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When disaster strikes, those affected often turn to social media to request aid, offer assistance, or share other information in real time. In recent years, data scientists have begun analyzing millions of Facebook posts and tweets in order to study the collective response before, during and after a crisis.��

In the face of this mountain of information, however, it can be hard to identify the most relevant posts and trends. But thanks to a close collaboration between social science and software engineering, University of Colorado Boulder researchers Leysia Palen and Kenneth Anderson are innovating new ways to find the underlying human behaviors hidden within noisy data.

“The trick is understanding the potential of large-volume social media information along with its limits,” says Palen, chair of the����in the����at ���Ҵ�ý�ƽ������. “Just because we have a lot of data doesn’t mean that we have all the answers.”

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