While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users’ past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors’ profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis.
Recently there has been progress from experts from different fields of academia in exploring the characteristics of the data source and content that will enable us to determine the veracity of information in an autonomous and efficient manner @cite_29 @cite_21 @cite_1 . Key concepts include information quality, which can be defined as an assessment or measure of how fit an information object is for use, and information trustworthiness, which is the likelihood that a piece of information will preserve a user's trust, or belief, in it @cite_24 . These concepts may overlap and indeed, increasing one (e.g., quality) may lead to an increase in the other (e.g., trustworthiness). Other relevant factors include Accuracy (Free-of-error), Reliability, Objectivity (Bias), Believability (Likelihood, Plausibility of arguments), Popularity, Competence and Provenance @cite_23 @cite_19 @cite_30 @cite_28 @cite_0 .
@cite_30 @cite_28 @cite_29 @cite_21 @cite_1 @cite_24 @cite_19 @cite_0 @cite_23
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In recent years, information trustworthiness has become a serious issue when user-generated contents prevail in our information world. In this paper, we investigate the important problem of estimating information trustworthiness from the perspective of correlating and comparing multiple data sources. To a certain extent, the consistency degree is an indicator of information reliability--Information unanimously agreed by all the sources is more likely to be reliable. Based on this principle, we develop an effective computational approach to identify consistent information from multiple data sources. Particularly, we analyze vast amounts of information collected from multiple review platforms (multiple sources) in which people can rate and review the items they have purchased. The major challenge is that different platforms attract diverse sets of users, and thus information cannot be compared directly at the surface. However, latent reasons hidden in user ratings are mostly shared by multiple sources, and thus inconsistency about an item only appears when some source provides ratings deviating from the common latent reasons. Therefore, we propose a novel two-step procedure to calculate information consistency degrees for a set of items which are rated by multiple sets of users on different platforms. We first build a Multi-Source Deep Belief Network (MSDBN) to identify the common reasons hidden in multi-source rating data, and then calculate a consistency score for each item by comparing individual sources with the reconstructed data derived from the latent reasons. We conduct experiments on real user ratings collected from Orbitz, Priceline and TripAdvisor on all the hotels in Las Vegas and New York City. Experimental results demonstrate that the proposed approach successfully finds the hotels that receive inconsistent, and possibly unreliable, ratings. The importance and value of information cannot be disputed. It is used as basis for menial and mission-critical tasks alike. In a society where information is so easily publicised and freely accessible, however, being able to assess information quality and trustworthiness is paramount. With appreciation of this fact, our paper seeks to navigate these two mature fields and define the latest state-of-the-art. The novelty of this work is found in the provision of an up-to-date review, a research survey which considers and links provenance, quality and trustworthiness, and a literature analysis that includes a first-look review at some of these aspects within the social- media domain. This factor-based review should provide an ideal grounding for future research that assesses interaction between these three topics, which may then also progress to associations with information assurance and security at large. To demonstrate how some of the factors might be considered, we also examine their application to a commonplace scenario. 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. The vast amount of information available online places decision makers wishing to use this content in an advantageous but also very difficult position. The advantages stem from the volume of content from a variety of sources that is readily available; the difficulties arise because of the often unknown quality and trustworthiness of the information – is it fact, opinion or purely meant to deceive? In this paper we reflect on and extend current work on information trust and quality metrics which can be used to address this difficulty. Specifically, we propose new metrics as worthy of consideration and the new combinatorics required to take measurements of the various trust factors into a single score. These feed into our existing overarching policy-based approach that uses trustworthiness metrics to support decision-making online.