What is the Nature of Tencent Weibo: Detect the Unique Features of Tencent Users Daifeng Li Jingwei Zhang Golden Guo-zheng Sun Dept. of Computer Science Depart. of Electronic Tencent Company and Technology Engineering Beijing, China Tsinghua University Tsinghua University [email protected] Beijing, China Beijing, China daifl[email protected] [email protected] Jie Tang +ing Ding Zhipeng Luo Dept. of Computer Science School of Library and Dept. of Computer Science and Technology Information Science and Technology Tsinghua University Indiana University Tsinghua University Beijing, China Bloomington Beijing, China [email protected] IN, USA [email protected] [email protected] ABSTRACT Categories and Subject Descriptors Tencent Weibo has become the world largest Micro-Bloggings 1 + 2 3Database and Management45 Data Mining; 6 - website within less than three years, which contains 370 mil- 3Computer Applications45 Social and Beha$ioral Science lion users, who contribute 30-60 million micro-blogs(we call weibo) for each day Different with Twitters, Tencent pro- vides users more #ermissions to share di"erent %inds of in- General Terms formation, such as more relaxed limitation of text length, Human 0actors, Measurment pictures, videos and etc, which could hel# users to build their #ersonalized micro media center. The main purpose of this paper is to take deep insight into Tencent Weibo and Keywords study its uni(ue features, which are di"erent from other fa- Tencent, Social 8etworks, Information Di"usion, Topic Trend, mous Micro-Bloggings weibsite We ha$e collected the entire User Beha$iors, ;aralleled ;ageRank Tencent Weibo from 10th, Oct, 201) to 1st, Dec, 2012 and obtained 37.1 million user profiles, -) + billion users’ beha$- iors. We ha$e made analysis for Tencent users. different 1. INTRODUCTIO behaviors from both macro and micro level, and make com- Tencent Weibo, the biggest Micro-Bloggings services web- parison with other social networks such as Twitter, Delicious site not only in =hina, but also in the world, is consid- and et al. We find that from macro level, Tencent Weibo is ered as the important #owers to change #eople’s traditional more inclined to be a platform of social media, rather than communication styles in China. > more complete function a communication network/ compared with Twitter, Tencent system in Tencent Weibo could help online users to build Weibo has more complex network and more active users their own #ersonalized medias for both obtaining and dif- 0rom micro le$el, Tencent users are more interested in cre- fusing information; the behaviors of Tencent users result in ating and sharing original messages/ compared with Twitter, a highly information sensiti$e system, which means that an Tencent users are more li%ely to discuss on a certain number attractive information will spread rapidly and co$er a range of old topics, they ha$e a lower reciprocity rate, a stronger of millions level of nodes as the formal information is an- connection with smaller number of their followers/ besides, nounced even before. Similar with Twitter, the behaviors of there e&ists correlation for users. different behaviors from following in Tencent WeiBo means to recei$e all information statistical level/ at last, we find that users’ behaviors ha$e from whom the user follows; and the following and followed a #ositi$e correlation with the number of their followers in behaviors require no reciprocation. The basic function of a limited extent, and the relation will in$alid when it ex- Tencent Weibo is to #ost and repost messages within 140 ceeds that extent To the best of our %nowledge, this work Chinese characters. Some symbols are also the same with is the first (uantitati$e study on the entire Tencentsphere Twitter, for e&ample, ?@means to address the user, whose 9! and information di"usion on it. is behind it; the phrases between two ?A@ means a “Topic”, which is similar with the definition of “hastag” in Twitter. Besides, Tencent Weibo also supports other kinds of com- munication functions, such as ?=omments@, which provides specific space for users to discuss on a certain topic; “Pri- $acy letters@, which could pro$ide pri$acy communications Copyright is held by the author/owner(s). between two users. >ll the assignments abo$e could help ACM X-XXXXX-XX-X/XX/XX. users to participant in group discussions in a more easier way and could better express their opinions, and spread in- Hopcroft et. al studied the features of “Reciprocal” in Twit- teresting information more fle&ible. ter and made prediction on users’ behaviors 3D4/ ;eng et. The researches of large-scaled data analysis has been widely al studied the features of ?<etweet” in Twitter and applied studied and applied in many famous Micro-blogs services =<0 to model ?<etweeting@ beha$iors [16]/ Zhao et al website, such as Twitter, 0acebook and etc. Those researches studied the #otential reasons for users’ beha$iors in Twit- take dee# insight into to#ological features and dynamic trends ter 3+0]/ Banerjee et. al studied users’ on line behaviors of evolution, their research results are the important comple- and detected their interests from Twitter 3)4 3+4/ 0ujisaka ment to current social network theory, but seldom consider learned users’ behaviors patterns from Twitter by combining the influence of users. instinct attributes of users, such as re- geo-tags 324/ Krishnamurthy, et. al used follower-followee re- gions, countries, races, cultures and etc. In our researches, lationship to study users. characteristics [13]. Some other we mainly focuse on Tencent Weibo, the most famous =hi- researches re$eal the capability of social sensor and predic- nese Micro-Bloggings services system; Tencent Weibo owns tion #owers of Twitter, such as 33], 310], 3)24 and 3?4 more than 37 millions of =hinese users, there is no previous There also exist se$eral researches to systematically study researches on studying a social network, which is consisted Twitter, Kwak et. al applied general statistical methods to of such huge amount of users from the same country, so what analyze features of Twitter from both social network and they are talking about every day, how they construct their social media angles, and compare the results with other so- on-line structures, how they share and spread information, cial networks, such as Flickr and Houbute 3)-4/ similar with is the essential things, which we mostly want to know/ thus, Kwa%.s work, our research also marks the first look on entire the main purpose of this paper is to analyze the statistical micro-blogs of Tencent, which is meaningful for the current features of Tencent Weibo from several as#ects of weibo in- researches of social networks, because few researches ha$e creasing patterns, users’ beha$iors patterns, users. relation- been done to study s#ecific Micro-Blogs with di"erent cul- ships and information diffusion, which could hel# us better tural, economics and #olitical background. Tencent Weibo understand the uni(ue features of Tencent Weibo. > set as one of the biggest Micro-Blogs website, contains more of experiments are designed to make a systematic research than 370 million =hinese users, the researches of those users. from both macro and micro le$els/ from the macro le$el, we behaviors form the angle of entire Tencent Weibo should be would like to take insight into whether there exists grou# be- an important complementarity for existing researches. In havior patterns, how about the influences of those patterns order to gain a better observation result, > systematic at- to determine the spread of different information/ statistical tempt at characterizing uni(ue features of Tencent Weibo analysis of the increase of users. di"erent %inds of beha$iors is designed, the ex#eriment takes deep insight into users’ along with intrinsic time 0rom the micro level, we would online behaviors, information propagation, dynamic content li%e to observe whether there exists behaviors patterns for analysis from both macro and micro level. users, how users communicate with each other, what factors will influence users. behaviors. We also design a paralleled 3. DATA COLLECTION page-rank algorithm to rank the importances of all users and The crawler algorithm is designed mainly based on users. compare the results with the rank based on number of fol- all beha$iors in Tencent Weibo, which crawls each of users. lowers and number of reposts. >t last, a topic trend model activities along the time We build a mathematical model to is introduced to observe what topics are users tal%ing about describe all the crawled behaviors as5AI uname, parent, every day, the e$olution of those topics, how to classify those root, time, location, action tname, type,{ content , where topics, and the features of all participants uname means the 9! of user,{ we assume his 9! isX}}, parent This paper is organized as follow5 Section + introduces represents the ?u##er-level beha$iors@ ofX.s current behav- the related work; Section 3 describes the data set, which is ior, for example,X follows or retweetsY ’s message, soY ’s crawled from Tencent Weibo; Section - discusses the results behaviors of creating that message could be seen as parent of of users. behaviors from macro level/ Section C discusses the X’s current beha$ior; root is other user’s original behaviors, results of users. behaviors from micro level; Section 6 dis- which indirectly causeX’s current beha$ior, for example, cusses the results from paralleled page rank algorithm/ in userZ deli$ers a original message, userY forwards his mes- Section 7, we study how topics evolved along the time; Sec- sage, userX then forwards the same message fromY , then tion 2 is the conclusion.