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. 2020 May 26;12137:623–637. doi: 10.1007/978-3-030-50371-0_46

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© Springer Nature Switzerland AG 2020

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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Fig. 3. — Adoption (a) and usage dominance values for models M4–M9. Each line represents changes in proportions of hashtags produced inside adoption () and usage dominant communities () to number of all hashtags produced in the network. The results are averaged across 30 logarithmic bins and normalized by the random sampling model (M1). ground_truth represents ground truth spreading including all hashtags. The series: ground_truth_small_intra and ground_truth_high_intra show the results for hashtags with lower and higher than average, accordingly. The standard error slightly exceeds 2% only for one model. For the rest of the models, as well as for the ground truth, its value is below 1%.

Inline graphic — Adoption (a) and usage dominance values for models M4–M9. Each line represents changes in proportions of hashtags produced inside adoption () and usage dominant communities () to number of all hashtags produced in the network. The results are averaged across 30 logarithmic bins and normalized by the random sampling model (M1). ground_truth represents ground truth spreading including all hashtags. The series: ground_truth_small_intra and ground_truth_high_intra show the results for hashtags with lower and higher than average, accordingly. The standard error slightly exceeds 2% only for one model. For the rest of the models, as well as for the ground truth, its value is below 1%.