The success of the Web is based largely on its open, decentralized nature; at the same time, that allows for a wide range of perspectives and intentions. Trust is required to foster successful interactions and to filter the abundance of information. In this review, we present a comprehensive survey of trust on the Web in all its contexts. Three main targets of trust are identified: content, services, and people. Trust in the content on the Web, including webpages, websites, and Semantic Web data is addressed first. Then, we move on to look at services including peer-to-peer environments and Web services. This includes a discussion of Web policy frameworks for access control. People are the final group, where we look at the role of trust in web-based social networks and algorithms for inferring trust relationships. Finally, we review applications that rely on trust and address how they utilize trust to improve functionality and interface.

In this paper, we examine to which degree behavioral measures can be used to predict personality. Personality is one factor that dictates people's propensity to trust and their relationships with others. In previous work, we have shown that personality can be predicted relatively accurately by analyzing social media profiles. We demonstrated this using public data from facebook profiles and text from Twitter streams. As social situations are crucial in the formation of one's personality, one's social behavior could be a strong indicator of her personality. Given most users of social media sites typically have a large number of friends and followers, considering only these aspects may not provide an accurate picture of personality. To overcome this problem, we develop a set of measures based on one's behavior towards her friends and followers. We introduce a number of measures that are based on the intensity and number of social interactions one has with friends along a number of dimensions such as reciprocity and priority. We analyze these features along with a set of features based on the textual analysis of the messages sent by the users. We show that behavioral features are very useful in determining personality and perform as well as textual features.

Analysis of online social networks may provide a metric for establishing trust in user-generated content.

Benford’s Law states that, in naturally occurring systems, the frequency of numbers’ first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford’s Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford’s Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual’s social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets.