Social media (i.e., Twitter, Facebook, Flickr, YouTube) and other services with user-generated content have made a staggering amount of information (and misinformation) available. Government officials seek to leverage these resources to improve services and communication with citizens. Yet, the sheer volume of social data streams generates substantial noise that must be filtered. Nonetheless, potential exists to identify issues in real time, such that emergency management can monitor and respond to issues concerning public safety. By detecting meaningful patterns and trends in the stream of messages and information flow, events can be identified as spikes in activity, while meaning can be deciphered through changes in content. This paper presents findings from a pilot study we conducted between June and December 2010 with government officials in Arlington, Virginia (and the greater National Capitol Region around Washington, DC) with a view to understanding the use of social media by government officials as well as community organizations, businesses and the public. We are especially interested in understanding social media use in crisis situations (whether severe or fairly common, such as traffic or weather crises).

In this paper we propose a novel method for multimedia semantic indexing using model vectors. Model vectors provide a semantic signature for multimedia documents by capturing the detection of concepts broadly across a lexicon using a set of independent binary classifiers. While recent techniques have been developed for detecting simple generic concepts such as indoors, outdoors, nature, manmade, faces, people, speech, music, and so forth [1], these labels directly support only a small number of queries. Model vectors address the problem of answering queries for which relationships to specific concepts is either unknown or indirect by developing a basis across across the lexicon. In the simplest case, each model vector dimension corresponds to the confidence score by which a corresponding concept from the lexicon is detected. However, we show how other information such as relevance, reliability and concept correlation can also be incorporated. Overall, the model vectors can be used in a variety of methods for multimedia indexing, including model-based retrieval, relevance feedback searching and concept querying. In this paper, we present the model vector method and study different strategies for computing and comparing model vectors. We empirically evaluate the retrieval effectiveness of the model vector approach compared to other search methods in a large video retrieval testbed.

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