Ho are active in the community nearly every day they are

Ho are active in the community nearly every day they are active on Twitter. Once we had selected the communities of interest, we collected a more detailed tweet history for each participating user, as described in ?.rsos.royalsocietypublishing.org R. Soc. open sci. 3:…………………………………………4.2. Analysing the endurance of the communitiesWe analysed how well our communities endured over time. We examined a 28-day LM22A-4 solubility period starting on 22 September 2014 (which we will call the `autumn period’) and a 28-day period starting on 2 February 2015 (which we will call the `spring period’), and compared how many users in each community were active (mentioned or were mentioned by other users) within the community. Would the same users still be tweeting each other in the spring, or would the communities have dissolved over time? Figure 7 shows a log og plot of the results. We see that the communities persisted well from autumn to spring. In three of them, communities 14 (human resources), 17 (friends chatting) and 18 (friends chatting), all the original users were still active in the community. These are three out of the four smallest communities. The other 15 communities lost RRx-001 site between 6.5 (for community 16, nursing) and 39.3 (for community 7, Islam) of their users, with an average loss of 18.6 . We can see differences in the communities produced by the three algorithms here: the six produced by k-clique-communities lost an average of 3.8 of their users, compared to 16.4 for the Louvain method and 26.3 for weighted Louvain. Let us say user loss factor to mean the number of users active in the 28-day autumn period divided by the number active in the later 28-day spring period. When the user loss factor is 1, then the community has retained all its users; the higher the value, the more users the community has lost. We looked to see whether the conductance, sentiment or size of communities is related to their endurance. In figure 8, one can see that conductance is a predictor of what proportion of users will stop participating in the community, with correlation coefficient 0.42. When conductance is lower (so that the community is more densely connected internally and better separated from the rest of the network) then fewer users stopped participating on average. Similarly, the community sentiment is a predictor of community endurance, as shown in figure 9: the more positive the initial sentiment (measured in the autumn period), the fewer users stopped participating on average. For (SS) (as shown in figure 9) the correlation coefficient is -0.60; for (MC) it is -0.48 and for (L) it is -0.58. On the other hand, community size was not correlated to user loss factor; the correlation coefficient was 0.07. We noted in ?.2 that the correlations between the three sentiment measures (MC), (SS) and (L) at the individual tweet level were only moderate. The following shows the correlations between the community sentiments produced by the three measures, in the autumn and spring periods:Table 1. Selected summary statistics for the 18 communities we selected, in size order. The community marked as not connected had eight nodes separated from the rest…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….Ho are active in the community nearly every day they are active on Twitter. Once we had selected the communities of interest, we collected a more detailed tweet history for each participating user, as described in ?.rsos.royalsocietypublishing.org R. Soc. open sci. 3:…………………………………………4.2. Analysing the endurance of the communitiesWe analysed how well our communities endured over time. We examined a 28-day period starting on 22 September 2014 (which we will call the `autumn period’) and a 28-day period starting on 2 February 2015 (which we will call the `spring period’), and compared how many users in each community were active (mentioned or were mentioned by other users) within the community. Would the same users still be tweeting each other in the spring, or would the communities have dissolved over time? Figure 7 shows a log og plot of the results. We see that the communities persisted well from autumn to spring. In three of them, communities 14 (human resources), 17 (friends chatting) and 18 (friends chatting), all the original users were still active in the community. These are three out of the four smallest communities. The other 15 communities lost between 6.5 (for community 16, nursing) and 39.3 (for community 7, Islam) of their users, with an average loss of 18.6 . We can see differences in the communities produced by the three algorithms here: the six produced by k-clique-communities lost an average of 3.8 of their users, compared to 16.4 for the Louvain method and 26.3 for weighted Louvain. Let us say user loss factor to mean the number of users active in the 28-day autumn period divided by the number active in the later 28-day spring period. When the user loss factor is 1, then the community has retained all its users; the higher the value, the more users the community has lost. We looked to see whether the conductance, sentiment or size of communities is related to their endurance. In figure 8, one can see that conductance is a predictor of what proportion of users will stop participating in the community, with correlation coefficient 0.42. When conductance is lower (so that the community is more densely connected internally and better separated from the rest of the network) then fewer users stopped participating on average. Similarly, the community sentiment is a predictor of community endurance, as shown in figure 9: the more positive the initial sentiment (measured in the autumn period), the fewer users stopped participating on average. For (SS) (as shown in figure 9) the correlation coefficient is -0.60; for (MC) it is -0.48 and for (L) it is -0.58. On the other hand, community size was not correlated to user loss factor; the correlation coefficient was 0.07. We noted in ?.2 that the correlations between the three sentiment measures (MC), (SS) and (L) at the individual tweet level were only moderate. The following shows the correlations between the community sentiments produced by the three measures, in the autumn and spring periods:Table 1. Selected summary statistics for the 18 communities we selected, in size order. The community marked as not connected had eight nodes separated from the rest…………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

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