Using Information Trustworthiness Advice in Decision Making

Lecture Notes in Computer Science

Creese

Goldsmith

et al. 2013

Self Cite

Section: Discussionmentioning

confidence: 96%

Supporting Human Decision-Making Online Using Information-Trustworthiness Metrics

Lecture Notes in Computer Science

Creese

Goldsmith

et al. 2013

Self Cite

“…As indicated, these all operated on three levels of trustworthiness, namely, high, medium and low; the assumption being that the percentage score calculated would have to be mapped to these levels. Figure 2 displays the visuals in the context of the experiment which was conducted on a tablet PC device; full details are available in [20].…”

Section: Visuals For Communicating Trustworthinessmentioning

confidence: 99%

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

Journal of Trust Management

Agrafiotis

Goldsmith

et al. 2014

Self Cite

Information is the currency of the digital age -it is constantly communicated, exchanged and bartered, most commonly to support human understanding and decision-making. While the Internet and Web 2.0 have been pivotal in streamlining many of the information creation and dissemination processes, they have significantly complicated matters for users as well. Most notably, the substantial increase in the amount of content available online has introduced an information overload problem, while also exposing content with largely unknown levels of quality, leaving many users with the difficult question of, what information to trust? In this article we approach this problem from two perspectives, both aimed at supporting human decision-making using online information. First, we focus on the task of measuring the extent to which individuals should trust a piece of openly-sourced information (e.g., from Twitter, Facebook or a blog); this considers a range of factors and metrics in information provenance, quality and infrastructure integrity, and the person's own preferences and opinion. Having calculated a measure of trustworthiness for an information item, we then consider how this rating and the related content could be communicated to users in a cognitively-enhanced manner, so as to build confidence in the information only where and when appropriate. This work concentrates on a range of potential visualisation techniques for trust, with special focus on radar graphs, and draws inspiration from the fields of Human-Computer Interaction (HCI), System Usability and Risk Communication. The novelty of our contribution stems from the comprehensive approach taken to address this very topical problem, ensuring that the trustworthiness of openly-sourced information is adequately measured and effectively communicated to users, thus enabling them to make informed decisions.

“…Along with the actual information-trustworthiness measurements, there has also been some work on how these values should be communicated to users. In [8] for example, a simple trafficlight system (with red, amber and green) is used to convey quality; a technique we also effectively apply to trust in [10]. While in [9], the authors take a more fine-grained approach to presenting trust as they colour the text background of Wikipedia content -from white (high trustworthiness) to dark orange (low trustworthiness) dependent on how trustworthy their system deems that segment of information to be.…”

Section: Related Workmentioning

confidence: 99%

“…Secondly, the utility of those and similar factors (displayed in a radar graph) as a tool to build confidence in a single informationtrustworthiness metric (visualised as a traffic light). The overall goal of this metric was to support decision-makers using online information in distinguishing to what extent to trust content and its source; articles with similar general aims can be seen in [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Communicating trustworthiness using radar graphs: A detailed look

2013 Eleventh Annual Conference on Privacy, Security and Trust

Agrafiotis

Creese

et al. 2013

Self Cite

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.