@inproceedings{64a4f3731abd4d7c9e30afe6666e0f13,
title = "Finding true and credible information on Twitter",
abstract = "In this paper, we present a unique study of two successful methods for computing message reliability. The first method is based on machine learning and attempts to find a predictive model based on network features. This method is generally geared towards assessing credibility of messages and is able to generate high recall results. The second method is based on a maximum likelihood formulation and attempts to find messages that are corroborated by independent and reliable sources. This method is geared towards finding facts in which humans are treated as binary sensors and is expected to generate high accuracy results but only for those facts that have higher level of corroboration. We show that these two methods can point to similar or quite different predictions depending on the underlying data set. We then illustrate how they can be fused to capture the trade off between favoring true versus credible messages which can either be opinions or not necessarily verifiable.",
author = "S. Sikdar and S. Adali and M. Amin and T. Abdelzaher and K. Chan and Cho, {J. H.} and B. Kang and J. O'Donovan",
note = "Publisher Copyright: {\textcopyright} 2014 International Society of Information Fusion.; 17th International Conference on Information Fusion, FUSION 2014 ; Conference date: 07-07-2014 Through 10-07-2014",
year = "2014",
month = oct,
day = "3",
language = "English (US)",
series = "FUSION 2014 - 17th International Conference on Information Fusion",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "FUSION 2014 - 17th International Conference on Information Fusion",
address = "United States",
}