Qi Gao, Fabian Abel, Geert-Jan Houben, Yong Yu posts per weekend day/ posts per week Overall enton certain tupes of per day during the atio between weekend posts and o weekday posts the average number of posts per day on a weekend divided by the users average number of posts per weekday Fig. 5. Weekend-weekday ratio per user In Fig. 5 we plot the weekend-weekday ratio for the individual users. W the overall amount of microblogging activities per day on Sina Weibo is higher on the weekends than during the day, we also discover that 1. 2% of the Sina Weibo users perform microblogging activities solely during the weekend(ratio of weekend posts is infinite). For about 50% of the users on Sina Weibo the weekend-weekday ratio is greater than l which means that they publish more frequently during the weekend. In contrast, on Twitter we identify only 28% of the users who publish more tweets per day on a weekend than during a weekday As depicted in Table 6, the occurrence of organizations and persons is more likely during the weekend than during the week on Sina Weibo whereas locations appear more likely during a weekday. On Twitter, the opposite characteristics can be observed. For example, Twitter users mention locations more frequently during the weekend than during the week. These differences in mentioning en- tities during weekends/weekdays on Sina Weibo and Twitter respectively may elate to different life styles that Chinese and Western people follow. Investigat ing the particular reasons for them can be interesting for future work. Furthermore, we study how individual user interests change over time by calculating the standard deviation of the timestamps of microposts that mention a certain topic (entity). The higher the standard deviation of a certain topic the longer the time period over which the topic is mentioned in the posts In Fig. 6 we plot for each user the average standard deviation of the topics which a user mentioned at least once, and group the average standard deviations by the type of the topics. Overall, we observe that topics on Sina Weibo seem to fluctuate stronger than on Twitter. Sina Weibo users often mention certain concepts only once. For example, for more than 80% of the Sina Weibo users of our sample, the standard deviation of the organization-related topics is 0. These users mention only once in their posts. On both platforms the location- related concepts are, on average, mentioned over a longer period of time than organization-related and person-related concept Findings The main findings from the analysis of the temporal behavior (re- search questions RQ9 and RQ10) can be summarized as follows F9: On both platforms, the users posting behavior during weekdays differs the one during weekend: while users on Sina Weibo are more active on the weekends, Twitter users tend to be more active during weekdays. Moreover user interests change between weekends and weekdays. Again, this change of interests differs between Sina Weibo and Twitter users: while for Sina10 Qi Gao, Fabian Abel, Geert-Jan Houben, Yong Yu posts per weekend day / posts per weekday Weibo Twitter Overall posts 1.19 0.89 posts that mention certain types of entities: Location 0.81 1.05 Organization 1.50 0.91 Person 1.19 0.97 Table 6. Ratio between weekend posts and weekday posts = the average number of posts per day on a weekend divided by the average number of posts per weekday 0% 20% 40% 60% 80% 100% users 0 0.1 1 10 100 ratio of weekend posts Weibo Twitter users publish more posts per day on a weekend users publish more posts per day during the week Fig. 5. Weekend-weekday ratio per user In Fig. 5 we plot the weekend-weekday ratio for the individual users. While the overall amount of microblogging activities per day on Sina Weibo is higher on the weekends than during the day, we also discover that 1.2% of the Sina Weibo users perform microblogging activities solely during the weekend (ratio of weekend posts is infinite). For about 50% of the users on Sina Weibo the weekend-weekday ratio is greater than 1 which means that they publish more frequently during the weekend. In contrast, on Twitter we identify only 28% of the users who publish more tweets per day on a weekend than during a weekday. As depicted in Table 6, the occurrence of organizations and persons is more likely during the weekend than during the week on Sina Weibo whereas locations appear more likely during a weekday. On Twitter, the opposite characteristics can be observed. For example, Twitter users mention locations more frequently during the weekend than during the week. These differences in mentioning en￾tities during weekends/weekdays on Sina Weibo and Twitter respectively may relate to different life styles that Chinese and Western people follow. Investigat￾ing the particular reasons for them can be interesting for future work. Furthermore, we study how individual user interests change over time by calculating the standard deviation of the timestamps of microposts that mention a certain topic (entity). The higher the standard deviation of a certain topic the longer the time period over which the topic is mentioned in the posts. In Fig.6 we plot for each user the average standard deviation of the topics which a user mentioned at least once, and group the average standard deviations by the type of the topics. Overall, we observe that topics on Sina Weibo seem to fluctuate stronger than on Twitter. Sina Weibo users often mention certain concepts only once. For example, for more than 80% of the Sina Weibo users of our sample, the standard deviation of the organization-related topics is 0. These users mention thus organizations only once in their posts. On both platforms the location￾related concepts are, on average, mentioned over a longer period of time than organization-related and person-related concepts. Findings The main findings from the analysis of the temporal behavior (re￾search questions RQ9 and RQ10 ) can be summarized as follows: – F9: On both platforms, the users posting behavior during weekdays differs the one during weekend: while users on Sina Weibo are more active on the weekends, Twitter users tend to be more active during weekdays. Moreover, user interests change between weekends and weekdays. Again, this change of interests differs between Sina Weibo and Twitter users: while for Sina