In the age of big data, information for the same entity can be obtained from different sources, which is inevitably conflicting. Therefore, aggregation methods are needed to identify the trustworthy information from such conflicting data. Truth discovery, which improves the aggregation results by estimating source trustworthiness and discovering truths simultaneously, has become an emerging field. Most truth discovery methods assume that sources make their claims independently, which may not be true in practice. As a matter of fact, influences among sources are ubiquitous and the claims made by one source may be influenced by others. Although there is some work that considers source correlation, those methods are designed to handle categorical claims, which is not general enough to represent the complicated real world applications. To tackle these challenges in truth discovery, we propose an unsupervised probabilistic model named IATD. The model takes source correlations as prior for influence derivation. To model influences among sources, we introduce "claim trustworthiness", which fuses the trustworthiness of the source which provides the claim and the trustworthiness of its influencers. Besides, the proposed model can handle different data types using different distributions in the probabilistic model. Experiments on real-world datasets show that IATD model can improve the aggregation performance compared with the state-of-the-art truth discovery approaches. The properties of IATD model are further illustrated using simulated datasets.