Ers there are still substantial differences. To confirm the statistical significance of this finding, we have used randomization testing to estimate (one-sided) p-values2 which are shown as annotations in figures 2 and 3. Note that this does not mean that every user in the top 500 has a higher RRx-001 chemical information positive sentiment fraction (i.e. uses positive sentiment more frequently) than the average user. Figure 4 shows the distribution ofTo explain how these are produced, we shall sketch the calculation of the p-value for one of the attributes, the negative sentiment fraction as shown in figure 3. The average across all users is 0.142, whereas for the top 500 broadcasters it is only 0.119. We randomly generated 100 000 subsets of the 153 691 users and calculated the means for those subsets. From this we estimate how the mean of the attribute is distributed for randomly chosen sets of size 500. From this distribution, we calculate the p-value as the probability that a randomly selected set of 500 users would have a mean equal to 0.119 or more extreme (smaller). This probability is very close to zero (0.00022). buy PD168393 Informally, this means we can be very confident that the relationship we have found–that the top broadcasters use negative sentiment less often–has not simply happened `by chance’; the odds of that are less than 3 in 10 000.p < 0.p < 0.0.8 0.7 value of sentiment measure for outgoing edges p < 0.00001 p < 0.00001 0.6 0.5 0.4 0.3 0.2 0.1p < 0.rsos.royalsocietypublishing.org R. Soc. open sci. 3:................................................mean sentimentFigure 2. The means of the (SS) sentiment attributes for the top 500, 1000 and 5000 broadcasters (for = 0.75) compared with the mean values across all users. (The mean absolute sentiment values have been divided by 10 for easier viewing.)p < 0.00001 mean absolute sentiment/10 positive sentiment strength negative sentiment strength aggregate user sentiment measure using (SS) p = 0.00001 p < 0.00001 p < 0.00001 p = 0.00324 p = 0.00077 p = 0.top 500 broadcasters top 1000 broadcasters top 5000 broadcasters all usersp < 0.p < 0.p < 0.p < 0.p < 0.p < 0.0.50 0.45 0.40 fraction of outgoing edges 0.35 0.30 0.top 500 broadcasters top 1000 broadcasters top 5000 broadcasters all users p = 0.00011 p = 0.p = 0.0.20 0.15 0.10 0.05 0 positive sentiment fraction zero sentiment fractionnegative sentiment fractionaggregate user sentiment measure using (SS)Figure 3. The means of the (SS) sentiment fraction attributes for the top 500, 1000 and 5000 broadcasters (for = 0.75) compared with the mean values across all users.positive sentiment fraction for the top 500 broadcasters, and for all users, using (SS). The distributions overlap, of course, in particular there are a few top broadcasters with low positive sentiment fractions. Nevertheless, one can clearly see that the distributions are not the same: the distribution for the top 500 broadcasters is, in general, shifted towards the higher end of the horizontal axis, showing that on average top broadcasters use positive sentiment more often. Although we have shown the results for (SS) and = 0.75, with one exception the same pattern of results was found for all tested values of , and also using the other sentiment measures (MC) and (L) (again for six tested values of ), and the p-values were all less than 0.026. The exception was that for16 14 12 10 ( ) 8 6 4 2 0 [0, 0.05) [0.05, 0.10) [0.10, 0.15) [0.15, 0.20) [0.20, 0.25) [0.25, 0.30) [0.30, 0.35) [0.35, 0.40) [0.Ers there are still substantial differences. To confirm the statistical significance of this finding, we have used randomization testing to estimate (one-sided) p-values2 which are shown as annotations in figures 2 and 3. Note that this does not mean that every user in the top 500 has a higher positive sentiment fraction (i.e. uses positive sentiment more frequently) than the average user. Figure 4 shows the distribution ofTo explain how these are produced, we shall sketch the calculation of the p-value for one of the attributes, the negative sentiment fraction as shown in figure 3. The average across all users is 0.142, whereas for the top 500 broadcasters it is only 0.119. We randomly generated 100 000 subsets of the 153 691 users and calculated the means for those subsets. From this we estimate how the mean of the attribute is distributed for randomly chosen sets of size 500. From this distribution, we calculate the p-value as the probability that a randomly selected set of 500 users would have a mean equal to 0.119 or more extreme (smaller). This probability is very close to zero (0.00022). Informally, this means we can be very confident that the relationship we have found--that the top broadcasters use negative sentiment less often--has not simply happened `by chance'; the odds of that are less than 3 in 10 000.p < 0.p < 0.0.8 0.7 value of sentiment measure for outgoing edges p < 0.00001 p < 0.00001 0.6 0.5 0.4 0.3 0.2 0.1p < 0.rsos.royalsocietypublishing.org R. Soc. open sci. 3:................................................mean sentimentFigure 2. The means of the (SS) sentiment attributes for the top 500, 1000 and 5000 broadcasters (for = 0.75) compared with the mean values across all users. (The mean absolute sentiment values have been divided by 10 for easier viewing.)p < 0.00001 mean absolute sentiment/10 positive sentiment strength negative sentiment strength aggregate user sentiment measure using (SS) p = 0.00001 p < 0.00001 p < 0.00001 p = 0.00324 p = 0.00077 p = 0.top 500 broadcasters top 1000 broadcasters top 5000 broadcasters all usersp < 0.p < 0.p < 0.p < 0.p < 0.p < 0.0.50 0.45 0.40 fraction of outgoing edges 0.35 0.30 0.top 500 broadcasters top 1000 broadcasters top 5000 broadcasters all users p = 0.00011 p = 0.p = 0.0.20 0.15 0.10 0.05 0 positive sentiment fraction zero sentiment fractionnegative sentiment fractionaggregate user sentiment measure using (SS)Figure 3. The means of the (SS) sentiment fraction attributes for the top 500, 1000 and 5000 broadcasters (for = 0.75) compared with the mean values across all users.positive sentiment fraction for the top 500 broadcasters, and for all users, using (SS). The distributions overlap, of course, in particular there are a few top broadcasters with low positive sentiment fractions. Nevertheless, one can clearly see that the distributions are not the same: the distribution for the top 500 broadcasters is, in general, shifted towards the higher end of the horizontal axis, showing that on average top broadcasters use positive sentiment more often. Although we have shown the results for (SS) and = 0.75, with one exception the same pattern of results was found for all tested values of , and also using the other sentiment measures (MC) and (L) (again for six tested values of ), and the p-values were all less than 0.026. The exception was that for16 14 12 10 ( ) 8 6 4 2 0 [0, 0.05) [0.05, 0.10) [0.10, 0.15) [0.15, 0.20) [0.20, 0.25) [0.25, 0.30) [0.30, 0.35) [0.35, 0.40) [0.
