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The explosive growth in social network content suggests that the largest “sensor network” yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations...
Signal feature extraction and classification are two common tasks in the signal processing literature. This paper investigates the use of source identities as a common mechanism for enhancing the classification accuracy of social signals. We define social signals as outputs, such as microblog entries, geotags, or uploaded images, contributed by users in a social network. Many classification tasks...
ABSTRACT This paper addresses the challenge of truth discovery from noisy social sensing data. The work is motivated by the emergence of social sensing as a data collection paradigm of growing interest, where humans perform sensory data collection tasks. A challenge in social sensing applications lies in the noisy nature of data. Unlike the case with well-calibrated and well-tested infrastructure...
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