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
2107219896 1998165099 2032897813 2031490232 2095892879 1566370201 2002842121 2039635113 1567491469
Trust in information is developing into a vitally important topic as the Internet becomes increasingly ubiquitous within society. Although many discussions of trust in this environment focus on issues like security, technical reliability, or e-commerce, few address the problem of trust in the information obtained from the Internet. The authors assert that there is a strong need for theoretical and empirical research on trust within the field of information science. As an initial step, the present study develops a model of trust in digital information by integrating the research on trust from the behavioral and social sciences with the research on information quality and human– computer interaction. The model positions trust as a key mediating variable between information quality and information usage, with important consequences for both the producers and consumers of digital information. The authors close by outlining important directions for future research on trust in information science and technology. © 2008 Wiley Periodicals, Inc. Trust is an integral part of the Semantic Web architecture. Most prior work on trusts focuses on entity-centered issues such as authentication and reputation and does not take into account the content, i.e., the nature and use of the information being exchanged. This paper defines content trust and discusses it in the context of other trust measures that have been previously studied. We introduce several factors that users consider in deciding whether to trust the content provided by a Web resource. Our goal is to discern which of these factors could be captured in practice with minimal user interaction in order to maximize the quality of the system's trust estimates. We present results on a study to determine which factors were more important to capture, and describe a simulation environment that we have designed to study alternative models of content trust. The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than the offline counterpart. In this paper, we identify characteristics of rumors by examining the following three aspects of diffusion: temporal, structural, and linguistic. For the temporal characteristics, we propose a new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have fluctuations over time. We also identify key structural and linguistic differences in the spread of rumors and non-rumors. Our selected features classify rumors with high precision and recall in the range of 87 to 92 , that is higher than other states of the arts on rumor classification. Information that propagates through social networks can carry a lot of false claims. For example, rumors on certain topics can propagate rapidly leading to a large number of nodes reporting the same (incorrect) observations. In this paper, we describe an approach for nding the rumor source and assessing the likelihood that a piece of information is in fact a rumor, in the absence of data provenance information. We model the social network as a directed graph, where vertices represent individuals and directed edges represent information ow (e.g., who follows whom on Twitter). A number of monitor nodes are injected into the network whose job is to report data they receive. Our algorithm identies rumors and their sources by observing which of the monitors received the given piece of information and which did not. We show that, with a sucient number of monitor nodes, it is possible to recognize most rumors and their sources with high accuracy. The spread of false rumours during emergencies can jeopardise the well-being of citizens as they are monitoring the stream of news from social media to stay abreast of the latest updates. In this paper, we describe the methodology we have developed within the PHEME project for the collection and sampling of conversational threads, as well as the tool we have developed to facilitate the annotation of these threads so as to identify rumourous ones. We describe the annotation task conducted on threads collected during the 2014 Ferguson unrest and we present and analyse our findings. Our results show that we can collect effectively social media rumours and identify multiple rumours associated with a range of stories that would have been hard to identify by relying on existing techniques that need manual input of rumour-specific keywords. 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. The paper discusses and analyzes the notion of information quality in terms of a pragmatic philosophy of language. It is argued that the notion of information quality is of great importance, and needs to be situated better within a sound philosophy of information to help frame information quality in a broader conceptual light. It is found that much research on information quality conceptualizes information quality as either an inherent property of the information itself, or as an individual mental construct of the users. The notion of information quality is often not situated within a philosophy of information. This paper outlines a conceptual framework in which information is regarded as a semiotic sign, and extends that notion with Paul Grice's pragmatic philosophy of language to provide a conversational notion of information quality that is contextual and tied to the notion of meaning. Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.