@inproceedings{0ecabd619e25436db388722bfc9d21b4,
title = "Towards Reliable Missing Truth Discovery in Online Social Media Sensing Applications",
abstract = "Social media sensing has emerged as a new application paradigm to collect observations from online social media users about the physical environment. A fundamental problem in social media sensing applications lies in estimating the evolving truth of the measured variables and the reliability of data sources without knowing either of them a priori. This problem is referred to as dynamic truth discovery. Two major limitations exist in current truth discovery solutions: I) existing solutions cannot effectively address the missing truth problem where the measured variables do not have any reported measurements from the data sources; ii) the latent correlations among the measured variables were not fully captured and utilized in current solutions. In this paper, we proposed a Reliable Missing Truth Finder (RMTF) to address the above limitations in social media sensing applications. In particular, we develop a novel data-driven technique to identify the lagged and latent correlations among measured variables, and incorporate such correlation information into a holistic spatiotemporal inference model to infer the missing truth. We evaluated the RMTF using the real-world Twitter data feeds. The results show that the RMTF scheme significantly outperforms the state-of-the-art truth discovery solutions by correctly inferring the missing truth of the measured variables.",
keywords = "Missing Truth Discovery, Social Media Sensing, Spatiotemporal Inference",
author = "Daniel Yue and Jose Badilla and Yang Zhang and Dong Wang",
note = "Funding Information: This research is supported in part by the National Science Foundation under Grant No. CBET-1637251, CNS-1566465 and IIS-1447795, Army Research Office under Grant W911NF-17-1-0409, Google 2018 Faculty Research Award. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on. Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018 ; Conference date: 28-08-2018 Through 31-08-2018",
year = "2018",
month = oct,
day = "24",
doi = "10.1109/ASONAM.2018.8508655",
language = "English (US)",
series = "Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "143--150",
editor = "Andrea Tagarelli and Chandan Reddy and Ulrik Brandes",
booktitle = "Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018",
address = "United States",
}