Abstract
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.
Highlights
We live in a world where the ability to access and publish information is practically considered a human right
Agichtein et al [2] for instance, propose a system that can automatically identify highquality content items in question-and-answer networks using several contextual and intrinsic features. This drive towards automated assessment of social content can be seen in Castillo et al [3] as applied to analysing the credibility of Twitter data, and in Suzuki and Yoshikawa [4], who focus on evaluating editor and text features to determine the quality of Wikipedia articles. These proposals all draw on well-defined sets of sub-factors – e.g., provenance, reputation, competence, corroboration and recency – which tend to be indicative of quality and trust [5], and from these deduce useful metrics and approaches to arrive at a trustworthiness score that may be associated with the online content
Conclusions and future work The proliferation of information in online environments has rendered the designing of tools able to support decision-making using this information more crucial than ever
Summary
We live in a world where the ability to access and publish information is practically considered a human right. Agichtein et al [2] for instance, propose a system that can automatically identify highquality content items in question-and-answer networks using several contextual and intrinsic features This drive towards automated assessment of social content can be seen in Castillo et al [3] as applied to analysing the credibility of Twitter data, and in Suzuki and Yoshikawa [4], who focus on evaluating editor and text features to determine the quality of Wikipedia articles. These proposals all draw on well-defined sets of sub-factors – e.g., provenance, reputation, competence, corroboration and recency – which tend to be indicative of quality and trust [5], and from these deduce useful metrics and approaches to arrive at a trustworthiness score that may be associated with the online content
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