A survey from 2020 found that nearly half of journalists worldwide believed regular citizens to be the main source of disinformation. The international survey asked journalists to cite the top source of disinformation, with 46 percent of journalists stating politicians and elected officials.

Background: Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the "citation benefit". Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results: Here, we look at citation rates while controlling for many known citation predictors, and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations th...

A survey from 2020 found that 66 percent of journalists ranked Facebook as the platform with the most prolific spread of disinformation on COVID-19 worldwide. Second on the list was Twitter, while a further 35 percent identified Facebook-owned WhatsApp as another platform where misinformation surrounding coronavirus was spread.

BackgroundAs statisticians develop new methodological approaches, there are many factors that influence whether others will utilize their work. This paper is a bibliometric study that identifies and quantifies associations between characteristics of new biostatistics methods and their citation counts. Of primary interest was the association between numbers of citations and whether software code was available to the reader.MethodsStatistics journal articles published in 2010 from 35 statistical journals were reviewed by two biostatisticians. Generalized linear mixed models were used to determine which characteristics (author, article, and journal) were independently associated with citation counts (as of April 1, 2017) in other peer-reviewed articles.ResultsOf 722 articles reviewed, 428 were classified as new biostatistics methods. In a multivariable model, for articles that were not freely accessible on the journal’s website, having code available appeared to offer no boost to the number of citations (adjusted rate ratio = 0.96, 95% CI = 0.74 to 1.24, p = 0.74); however, for articles that were freely accessible on the journal’s website, having code available was associated with a 2-fold increase in the number of citations (adjusted rate ratio = 2.01, 95% CI = 1.30 to 3.10, p = 0.002). Higher citation rates were also associated with higher numbers of references, longer articles, SCImago Journal Rank indicator (SJR), and total numbers of publications among authors, with the strongest impact on citation rates coming from SJR (rate ratio = 1.21 for a 1-unit increase in SJR; 95% CI = 1.11 to 1.32).ConclusionThese analyses shed new insight into factors associated with citation rates of articles on new biostatistical methods. Making computer code available to readers is a goal worth striving for that may enhance biostatistics knowledge translation.

This statistic displays the most reliable sources of data according to professionals in the market research industry in the United States in 2017. During the survey, 32 percent of respondents cited marketing analytics as the most reliable data source.

Number of visits in 2019 for a sample of 23462 English Wikipedia articles which contain references to academic sources which have a green open access copy available but not yet used. The consultation statistics were retrieved from the Wikimedia pageviews API using the Python client (script also included). The sample was selected among articles which in April 2020 had at least one citation of an academic paper (using the "cite journal" template) for which OAbot (through Unpaywall data) had found a green open access URL to add (gratis open access, not necessarily libre open access). Data shows that the top 1 % most visited articles received 30 % of the visits: over 500 million in the year, corresponding to 1 million potential citation link clicks to distribute across all references assuming a 0.2 % click-through rate per Piccardi et al. (2020).

*Based on ISI - Thomson - Reuters Web of Science [305].aRanked by total number of citations received by all editions in all years (from year of publication until 31 December 2011).bFor identification purposes: only the first authors and an abbreviated title are mentioned; the full reference is provided in the list of references.cAll editions, except for Hill's Principles of medical statistics.c1dYears elapsed from first edition until the end of 2011.eSimple average from year of publication until 31 December 2011 (all editions).fThe data source does not allow to distinguish citations to each volume.gWhile only the main editions are mentioned in the Table and References, citations to all editions were counted.hThe corresponding (identical) journal article [337] received 2369 citations.