This paper presents a case study of long-term post-retraction citation to falsified clinical trial data (Matsuyama et al. in Chest 128(6):3817–3827, 2005. 10.1378/chest.128.6.3817), demonstrating problems with how the current digital library environment communicates retraction status. Eleven years after its retraction, the paper continues to be cited positively and uncritically to support a medical nutrition intervention, without mention of its 2008 retraction for falsifying data. To date no high quality clinical trials reporting on the efficacy of omega-3 fatty acids on reducing inflammatory markers have been published. Our paper uses network analysis, citation context analysis, and retraction status visibility analysis to illustrate the potential for extended propagation of misinformation over a citation network, updating and extending a case study of the first 6 years of post-retraction citation (Fulton et al. in Publications 3(1):7–26, 2015. 10.3390/publications3010017). The current study covers 148 direct citations from 2006 through 2019 and their 2542 second-generation citations and assesses retraction status visibility of the case study paper and its retraction notice on 12 digital platforms as of 2020. The retraction is not mentioned in 96% (107/112) of direct post-retraction citations for which we were able to conduct citation context analysis. Over 41% (44/107) of direct post-retraction citations that do not mention the retraction describe the case study paper in detail, giving a risk of diffusing misinformation from the case paper. We analyze 152 second-generation citations to the most recent 35 direct citations (2010–2019) that do not mention the retraction but do mention methods or results of the case paper, finding 23 possible diffusions of misinformation from these non-direct citations to the case paper. Link resolving errors from databases show a significant challenge in a reader reaching the retraction notice via a database search. Only 1/8 databases (and 1/9 database records) consistently resolved the retraction notice to its full-text correctly in our tests. Although limited to evaluation of a single case (N = 1), this work demonstrates how retracted research can continue to spread and how the current information environment contributes to this problem.

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Argumentation represents the study of views and opinions that humans express with the goal of reaching a conclusion through logical reasoning. Since the 1950's, several models have been proposed to capture the essence of informal argumentation in different settings. With the emergence of the Web, and then the Semantic Web, this modeling shifted towards ontologies, while from the development perspective, we witnessed an important increase in Web 2.0 human-centered collaborative deliberation tools. Through a review of more than 150 scholarly papers, this article provides a comprehensive and comparative overview of approaches to modeling argumentation for the Social Semantic Web. We start from theoretical foundational models and investigate how they have influenced Social Web tools. We also look into Semantic Web argumentation models. Finally we end with Social Web tools for argumentation, including online applications combining Web 2.0 and Semantic Web technologies, following the path to a global World Wide Argument Web.

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We present the first database-wise study on the citation contexts of retracted papers, which covers 7,813 retracted papers indexed in PubMed, 169,434 citations collected from iCite, and 48,134 citation contexts identified from the XML version of the PubMed Central Open Access Subset. Compared with previous citation studies that focused on comparing citation counts using two time frames (i.e., pre-retraction and post-retraction), our analyses show the longitudinal trends of citations to retracted papers in the past 60 years (1960-2020). Our temporal analyses show that retracted papers continued to be cited, but that old retracted papers stopped being cited as time progressed. Analysis of the text progression of pre- and post-retraction citation contexts shows that retraction did not change the way the retracted papers were cited. Furthermore, among the 13,252 post-retraction citation contexts, only 722 (5.4%) citation contexts acknowledged the retraction. In these 722 citation contexts, the retracted papers were most commonly cited as related work or as an example of problematic science. Our findings deepen the understanding of why retraction does not stop citation and demonstrate that the vast majority of post-retraction citations in biomedicine do not document the retraction. Peer Review https://publons.com/publon/10.1162/qss_a_00155