Communicating trustworthiness using radar graphs: A detailed look

Abstract

Abstract-The amount of trust we, as human-beings, place in each other or an object (e.g., online information) is typically guided by several trust factors and antecedents. These factors can vary in importance depending on the individual making the trust decision and also on the situation -such is actually the subjective nature of trust. In this paper, we explore this notion of factors' importance by delving into detail on some of our recent user experiments and subsequent findings, partly described in previous work. These experiments used radar graphs to communicate trustworthiness as a function of five trust factors, namely competence, popularity, recency, corroboration and proximity. Here, we expand that work by further considering the importance of each of the factors to participants, while also investigating the correlations between individuals' perceptions of trust, and aspects such as graph area or size and expected scores as calculated by linear regression analysis. More specifically, we focus on outliers and endeavour to understand what is the cause of their existence. This research contributes to the field of communicating trustworthiness now, but is also meant to act as a platform for future, more directed research on visuals intended to communicate trustworthiness.

Computational trust methods for security quantification in the cloud ecosystem

“…Nurse et al present a multicriterial trust visualization using radar charts in Nurse et al (2013) (cf. The more stars a product is given, the higher its rating and thus, its trustworthiness.…”

Two sides of the coin: measuring and communicating the trustworthiness of online information

“…Figure 3 shows the interface that was presented to study participants. This allowed us to assess: (i) whether individuals could understand the detailed factors that they were requesting -we found that they could; (ii) their ability to perceive and comprehend radar graphs to deduce trust -generally, also positive conclusions were drawn; and (iii) the existence of variations in the levels of importance attached to trust sub-factors -findings did point to a clear difference, with Competence being the most important of the five and Popularity being the least important [22].…”

“…The aim of our radar graphs experiment was to explore people's perception of five trust factors, which our previous findings suggest are key to trust online [5,22], and also to assess their importance to individuals in so far as they pertain to judgements on trustworthiness. The factors were: Competence (Cm), the level of knowledge of a person or information source; Proximity (Pr), the geographical closeness of a source to an event of interest; Popularity (Po), how well-known is a source; Recency (Re), how recent or up-to-date is information to the event of interest; and Corroboration (Cr), how well supported the information is by a variety of different sources.…”

Using semantic clustering to support situation awareness on Twitter: the case of world views

“…Other approaches have also attempted to use assessments of individual information items (e.g., tweets) using trust metrics [12,19,20]. While these provide a rigorous and automated approach, they often require reliable and a good quantity of metadata to make appropriate judgements.…”