A Survey on Truth Discovery <s> Fine-grained source reliability <s> Accessing online information from various data sources has become a necessary part of our everyday life. Unfortunately such information is not always trustworthy, as different sources are of very different qualities and often provide inaccurate and conflicting information. Existing approaches attack this problem using unsupervised learning methods, and try to infer the confidence of the data value and trustworthiness of each source from each other by assuming values provided by more sources are more accurate. However, because false values can be widespread through copying among different sources and out-of-date data often overwhelm up-to-date data, such bootstrapping methods are often ineffective. In this paper we propose a semi-supervised approach that finds true values with the help of ground truth data. Such ground truth data, even in very small amount, can greatly help us identify trustworthy data sources. Unlike existing studies that only provide iterative algorithms, we derive the optimal solution to our problem and provide an iterative algorithm that converges to it. Experiments show our method achieves higher accuracy than existing approaches, and it can be applied on very huge data sets when implemented with MapReduce. <s> BIB001 </s> A Survey on Truth Discovery <s> Fine-grained source reliability <s> In crowdsourced data aggregation task, there exist conflicts in the answers provided by large numbers of sources on the same set of questions. The most important challenge for this task is to estimate source reliability and select answers that are provided by high-quality sources. Existing work solves this problem by simultaneously estimating sources' reliability and inferring questions' true answers (i.e., the truths). However, these methods assume that a source has the same reliability degree on all the questions, but ignore the fact that sources' reliability may vary significantly among different topics. To capture various expertise levels on different topics, we propose FaitCrowd, a fine grained truth discovery model for the task of aggregating conflicting data collected from multiple users/sources. FaitCrowd jointly models the process of generating question content and sources' provided answers in a probabilistic model to estimate both topical expertise and true answers simultaneously. This leads to a more precise estimation of source reliability. Therefore, FaitCrowd demonstrates better ability to obtain true answers for the questions compared with existing approaches. Experimental results on two real-world datasets show that FaitCrowd can significantly reduce the error rate of aggregation compared with the state-of-the-art multi-source aggregation approaches due to its ability of learning topical expertise from question content and collected answers. <s> BIB002
In some scenarios, it is reasonable to estimate multiple source reliabilities for a single source, and thus the variety in source reliability can be captured. The authors in  demonstrate that when objects can be clustered into sets, it is better to estimate a source reliability degree for each object set. Similarly, as a website may have very different reliabilities for different attributes of objects, the authors in BIB001 treat a website as multiple sources according to attributes and objects, instead of treating the website as a single source. This is equivalent to assigning multiple source reliability degrees to a single website. In BIB002 , the authors propose a probabilistic graphical model to jointly learn the latent topics of questions (objects), the fine-grained source reliability, and the answers to questions (truths). Furthermore, as the number of provided observations becomes very large, the source consistency assumption may not hold any more, and it is more reasonable to have multiple source reliabilities per source.