Trust-based individualization for persuasive presentation builder

For most people, decision-making involves collecting opinion and advice from others who can be trusted. Personalizing a presentation’s content with trustworthy opinions can be very effective towards persuasiveness of the content. While the persuasiveness of presentation is an important factor in face-to-face scenarios, it becomes even more important in an online course or other educational material when the “presenter” cannot interact with audience and attract and influence them. As the final layer of our personalization model, the Pyramid of Individualization, in this paper we present a conceptual model for collecting opinionative information as trustworthy support for the presentation content. We explore selecting a credible publisher (expert) for the supporting opinion as well as the right opinion that is aligned with the intended personalized content

User trust graph: A model to measure trustworthiness

In this paper, we address the problem of evaluating the trustworthiness of a user in different types of Twitter graphs. We discuss this within the context of a persuasive recommender system that aims at creating personalized content using social media and other personal information. Twitter has been established as an alternative type of information resource due to its simplicity and its enormous number of users who transmit diverse information in real time. There is evidence that people are becoming increasingly reliant on others’ opinion through their social network accounts. Trustable opinion is influential in persuading individuals to purchase products or support policy makers. The domain varies from consumer market to news and politics. Evaluating trust between two people is a delicate subject. In Twitter, neither the social graph nor structured data such as total number of likes or common retweets are sufficient to measure the trustworthiness of a user within a given social group. It is important to consider the actual sentiment associated with the tweets shared between the two parties. Existing approaches only consider relationships among users based on structured data. In this paper, we introduce a new approach to calculate a trust score as a function of time and tweet sentiment