is sampled from a single manifold, embedded in a high dimensional space. We propose a method for streaming NLDR when the observed data is either sampled from multiple manifolds or irregularly sampled from a single manifold. We show that existing NLDR methods, such as Isomap, fail in such situations, primarily because they rely on smoothness and continuity of the underlying manifold, which is violated in the scenarios explored in this paper. However, the proposed algorithm is able to learn effectively in presence of multiple, and potentially intersecting, manifolds, while allowing for the input data to arrive as a massive stream.