Social sensing has emerged as a new sensing paradigm where humans (or devices on their behalf) collectively report measurements about the physical world. This paper focuses on an optimized task allocation problem in multi- attribute social sensing applications where the goal is to effectively allocate the tasks of collecting multiple attributes of the measured variables to human sensors while respecting the application's budget constraints. While recent progress has been made to tackle the optimized task allocation problem, two important challenges have not been well addressed. The first challenge is "online task allocation": The task allocation schemes need to respond quickly to the potentially large dynamics of the measured variables (e.g., temperature, noise, traffic) in social sensing. Delayed task allocation may lead to inaccurate sensing results and/or unnecessarily high sensing costs. The second challenge is the "multi-attribute constrained optimization": Minimizing the overall sensing error given the dependencies and constraints of multiple attributes of the measured variables is a non-trivial problem to solve. To address the above challenges, this paper develops an Online Optimized Multi-attribute Task Allocation (OO-MTA) scheme inspired by techniques from machine learning and information theory. We evaluate the OO-MTA scheme using an urban sensing dataset collected from a real-world social sensing application. The evaluation results show that OO- MTA scheme significantly outperforms the state-of-the-art baselines in terms of the sensing accuracy.