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This paper develops a new principled frameworkto solve a hardness-aware truth discovery problem in socialsensing applications. Social sensing has emerged as a new applicationparadigm where a large crowd of social sensors (humansor devices on their behalf) are recruited to or voluntarily reportobservations about the physical environment at scale. Theseobservations may be either true or false, and hence...
Social sensing is a new application paradigm of cyber-physical-social systems (CPSS), where a group of individualsvolunteer to report their claims about the physicalenvironment using cyber devices. A fundamental problem insocial sensing application is to ascertain source reliability andthe claim correctness without knowing either of them a priori, which is referred to as truth finding. Several key...
This paper presents a new principled framework for exploiting time-sensitive information to improve the truth discovery accuracy in social sensing applications. This work is motivated by the emergence of social sensing as a new paradigm of collecting observations about the physical environment from humans or devices on their behalf. These observations maybe true or false, and hence are viewed as binary...
This paper develops a new principled framework for exploiting time-sensitive information to improve the truth discovery accuracy in social sensing applications. This work is motivated by the emergence of social sensing as a new paradigm of collecting observations about the physical environment from humans or devices on their behalf. These observations maybe true or false, and hence are viewed as binary...
This paper presents a confidence-aware maximum likelihood estimation framework to solve the truth estimation problem in social sensing applications. Social sensing has emerged as a new paradigm of data collection, where a group of individuals volunteer (or are recruited) to share certain observations or measurements about the physical world. A key challenge in social sensing applications lies in ascertaining...
This paper presents a spatial-temporal aware analytical framework to solve the truth finding problem in social sensing applications. Social sensing has emerged as a new big data application paradigm of collecting observations about the physical environment from social sensors (e.g., humans) or devices on their behalf. The collected observations may be true or false, and hence are viewed as binary...
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...
This paper estimates new confidence bounds on source reliability in social sensing applications. Scalable and robust estimation of source reliability is a key challenge in social sensing where humans or human-operated sensors act as data sources. In order to assess correctness of data, the reliability of sources must first be assessed, yet this is complicated when sources are not a priori known and...
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