Prediction algorithm for users Retweet Times

Transcription

1 , pp Predcton algorthm for users Retweet Tmes Hahao Yu, Xu Feng Ba,ChengZhe Huang, Haolang Q Helongang Insttute of Technology, Harbn, Chna Abstract. In vew of the fact that the propagaton path topology cannot effectvely deal wth complex socal network conssts of hundreds of mllons of users. More researchers choose to use machne learnng methods to complete retweet predcton. Those use the classfcaton method to udge whether a message wll be retweeted or not. Ths paper argues that retweet predcton should be regresson analyss problem, not ust the classfcaton problem. Through collectng user characterstcs on Twtter and selectng some features whch have an mportant mpact on the retweet behavor, a Predcton algorthm Based on the Logstc Regresson for users Retweet Tmes n socal network was proposed. Experment results based on the actual data set show the regresson analyss predctng model has a good predctng accuracy n dealng wth retweet predctng, the proposed method s effectveness. Keywords: Socal network, Retweet Tmes, tweet, Logstc Regresson Introducton Wth the development of nternet and moble technology, In partcular Mcroblog has greatly accelerated the speed of nformaton dssemnaton n the network. Usng mcroblog Retweet, users can very easly share Mcroblog content of other users to acheve of nformaton dssemnaton. A mcroblog after forwardng of dfferent users, the propagaton speed wll ncrease n a geometrc level. The obectve of retweet predctng s to predct accurately a message transmsson range and development trends. Through ths nformaton, the process of nformaton dssemnaton can be effectve nterventon to control the spread of the message []. Ths study s very mportant for many felds, such as vral marketng, personalzed message recommendaton [] and others. Currently, the research of retweet predcton on socal network s dvded nto two drectons. One s through studyng the nformaton propagaton path topology to buld predcton model, most research based on dynamc propagaton and vrus propagaton theory [4]. The other way to buld predcton model s based on machne learnng algorthm, conssts manly of Support Vector Machne [5-7]. The research of nformaton propagaton path topology s base on determnng a node whether forwardng. Wth large user bases and complex user relaton n The whole network topology, t's a very dffcult task that construct the topology of user networks In vew of the fact that the propagaton path topology cannot effectvely deal wth complex socal network conssts of hundreds of mllons of users. More researchers ISSN: ASTL Copyrght 05 SERSC

2 choose to use machne learnng methods to complete retweet predcton. Those use the classfcaton method to udge Whether a message wll be retweeted or not n The future. However, they only studed whether the message wll be retweeted wthout takng nto account the retweet tmes of the message. In summary, these prevous results are unsatsfactory, further studes are stll necessary. Ths paper argues that retweet predctng should be regresson analyss problem, not ust the classfcaton problem. Because we need through the forwardng tmes of the message to determne the propagaton scale, rather than whether retweeted. Ths paper reposts on buldng regresson analyss model based on Logstc Regresson algorthm to predctng the scale of nformaton dssemnaton. We selected some features that have an mportant mpact on the retweet behavor and dvded nto four categores, ncludng user features, text features, temporal feature and metadata feature. To note s that we take nto account the effect of text content of the retweet behavor. Retweet predctng based on regresson analyss Regresson analyss. Regresson analyss s a statstcal method whch s handlng the correlaton between varables. By one or more value of the varable, predct the value of another varable. Further predct the scale of development and factor analyss. Retweet Feature selecton. Because logstc regresson generally used to solve lnear regresson problem, Based on the analyss of the relevant research, ths paper choose mcroblog features of lnear relatonshp wth the forwardng and greater mpact. We use the followng features and dvde them nto for dstnct sets: User features, Text features, Temporal feature and Metadata feature. Logstc Regresson model. In statstcs, Logstc Regresson s a statstcal classfcaton model, used to predct the results of classfcaton based on one or more of the features. Logstc Regresson measures the relatonshp between varable ndependent varables and categorcal dependent, by usng probablty scores as the predcted values of the dependent varable. Thus, Logstc Regresson not only be used for solvng classfcaton problems also be used to solve the regresson problem, that s why we use Logstc Regresson modelng. Logstc Regresson (LR) and Support Vector Machne (SVM) are dscrmnatve learnng model. They can be used to establsh the predcton model. But Support Vector Machne use quadratc programmng for support vector. Algorthms for quadratc programmng nvolve calculaton of m-order matrx. (m s the number of samples). When the m s large, the storage and computaton of the matrx wll consume a large amount of machne memory and CPU. Space complexty and Tme Complexty of LR s lower than SVM. Ths paper uses multple mcroblog characterstcs durng establshng the predcton model based on LR. Therefore, the model has a number of ndependent varables. The logstc functon can be wrtten as: 0 Copyrght 05 SERSC

3 log t ( p ) p ln( p ) Where, p x... a b x b x... b, x x s ndependent varable. That s mcroblog characterstcs. p p ( y x ) s dependent varable(retweet Tmes), y s cumulatve probablty of ndependent varables. q x q 3 Experments Datasets. We run our experments wth tweets collected n February and March 03 whch obtaned from Twtter API by ourselves, because need to analyze the topc of each tweet, we retan only the Englsh text tweets. After a few prelmnary treatments, the fnal data set contans approxmately 36.8 mllon tweets and 4.56 mllon users. Table. Detaled nformaton of each dataset dataset number of tweets retweet rate maxmum number of forwardng tmes average number of forwardng tmes dataset one:tranng set % dataset one:testng set % dataset two:tranng set % dataset two:testng set % dataset three:tranng set % dataset three:testng set % dataset four:tranng set % dataset four:testng set % By defnton of forwardng tmes, the tme span of tranng set and test set need to be consstent, furthermore, n order to reduce the nfluence of data set on the results, the data set s dvded nto four parts. Thus, each tranng set or test set contans a week of tweets, and to ensure that the maxmum forwardng tmes of tranng set s greater than the test set. As shown n table.we counted the number of tweets, retweet rate and the maxmum number of forwardng tmes for each part. We can also fnd that each part contans about 30 mllon tweets and there are only % of the tweets has been reweeted n our datasets. Copyrght 05 SERSC

4 Evaluaton standards. We use MSE (Mean Square Error) to evaluate the predcton results of dfferent models, the smaller value of MSE, the better predcton results of model, and the fnal results should be the average value of four expermental, functon can be wrtten as: Where, MSE ( y, yˆ ) n samples n samples 0 ( y ŷ s the predcton value of sample, yˆ ) y s the The calculated value of sample Accordng to the Retweet Tmes defnton above. Expermental settngs. We buld classfcaton model Usng the Hong[8] and regresson model above by each part of dataset, In addton, we use MSE to evaluate the predcton results of dfferent models, the smaller value of MSE, the better predcton results of model, and the fnal results should be the average value of four expermental. For Regresson model, we establsh Logstc Regresson model usng tranng set, and n the process of model buldng we use four features: User features, Text features, Temporal feature and Metadata feature. Then predcton retweet Tmes accordng to the establshed predcton model. Fnally calculate the mean square error of predcton value and the real value for tweets. For classfcaton model, nstead of drectly predctng the forwardng tmes, we dvde the messages nto dfferent classes by the forwardng tmes: a: zero, b: between and 00, c: between 0 and 000, d: more than 000. When calculatng the MSE value, we need to gve a value for each category as ther predcted forwardng tmes: a: 0, b: 50, c: 550 and d: 000. For classfcaton model and calculate the mean square error of predcton value and the real value for tweets. Expermental results and analyss. The detaled nformaton of each dataset are shown n Table, we can see that the max of forwardng tme only 553, ths s not consstent wth the real stuaton. Gettng all tweets whch user generated s an mpossble task, because Twtter dd not fully open the data nterface, we can only crawl the data randomly n a tme sequence from Twtter API. Thus, the expermental data wll be based wth the actual stuaton, but t wll not affect the expermental results, to some extent, ths wll reduce the advantage of the regresson model. Table. The expermental results classfcaton model(mse) regresson model(mse) performance mprovement dataset one % dataset two % dataset three % dataset four % mean 393, % The data n Table also show that the MSE of regresson model based on Logstc Regresson s smaller than classfcaton model, the average of the former reached 393.4, the latter only 99.93, the predcton performance of the model s mprove Copyrght 05 SERSC

5 about 4%. In addton, there are 9% tweets of the dataset have not been retweeted, that ther forwardng tmes s 0, both regresson model and classfcaton model wll predct the forwardng tmes s 0 f the predcton s correct, then wll narrow the gap of MSE between regresson model and classfcaton model. Despte ths, the predcted performance of regresson model stll better than the classfcaton model. 4 Concluson Ths study predcts the forwardng tmes of a tweet through buldng a regresson model based on Logstc Regresson algorthm. We selected some features whch have an mportant mpact on the retweet behavor and buld the regresson analyss predctng model. Experment results based on the actual data set show the regresson analyss predctng model has a better predctng accuracy n dealng wth retweet predctng than the classfcaton model, our method s effectveness. References. Jng, X., LIU Gong-shen: Predcton of User s Retweet Behavor n Socal NetWork. Journal of SHANGHAI JIAO TONG Unversty,03,47(004): (03). Konstas I, Stathopoulos V, Jose JM: On Socal Networks and Collaboratve recommendaton [C]//Proceedngs of the 3th Internatonal ACM SIGIR Conference on Research and Development n Informaton Retreval,ACM,009:95-0 (009) 3. Naar, Denoyer L, Galllnar P.: Predctng nformaton dffuson on socal networks wth partal knowledge [C]//Proceedngs of the st nternatonal conference companon on World Wde Web,ACM,0:97-04 (0) 4. Gomez Rodrguez M,Leskovec J,Krause A.Inferrng networks of dffuson and nfluence[c]//proceedngs of the 6th ACM SIGKDD nternatonal conference on Knowledge dscovery and data mnng. ACM, 00:09-08 (00) 5. Petrovc S,Osborne M,Lavrenko V.RT to Wn! Predctng Message Propagaton n Twtter[C]//The Internatonal AAAI Conference on Weblogs and Socal Meda (ICWSM), (0) 6. Zaman TR, Herbrch R, Van G.: Predctng nformaton spreadng n twtter[c]//workshop on Computatonal Socal Scence and the Wsdom of Crowds,NIPS, 00, 04 (45): (00) 7. Luo, Z., Osborne, M.: Who Wll Retweet Me? Fndng Retweeters n Twtter [C]//Proceedngs of the 36th Internatonal ACM SIGIR Conference on Research and Development n Informaton Retreval ACM 03: (03) 8. Hong, L.G., Dan, O. and Davson, B.D.: Predctng Popular Messages n Twtter. Proceedngs of the 0th nternatonal conference companon on World Wde Web, ACM, (0) Copyrght 05 SERSC 3