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We utilised the pivot tables generated from our analysis to visualise the distribution of Specificity and Transparency across different OI types and industries. We transformed these tables into heatmaps, which provided a clear and intuitive visualisation of the gaps and values in the dataset. This visualisation approach identifies areas where certain OI types were underrepresented or exhibited particularly high or low Specificity and Transparency values. The graphs are in order: number of tweets per combination of OI type and industry; the average number of interactions per combination of OI type and industry; average Specificity score; and lastly, average Transparency.
The pivot table analysis cross-tabulating the industry with the types of OI mentioned in the tweets revealed interesting distribution patterns. Among the NACE sectors, the most prevalent types of OI projects mentioned in the tweets were Open Innovation labs/incubators/accelerators and Innovation Partnerships, with a notable number of tweets discussing these project types. Conversely, all NACE sectors, except Information and Communications (J) and Scientific and technical activities (M), showed limited or no representation in the analysed tweets. 
These findings highlight the variations in the distribution of OI projects across different industry sectors, suggesting potential disparities in the level of engagement with OI practices.