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In large and open environments, users may often encounter entities (e.g. people, products and information) which they have no previous experience with or prior knowledge of. In this case, they usually rely on the experience or knowledge of other users (referred as advisors) in the form of opinions (e.g. reviews and ratings), to choose which entities to interact with. However, in these environments, users can freely express their opinions, and the quality of opinions may then vary. Of all the possible reasons that result in diversity of opinion quality, the following two are the most dominant: (1) users are subjectively different, which thus leads to discrepancy of users’ opinions towards same entities; and (2) some users might be dishonest and lie about their experience with entities. However, some existing approaches may only consider either dishonesty or subjectivity difference, while others cannot accurately distinguish these two different aspects. On the other hand, subjectivity difference and dishonesty, regarded as either the extrinsic or intrinsic characteristics of users (human-beings), are more related to the behavioral and psychological perspectives of users other than merely the statistical probability of data points that prevalently adopted by Computer Science. In other words, it is expected to involve understanding users’ motivation and needs of providing opinions when evaluating them. It is thus worthwhile to incorporate social factors when addressing these two aspects for opinion evaluation. With the aforementioned two issues in mind, we aim to solve the subjectivity and dishonesty problems for opinion evaluation by also taking social factors into consideration in this dissertation. In particular, this dissertation is comprised of four studies. First, we propose a novel trust model stemmed from diffusion theory in Social Science (called DiffTrust), to evaluate the opinions of advisors by modeling their trustworthiness. Trust has been recognized as a diffusive concept. When modeling trust, it is crucial to

Coping with subjectivity and dishonesty in opinion evaluation by exploiting social factors