Microblogging Behavior on Sina Weibo and Twitter Table 3. Impact of the access behavior on the syntactic characteristics of microposts proportion of p 65%/3.5%3.8%/179% 20.7%/18.6%19.9%/21.3% observed on Sina Weibo. The number of hashtags is slightly less influenced by the type of activity that caused a micropost( see Table 3) Findings Given the results above, we can answer RQ3 and RQ4 as follows F3: Overall, the results show that hashtags and URLs are less frequently applied on Sina Weibo than on Twitter. This finding holds for both(i) the entire user population and (ii)individual users. In fact, we observe that a large fraction of users on Sina Weibo does not make use of hashtags which implies that hashtag-based user profiles, as discussed in 4, or topic modeling based on hashtags, as proposed by Romero et al. [6 do not seem to be appropriate on Sina Weibo. The usage statistics regarding question marks ndicate that Twitter users ask twice more questions than Sina Weibo users. F4: The usage of hashtags and URLs is moreover influenced by the access behavior. We discover that (i)users are more likely to use hashtags and URLs when they post messages via desktop applications than via mobile ap- plications. Furthermore, (ii) whenever messages are published as a byprod uct of another activity -where the primary intention of the user is rather the promotion of an activity that the user performed on another platform the probability that a micropost contains a hashtag or URL increases. A large fraction of these byproduct microposts seems to be automatically gener- ated based on the activity the user performed on another platform For user modeling those posts offer means to further contextualize the microblogging activities by following the URLs that are contained in the posts(cf. 4) 4.3 Semantic Content Analysis Results Based on the semantic enrichment provided by our user modeling framework, we analyze and compare the types of concepts and topics that people mention in their microposts on Sina Weibo and Twitter respectively. In Table 4 we compare the usage of three types of entities (location, people and organi- zation). Most of the extracted semantic concepts refer to locations(e.g. cities points of interests): 58.4% for Sina Weibo and 44.6% for Twitter. On Twitter. posts that refer to organizations(e. g. companies, institutions) are more than four times more likely to appear than on Sina Weibo. Examples of entities that were trending on Twitter include different types of entities such as"Mubarak (person), the former president of Egypt, or"Republican Party"(organization) In contrast, the most popular entities on Sina Weibo are related to locations such as“ Beijing"or“ United States Fig 3 depicts the average number of entities that can be extracted per post for the individual users in our sample. For 24.8% of the Sina Weibo users, one can detect, on average, more than one entity per post. Moreover, the fraction of users for whom no entity can be extracted is 7.9% in contrast to 10. 1% Twitter. The semantics of the users'messages posted on Sina Weibo are there- fore easier to deduce than on Twitter. Based on a comparison of a sample ofMicroblogging Behavior on Sina Weibo and Twitter 7 Table 3. Impact of the access behavior on the syntactic characteristics of microposts Syntactic characteristics proportion of posts posts that contain: Weibo Twitter Desktop/Mobile Microblog/Byproduct Desktop/Mobile Microblog/Byproduct hashtags 6.5%/3.5% 3.8%/17.9% 20.7%/18.6% 19.9%/21.3% URLs 17.8%/5.2% 5.7%/73.5% 31.6%/20.1% 25.3%/97.9% observed on Sina Weibo. The number of hashtags is slightly less influenced by the type of activity that caused a micropost (see Table 3). Findings Given the results above, we can answer RQ3 and RQ4 as follows: – F3: Overall, the results show that hashtags and URLs are less frequently applied on Sina Weibo than on Twitter. This finding holds for both (i) the entire user population and (ii) individual users. In fact, we observe that a large fraction of users on Sina Weibo does not make use of hashtags which implies that hashtag-based user profiles, as discussed in [4], or topic modeling based on hashtags, as proposed by Romero et al. [6] do not seem to be appropriate on Sina Weibo. The usage statistics regarding question marks indicate that Twitter users ask twice more questions than Sina Weibo users. – F4: The usage of hashtags and URLs is moreover influenced by the access behavior. We discover that (i) users are more likely to use hashtags and URLs when they post messages via desktop applications than via mobile ap￾plications. Furthermore, (ii) whenever messages are published as a byprod￾uct of another activity – where the primary intention of the user is rather the promotion of an activity that the user performed on another platform – the probability that a micropost contains a hashtag or URL increases. A large fraction of these byproduct microposts seems to be automatically gener￾ated based on the activity the user performed on another platform. For user modeling those posts offer means to further contextualize the microblogging activities by following the URLs that are contained in the posts (cf. [4]). 4.3 Semantic Content Analysis Results Based on the semantic enrichment provided by our user modeling framework, we analyze and compare the types of concepts and topics that people mention in their microposts on Sina Weibo and Twitter respectively. In Table 4 we compare the usage of three types of entities (location, people and organi￾zation). Most of the extracted semantic concepts refer to locations (e.g. cities, points of interests): 58.4% for Sina Weibo and 44.6% for Twitter. On Twitter, posts that refer to organizations (e.g. companies, institutions) are more than four times more likely to appear than on Sina Weibo. Examples of entities that were trending on Twitter include different types of entities such as “Mubarak” (person), the former president of Egypt, or “Republican Party” (organization). In contrast, the most popular entities on Sina Weibo are related to locations such as “Beijing” or “United States”. Fig. 3 depicts the average number of entities that can be extracted per post for the individual users in our sample. For 24.8% of the Sina Weibo users, one can detect, on average, more than one entity per post. Moreover, the fraction of users for whom no entity can be extracted is 7.9% in contrast to 10.1% on Twitter. The semantics of the users’ messages posted on Sina Weibo are there￾fore easier to deduce than on Twitter. Based on a comparison of a sample of