Research on Clustering Method for Government Micro-blogging User Segments Based on User Interaction Behavior Suozhu Wang1, a, Jun Wang2, b

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1 6th Internatonal Conference on Measurement, Instrumentaton and Automaton (ICMIA 017) Research on Clusterng Method for Government Mcro-bloggng User Segments Based on User Interacton Behavor Suozhu Wang1, a, Jun Wang, b 1, School of Management, Captal Normal Unversty, Bejng, , Chna a wsz_wsz@16.com, b wangjuncnu@16.com Keywords: Government mcro-bloggng, Interacton behavor, User segments clusterng Abstract. One purpose of government mcro-bloggng s to provde servces for the publc. Therefore, t s of great sgnfcance to study the users characterstcs and clusterng user group for provdng personalzaton servces and mcro-bloggng's operaton and management. In ths paper, a measurng nteracton strength method of user and mcro-bloggng s presented by mean of user behavor features such as retweet, comment and so on n the same government mcro-bloggng, and a fuzzy clusterng method for user segments s put forwarded based on user nteracton behavor strength. An example demonstrates effectveness and feasblty of the method. Introducton Government mcro-bloggng refers to mcro-bloggng that t s opened by the party and government organzatons or party and government offcals through real name authentcaton. Its purpose s to release government nformaton, promote the dsclosure of government nformaton, strengthen the communcaton between offcals and the people, shape new government, and strengthen publc servces. Wth the rapd development of government mcro-bloggng, t has become a sgnfcant way for government provdng publc servces, and a new access to socal dynamcs and government affar nformaton for the publc. Government mcro-bloggng, one of the functonal mcro-bloggng, s user servces orented. Government mcro-bloggng s startng pont and destnaton s servng for the publc who wll beneft a lot from such servce. Users have dfferentated nterests and behavoral characterstcs n the same government mcro-bloggng, whch can be reflected by the user's retweet, comment and other nteractve behavor on mcro-bloggng [1]. The user's generc pattern can be dscovered by dvdng the user groups wth smlar behavor preferences. Clusterng results can provde decson support for offerng personalzaton servces to the publc as well as mcro-blog's operaton and management. There s no term for government mcro-bloggng n abroad, a large number of researches focus on the ssues, such as government offcers usng mcro-blog, ts nfluence on government management, etc., based on the study about Twtter, Facebook and other socal meda. For example, Ho Young Yoon, etc. [] hold that followng and menton on mcro-bloggng are man approaches for poltcans lnkng each other n order to ganng more poltcal support. Alhabash, etc. [] hold that marketers use ndex, such as nformaton dssemnaton, sentment assessments, nformaton revews and etc., to study people's behavor on socal network webstes. John Carlo Bertot, etc.[4] consder that the collaboraton, partcpaton, autonomy and tmelness of the government mcro-bloggng wll help to enhance governmental openness, transparency and ant-corrupton capablty. In recent years, study about government mcro-bloggng has obtaned a great quantty of achevements at home. J. Chen, etc.[5] summarzed research results at home and abroad recent years, clam that domestc and foregn researches manly concentrate on the ssues, such as government mcro-bloggng concept, applcaton, propagaton, etc., whle quanttatve study on a large number of objectve data produced by government mcro-blog actvtes s not qute enough. By far, research fndngs n government mcro-bloggng user behavor characterstcs analyss and clusterng have not been seen yet. Therefore, t s worth dscoverng that how to use the data produced by the government mcro-bloggng actvty and adopt data mnng methods to study related ssues about government mcro-blog. Above all, ths paper wll propose a user nteracton behavor characterstcs measure method based on the data generated by user nteracton wth government mcro-blog, and user group fuzzy Copyrght 017, the Authors. Publshed by Atlants Press. Ths s an open access artcle under the CC BY-NC lcense ( 0

2 clusterng method depend on user nteracton behavor accordng to the method s put forwarded n ths paper. Specfc steps and clusterng process are llustrated by experment. The research result s amed at provdng a new dea and method for government mcro-bloggng operator and manager, when they analyze user groups. User Interacton Behavor and Strength User nteracton behavor Spreadng accurate nformaton ssued by the government as fast as possble s one of the ntentons of government mcro-bloggng operatng. The followers of government mcro-bloggng wll fnd out what they are nterested n and retweet and comment smultaneously, whch make offcal nformaton shared and dstrbuted, when they are browsng and readng those mcro-blogs. Through the drect analyss on government mcro-bloggng, nteracton behavors between users and government mcro-blog manly ncludes: follow, retweet, comment, menton (@) and etc. All of these nter-behavors are essental for nformaton dssemnaton. Therefore, user nteracton behavor can be defned as follows. Defnton 1 Interacton behavor. Let U be one user of the government mcro-bloggng, GW stands for one of the government mcro-blogs. An nteracton behavor from a user to government mcro-blog can be expressed as U GW, when one of followng actvty occurs (1) U comment on a mcro-blog posted by GW. () U retweet a mcro-blog posted by GW. () U post a mcro-blog mentoned (@) GW. Through the defnton above, a varety of nteracton behavors can take place between U and GW, and the number of nteracton behavors shows user s actvty on mcro-blog, the more numbers of nteracton behavors the user more actvty. Obvously, the nteracton strength wll go to dffer due to dfferent users and GW on the same government mcro-bloggng. To measure a user s nteracton strength, ths paper ntends to present user nteracton strength by defnng user actvty vector. Actvty means the users nfluence on a mcro-bloggng. The more user actvty s, the more lkely to engage n the spread of hot topcs and attract other users attenton. User nteracton strength Suppose that GW s a government mcro-bloggng, let W = {w1, w,..., w j,..., wn } be a set of all GW ssued mcro-blogs wthn a perod tme, U = {u1, u,..., w,..., um } be a set of all users. Defnton Retweet actvty degree. Let Wu (Wu W ) be the set of mcro-blogs retweeted by the user u wthn a perod tme, retweet actvty degree s defned as n (1) ( = 1,,..., m ) F Au = ft j / (( ft t ) / n ) w j W u t =1 Where ftt denotes the number of mcro-blogs wt retweeted, n denote the total number of mcro-blogs. Retweet actvty degree ndcates the rato of the number of mcro-blog retweeted by a certan user to the average number of all retweeted mcro-blogs, whch descrbes the actve degree of a user retweet behavor on a government mcro-blog, the bgger the value, the user s more actve. Defnton Comment actvty degree. In a certan perod of tme, let Wu (Wu W ) represents a set of mcro-blogs commented by the user u, then comment actvty degree s defned as n () ( = 1,,..., m ) C Au = ct j / (( ct t ) / n ) w j W u t =1 Where ctt denotes the number of the mcro-blogs t commented, n denotes the total number of mcro-blogs. Comment actvty degree ndcates the rato of the number of mcro-blogs commented by a certan user to the average number of all commented mcro-blogs, whch descrbes the actve degree of a user comment behavor on a government mcro-bloggng, the bgger the value, the user s more actve. 1

3 Defnton 4 Menton (@) actvty degree. In a certan perod of tme, let nu represents the number of GW mentoned (@) by the user u, then menton (@) actvty degree s defned as () ( = 1,,..., m ) Au = n u / ( N / m ) where N denotes the number of GW mentoned (@) by all users, m denotes the total number of users. Menton(@) actvty degree ndcates the rato of the number of mcro-blogs mentoned(@) by a certan user to the average number of mentoned(@) by all users, whch descrbes the actve degree of a user menton(@) behavor on a government mcro-bloggng, the bgger the value, the user s more actve. Defnton 5 Interacton behavor strength. In a certan perod of tme, nteracton behavor strength between user u and GW s represented by a vector IBu GW = ( FAu, CAu, Au ), the norm of vector s IBu GW, whch represents the nteracton strength between u and GW, where FAu, CAu, Au calculated by Eq.(1), Eq.() and Eq.() separately. Interacton strength vector denotes the overall behavors of a certan user n retweet, comment, and menton (@). The bgger the norm of vector, the greater the nteracton strength between user and government mcro-bloggng. The user nteracton behavor strength matrx can be gven by usng the vector as the row of matrx. Defnton 6 Interacton behavor strength matrx. After data standardzaton processng for FAu, CAu, Au, UIB = [ FAu, CAu, Au ]m (=1,,, m) s sad to be user nteracton behavor strength matrx. User group clusterng method Clusterng method Clusterng s that a group of physcal or abstract objects are dvded nto several classes by the smlarty degree between them, and smlar objects consttute one class [6]. A class s a set conssted of objects that are smlar to each other, whle objects are heterogeneous among dfferent classes. Smlarty degree can be measured by object attrbute data. There are many commonly clusterng algorthms n feld of data mnng. They are partton method, herarchcal clusterng method, method based on densty, and so on. However, these methods have some dsadvantages, especally n dealng wth large-scale, hgh dmenson, fuzzy, dynamc data. In practce, the words (hgh, low, general, etc.) that descrbe the degree of user actvty are ambguous. Therefore, n ths paper, cluster user groups wll use fuzzy clusterng method. Fuzzy clusterng analyss s generally refers to construct fuzzy matrx based on the propertes of the research subject, and on ths bass, accordng to the degree of membershp to determne the cluster relatonshp. Fuzzy mathematcs method was used to determne the fuzzy quanttatve relaton between samples, objectvely and accurately for cluster Assume that W = {wd1, wd,..., wd j,..., wd n } ( wd s mcro-blog s ID) be a set of a government mcro-bloggng ssued all mcro-blogs, U = {u1, u,..., w,..., um } be all users of GW, and represents objects to be classfed. Specfc steps of clusterng analyss method are as follows: Step 1 On a government mcro-bloggng, collect users nteracton behavor data, ncludng retweet, comment, menton (@), by usng scrapng tool, and the data s processed by statstcs and preprocessng to form user nteracton behavor data table. Step Calculate the strength vector of each user's nteracton behavor by Eq.(1), Eq.() and Eq.(), accordng to user nteracton behavor data table, and then standardze these data to form the user nteracton behavor strength matrx UIB defned n defnton 6. Step Calculate the smlarty Sj between u and uj by usng Eq.(4) and Max-mn smlarty measurement based on the user nteracton behavor strength matrx UIB, and form fuzzy smlar

4 matrx R F = [ Sj ]m m : Sj = ( FAu FAu j ) + (CAu CAu j ) + ( Au Au j ) ( FAu FAu j ) + (CAu CAu j ) + ( Au Au j ) (, j = 1,,..., m) (4) Step 4 If fuzzy smlar matrx RF s fuzzy classfcaton relaton, then cluster analyss drectly, otherwse, go to the next step. Step 5 Transform from fuzzy smlar matrx RF to fuzzy classfcaton relaton, by the means of fuzzy smlar matrx squares matrx-self crcularly, R o R = R, R o R = R 4,... untl Rk = R, then R k s fuzzy classfcaton relaton. and adopt cut-matrx to fuzzy cluster analyss accordng to fuzzy Step 6 Select k classfcaton relaton R, If the bgger the, the more accurate the classfcaton, otherwse the more naccurate the classfcaton. Step 7 Output clusterng pattern. Example and analyss To testfy the feasblty of the clusterng method dscoursed above, take a local government mcro-bloggng as an example to demonstrate the specfc clusterng analyss process. The government mcro-bloggng ssued 100 mcro-blogs n a perod of tme, 7 users behavor data collected by scrapng tool has been preprocessed shown n table 1. Table 1 User nteracton behavor data User Retweet Mcro-blog Retweeted Mcro-blog Commented Id U1 {wd1, wd5,wd10} {wd1, wd5 } U 5 {wd1,wd,wd5,wd7,wd15} {wd1, wd,wd6 } {wd1 } U 6 1 {wd1,wd,wd5,wd9,wd10 wd16} U4 1 {wd16,wd100 } {wd4, wd6 } U5 4 {wd,wd4,wd0,wd6 } {wd1, wd19 } U6 1 0 {wd5,wd8 } {wd } U7 4 4 {wd17,wd5,wd0 } {wd17,wd4,wd5,wd6 } k Accordng to Table 1 user nteracton behavor data, calculate the user nteracton behavor strength matrx by step gven above, results as follows: Accordng to the user nteracton behavor strength matrx UIB, calculate user fuzzy smlar matrx by step gven above, results as follows: As a result of RF s not fuzzy classfcaton relaton, t s necessary to squares matrx-self

5 crcularly by step 5 gven above, the fuzzy classfcaton relaton as follows Accordng to the fuzzy classfcaton relaton RK, select dfferent confdence level and cluster analyze. When 0.91 < λ 1, then users are dvded nto 7 classes: {U1}, {U }, {U }, {U 4 }, {U 5 }, {U 6 }, {U 7 }. When < λ 0.91, then users are dvded nto 6 classes: {U }, {U1,U 5 }, {U }, {U 4 }, {U 6 }. When < λ 0.714, then users are dvded nto 5 classes: {U1, U, U 5 }, {U }, {U 4 }, {U 6 }, {U 7 }. When 0.7 < λ 0.704, then users are dvded nto 5 classes: {U1,U,U }, {U 4 }, {U 5 }, {U 6 }, {U 7 }. When < λ 0.7, then users are dvded nto classes: {U1,U,U,U 5,U 7 }, {U 4 }, {U 6 }. When 0.58 < λ 0.619, then users are dvded nto classes: {U1,U,U,U 4,U 5, U 7 }, {U 6 }. When 0 < λ 0.58, then users are dvded nto 1 classes: {U1,U,U,U 4,U 5, U 6,U 7 }. Accordng to the results above, users can be clustered n dfferent levels by government mcro-bloggng management and operaton offcers based on the actual demands. Depend on the clusterng result, calculate the average nteracton behavor strength of each user group based on the matrx UIB, then the most actve user groups can be found by rankng. In the lght of Table 1, after dscoverng ntersecton of all users retweeted or commented mcro-blogs, all users retweeted or jontly commented mcro-blogs are calculable. Furthermore, hot topc, the publc concerned, can be dscovered. Conclusons and Future Work It s an urgent problem for the applcatons of government mcro-bloggng how to develop the publc behavor patterns accurately, whch fertlzes to optmze government mcro-bloggng and mproves government servces. Clusterng government mcro-blog users wll help the government mcro-bloggng operaton and management offcers to offer more personalzed nformaton servces to the publc, and provde gudance for the publc servces decsons of government agences. Based on the nteracton behavor data between users and government mcro-bloggng, ths paper put forward the user nteracton behavor strength measurement model and the clusterng method of user subdvson. The method of ths paper can provde a new dea for the analyss of government mcro-bloggng user groups. In ths paper, only take ndexes, ncludng retweet, comment and menton(@), nto account, whle lke, collecton and other behavor factors, whch reflects users preference and attenton degree on government mcro-bloggng as well, are not consdered. So our future work wll add more factors to the measurement of user nteracton behavor to calculate user Interacton behavor strength more accurately. Wth the development of the data mnng technology and other network nformaton mnng technologes, ths wll provde a lot of valuable tools for government mcro-bloggng managers. And we wll develop more effcent clusterng algorthms related to government mcro-bloggng users. 4

6 Acknowledgements Ths research was supported by Natonal Natural Scence Foundaton of Chna (grant No ) and by the Scence and Technology Plan of the Bejng Educaton Commttee (grant No. KM ). References [1] N. Zhang, H. Huang, M. Duarte, J. Zhang, Rsk analyss for rumor propagaton n metropolses based on mproved 8-state ICSAR model and dynamc personal actvty trajectores. Physca A: Statstcal Mechancs and ts Applcatons, Vol. 451(016), p [] Red, Chrstoph, K.bler, Felx, Goswam, Suparna, Krcmar, Helmut. Tweetng to Feel Connected: A Model for Socal Connectedness n Onlne Socal Network. Internatonal Journal of Human Computer Interacton, Vol (01), p [] Ho Young Yoon, Han Woo Park. Strateges affectng Twtter based networkng pattern of South Korean poltcans: socal network analyss and exponental random graph model. Qualty Quantty, Vol (014), p [4] John Carlo Bertot, Paul T. Jaeger, Derek Hansen. The mpact of polces on government socal meda usage: Issues, challenges, and recommendatons. Government Informaton Quarterly, Vol. 9 (01), p [5] Jng Chen, Qnjan Yuan. A Revew of Admnstratve Mcroblog Study n Chna and Abroad. Informaton Scence, Vol. -6 (014), p [6] Zhangcheng Qu, Hong Shen, User clusterng n a dynamc socal network topc model for short text streams. Informaton Scences, Vol. 414(017), p