Computational protocol: Quantifying the Twitter Influence of Third Party Commercial Entities versus Healthcare Providers in Thirteen Medical Conferences from 2011 – 2013

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Protocol publication

[…] We performed content analyses using NOD Analytics ( We used WordItOut ( to graphically represent the popular scientific topics in each Twitter channel. We calculated frequencies per category for: 1) number of Twitter accounts that authored tweets, 2) number of Twitter accounts that were mentioned within a tweet, 3) number of tweets composed. We performed chi-square tests to compare these data using JMP Pro version 10.0.0 (SAS, Cary, North Carolina). We calculated PageRank using the NodeXL plugin ( for Microsoft Excel 2013 (Microsoft, Redmond, Washington). Median and interquartile ranges for the PageRank were calculated and compared using the Kruskal-Wallis test. Each group needed to have at least 8671 @mentions in order to have achieved an 80% power to detect a 0.2 difference in PageRank. To mitigate any future concern about the lack of reproducibility of our results, we 1) did not perform subgroup analyses of Twitter influence by conference and 2) followed recent guidelines that make “classical hypothesis testing more congruent with evidence thresholds for Bayesian tests” []. As a result, the significance level was set at p < 0.005 [].This investigation conforms to STROBE guidelines for observational research and SAMPL guidelines for statistical reporting [,]. […]

Pipeline specifications

Software tools JMP Pro, NodeXL
Databases CodePlex
Applications Miscellaneous, Protein interaction analysis