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.
|Keywords||Network, Personalization, Persuasive, Trust, Twitter|
|Conference||3rd International Workshop on Personalization in Persuasive Technology, PPT 2018|
Arya, A, Hine, M.J, & Khataei, A. (Amirsam). (2018). User trust graph: A model to measure trustworthiness. In CEUR Workshop Proceedings (pp. 93–101).