A mood-sensitive recommendation system in social sensing

This chapter reviews a mood-sensitive (MS) recommendation system in social sensing. This work is motivated by the need to provide reliable information recommendation to users in social sensing. The key idea of social sensing is to use humans as sensors to observe and report events in the physical world. We define the measurements from human sensors as claims. A key challenge in social sensing is truth discovery where the goal is to identify truthful claims from the false ones and estimate the reliability of data sources with minimum prior knowledge on both sources and their claims. While current solutions have made progress on addressing this challenge, an important limitation exists: the mood sensitivity of human sensors has not been fully explored. Therefore, the true claims identified by existing schemes can be biased and lead to useless or even misleading recommendations. In this chapter, we present an MS recommendation system that incorporates the mood sensitivity feature into the truth discovery solution. The reviewed recommendation system estimates (i) the correctness and mood neutrality of claims and (ii) the reliability and mood sensitivity of sources. We compare our model with existing truth discovery solutions using four real-world datasets collected from online social media. The results show the reviewed recommendation system outperformed the baselines by finding more correct and mood neutral claims.