Daily Mood Assessment based on Mobile Phone Sensing

Transcription

1 2012 Nnth Internatonal Conference on Wearable and Implantable Body Sensor Networks Daly Mood Assessment based on Moble Phone Sensng Yuanchao Ma Bn Xu Yn Ba Guodong Sun Department of Computer Scence and Technology Tsnghua Unversty, Bejng, Chna Run Zhu Department of Psychology Pekng Unversty, Bejng, Chna Abstract Wth the ncreasng stress and unhealthy lfestyles n people s daly lfe, mental health problems are becomng a global concern. In partcular, mood related mental health problems, such as mood dsorders, depressons, and elaton, are serously mpactng people s qualty of lfe. However, due to the complexty and unstableness of personal mood, assessng and analyzng daly mood s both dffcult and nconvenent, whch s a major challenge n mental health care. In ths paper, we propose a novel framework called MoodMner for assessng and analyzng mood n daly lfe. MoodMner uses moble phone data moble phone sensor data and communcaton data (ncludng acceleraton, lght, ambent sound, locaton, call log, etc.) to extract human behavor pattern and assess daly mood. Our approach overcomes the problem of subjectvty and nconsstency of tradtonal mood assessment methods, and acheves a farly good accuracy (around 50%) wth mnmal user nterventon. We have bult a system wth clents on Androd platform and an assessment model based on factor graph. We have also carred out experments to evaluate our desgn n effectveness and effcency. Keywords-moble phone sensor, mood assessment, behavor modelng, moble healthcare, realty mnng I. INTRODUCTION As technologes and economy contnue to develop dramatcally, well-beng goes well beyond keepng one from starvng, cold, and physcal dseases. People begn to pay more attenton to other elements that affect people s feelng of happness, of whch mental health s an mportant aspect. Mood related mental problems, such as mood dsorders, have become a global concern of human socety. The study of mood theory and the treatment of mood dsorder has been an mportant topc of psychology. Dfferent measures and treatments are appled accordng to dfferent mood status and symptoms. However, the assessment of mood has long been a challenge n mood-related study. Current mood assessment s manly based on tradtonal psychologcal measurements, lke scales and psychologcal counselng. But these methods are faced manly wth two challenges n mood measurng. One s the subjectvty of mood. Mood s wth no doubt subjectve, but for scentfc analyss, objectve mental states should be extracted from people s feelngs and expressons. Self-reported data, sufferng from ts subjectvty, cannot serve as a relable ndcator of objectve mood, wthout complex valdaton and process. The other s nconstancy. In comparson wth emoton, mood s relatvely long-tme state, but t stll changes n hours or days. Therefore, t s very dffcult to use tradtonal methods to assess mood n such frequency. Psychologcal scales, due to ther complexty, consume a large amount of tme and energy of user, whch s often annoyng to do everyday. Psychologcal counselng nvolves nteracton between clent and counselor (psychologst), and thus, t s more unrealstc to be taken frequently. Fortunately, the rapd ncreasng usage of moble phones, has opened a new possblty for daly mood assessment. Modern moble phones are equpped wth rch sensors, such as accelerometer, lght sensor, sound sensor(mcrophone), locaton sensor(gps), etc.. Besdes such sensory data, the moble phone generates soft-sensor data SMS and call nformaton that can also reflect people s daly behavor. What s more, the moble phone s usually nseparable wth ts user, and then t can use ts hard- and soft-sensors together to profle some sgnfcant behavor patterns of people. In ths paper, we propose a novel framework for daly mood assessment usng moble phone sensor data. Frst, we collect sensor data on moble phone n a smart manner, whch s both effcent and energy-frendly. Second, dfferent types of sensor data and communcaton events are then combned together to model people s daly behavor pattern, ncludng physcal movements, mnor motons, locaton trace, etc.. Thrd, these features are used as nput to model users real-tme mood. We buld a general model based on factor graph to do the analyss and assessment. We fnally buld a prototype system for collectng sensor data and communcaton data from Androd smartphones. We use self-report mood data as ground truth and perform the test-bed experment on a number of users for one month to evaluate the effectveness of our desgn. The contrbutons of our work are as follows. Frst, the proposed mood assessment s carred out solely on moble phone, wthout requrng any addtonal medcal devces, and then t s convenent to be used at low cost and supports real-tme dynamc mood analyss. Second, the use of moble phone sensor and communcaton data mproves the objectvty n mood assessment, by elmnatng expresson and comprehenson error of psychologcal scale and questonnare. Thrd, we buld a testbed system wth /12 $ IEEE DOI /BSN

2 the Androd phone and a back-end server, to evaluate the feasblty and effcency of our desgn. The rest of the paper s organzed as follows. Secton II brefly ntroduces some work related to ths paper, ncludng moble healthcare and mood assessment. Secton III descrbes the system we have bult for dong mood assessment on Androd platform. Secton IV gves our analyss based on the collected data and the proposed model. Secton V presents expermental results. Secton VI concludes the paper. II. RELATED WORK Our work ams at performng mood assessment by usng phone-based sensng technques. In ths secton, we frst brefly ntroduce these two domans respectvely, and then present some related work of combnaton of both areas. Mood and Mood Assessment. Mood and mood assessment s an mportant topc of psychology, and a lot of work has been done n the mood theory and the methodology of assessng mood n daly mood. Thayer [1] defnes mood as relatvely long lastng emotonal state, whch s dfferent from emoton, as mood s less specfc, less tense, and less lkely to be related to a partcular stmulus or event. It s now wdely recognzed that overall mood can be vewed as producton of two unrelated dmensons - energy and stress [2] [3]. In practcal research, mood s often measured n three basc dmensons: dspleasure (overall), tredness and tensty [2]. Mood assessment s usually acheved usng self-report methods lke psychologcal scale. Wlhelm, et al. [2] has evaluated the psychometrc propertes of a short mood scale to assess mood states n daly lfe, and dscusses problems brought by measures. Moble Phone Sensng. Moble phones, especally smartphones, are developng rapdly n recent year, and are becomng the central devces of communcaton and computng n people s daly lfe. Along wth the development of moble phones, moble phone sensng has also gan much popularty due to ts convenence [4] [5]. However, usng moble phone as the only devce for ndvdual s daly behavor sensng s stll a relatvely new topc. Nathan, et al. [6] descrbes usng data generated by moble phones to dscover ndvdual and socal patterns n human socety. Funf 1 s a platform buld on Androd that can collect varous knds of moble phone generated data for user to vew, trace and analyze. Data that can be collected nclude sensor data, communcaton data and phone usage data. Cu, et al. [7] proposes an energy-savng method for detectng user actvty usng moble phone accelerometers. Yuk, et al. [8] proposes a context acquston method usng a moble phone, usng locaton, accelerometer, sound, etc.. Context ncludes user actvty, envronment status. Constandache, et al. [9] dentfes the possblty of usng electronc 1 compasses and accelerometers n moble phones, as a smple and scalable method of localzaton wthout war-drvng, whch s another approach for locaton detecton wthout energy-consumng GPS. There s not much prevous work concernng mood assessment based on moble phones. Moturu, et al. [10] explores and uncovers the assocatons between sleep, mood and socablty by analyzng moble-phone-generated socal communcaton data and self-reported mood and sleep data. Tang, et al. [11] propose a method for quanttatvely predct users emotonal states based on moble socal network. Ths pece of work s dfferent from our work n that the model nput s mostly socal attrbutes, wth just a few moble phone based attrbutes lke locaton, and the model target s short-scale emoton, nstead of daly mood. III. SYSTEM OVERVIEW Our system, called MoodMner, focuses on daly mood assessment based on moble phone sensors and communcaton events. The proposed system contans a clent applcaton based on Androd platform, and a back-end server bult n Java. The clent applcaton collects sensor data as well as communcaton logs, apples smple analyss on the data, and fnally transfers the data to the server. The back-end server stores data from all users and creates personalzed model that outputs daly mood based on collected data. The output mood s then fed back to the user wth a confgured regular perod. We next present n detal the mplementaton of our system. We only cover Androd platform for now, but snce sensor avalablty does not dffer much on dfferent moble platforms, our approach can be easly adapted for other platforms. A. Survey of Androd Platform Sensors Androd 2 s a Lnux based operatng system for moble devces such as smartphones and tablet computers. 3 Androd conssts of a kernel based on the Lnux kernel, and a moble optmzed vrtual machne called Dalvk. Applcatons on Androd s usually wrte n Java and translated to Dalvk dex-code runnng on Dalvk. Startng from 2007, Androd s now one of the most popular moble operaton system. Androd has bult-n support for a wde range of sensors. Currently, the Androd API of verson 16 descrbes 11 sensor types 4, wth new sensor types addng gradually. What s more, androd phones all have sound sensor mcrophone, and most of them have the ablty of sensng locaton usng GPS, WIFI, etc.. But, snce Androd powers a large varety of devces, and dfferent devces run dfferent versons of Androd system, ther s no guarantee of the avalablty of a partcular sensor type on a partcular devce. We have (operatng system)

3 selected several sensor types based on the usefulness for daly behavor modelng as well as avalablty on dfferent devces. Androd uses broadcaster-recever pattern to manage sensors. When an applcaton wants to use a sensor, t frst regsters tself as a recever to the system, and at the same tme, t specfes a senstvty level at whch t wants to receve sensor data. Then an event s sent to the applcaton when sensor value s changed (accordng to the senstvty level) [12]. Accelerometer. Androd accelerometer data s calculated by measurng forces appled to the sensor tself, ncludng the force of gravty. There are three readngs correspondng to three axes of Androd coordnate system: the x-axs horzontal and ponts to the rght, the y-axs vertcal and ponts up, and the z- axs ponts towards the outsde of the front face of the screen [12]. Thus accelerometer readng at tme t can be denoted as (x (t),y (t),z (t) ). Accelerometer may be the most common sensor avalable on Androd devces, and t has relatvely low energy cost. Accelerometer data can be used to detect user s physcal movement, whch s very mportant n behavor modelng. For these reasons, accelerometer plays as a central role n our system. Sound sensor. Actually, sound sensor s not an Androd sensor type. However, every phone s ftted wth a mcrophone, whch can be used to sense background sound and user s voce. In fact, envronmental sound can affect human mood, and human voce can dsclose mood states of the speaker. Locaton. Locaton s closely related to mood, and can also provde context nformaton for user behavor. Androd platform provdes both coarse and fne locaton servces. Locaton can be obtaned by both GPS, WIFI, or cellular network. Fne locaton provded by GPS can acheve an accuracy better than 3 meters; the locaton error wthout GPS may be of tens of meters, but usually acceptable for buldng-level localzng. Lght sensor. Ambent lght sensor s used n moble phone for auto-adjustng screen brghtness, controllng keypad backlght, etc.. Optcal track pad on some phones also uses lght sensor. In daly mood assessment, ambent lght can be employed to determne the poston of the phone n bag, n pocket, or on desk and to detect envronment brghtness. B. Applcaton Implementaton Our MoodMner system conssts of the followng components: collectng sensor data, obtanng communcaton statstcs, and recordng user-reported mood status on daly bass. Our system works as follows. Sensor data collectng s started as soon as the app starts. Data of dfferent sensor types s recorded usng dfferent strateges (explaned later). Collected sensor data s stored n text fles on the phone (Androd has bult-n support for SQLte database, but ts performance for frequent large-amount data readng/ wrtng s very poor compared to usng fles.) Users can report ther mood states at any tme, and the reported data wll also be stored n text format. Each day s data wll be uploaded n the next day. Communcaton statstc data s collected from Androd system log before the upload operaton. Data uploadng happens automatcally when the phone has a vald WIFI Internet connecton. If the phone has not connected to WIFI network for a whole day, the data wll then be saved on the phone untl the tme the WIFI connecton s avalable. The applcaton can show the calculated features related to user s behavor pattern, as well as the assessed mood. These nformaton can also be sent to other recpents gven the approval of the user. The user s able to send feedback of the assessed mood, whch wll be used n future assessment. Fgure 1 shows the man nterface of MoodMner, whch dsplays calculated feature values and mood. Fgure 1. Informaton dsplay nterface of MoodMner C. Sensor Management Strategy Moble phone sensors usually cost consderable amount of energy, and ntensve use of sensors wll serously affect battery lfe. In our system, we use dfferent strategy to manage workng cycle of dfferent sensors, consderng both energy consumpton and modelng demand. Two mportant and energy-costng sensory types are accelerometer and locaton. Accelerometer s avalable on most androd phones, and ts energy cost s consderably low among sensors. So we use accelerometer contnuously n our system, for detectng users actvty as well as mcromoton (whch s explaned n Secton IV-A). When raw accelerometer data s receved, t s frst compared wth the prevous readng. The new value s recorded only when the two values have sgnfcant dfference, whch ndcates the physcal atttude of the phone has changed dramatcally (e.g. pcked up by the user). Based on experment, we only record value whose change on any axs s greater than 0.5m/s

4 The other sensory type s locaton. It s measured by sensng ether GPS or WIFI sgnal, both of whch consumes a large amount of energy. We use a smart framework for locaton sensng, by combnng locaton and accelerometer readngs. Detaledly, the locaton sensor does not work contnuously unless the phone s n movng state (dentfed va accelerometer readngs); ths provdes better accuracy whle prolongng the battery lfe [7]. IV. DATA AND MODEL DESCRIPTION A. Problem and Feature Defnton Our approach deals wth the problem of daly mood assessment usng moble phone sensor and communcaton data. In ths secton, we wll gve a seres of defntons, and then formalze the problem. Defnton 1: Mood. Mood s defned as daly emotonal status of a person. There are a number of structure theory about mood, most of whch decomposte mood nto three dmensons. Thayer [1] further states that the three dmensons are not equal - Two of them are basc dmensons and the other s a mx of two basc dmensons. We adopt Thayer s theory and choose three dmensons to represent an ndvdual s daly mood, ncludng dspleasure, tredness and tensty [3]. Degree of dspleasure of user n day t s donated as d (t), tredness as t (t) and tensty as te (t). The three values d (t),t (t) and te (t) are nteger values whose range s 1 to 5, wth 1 for least severe and 5 for most severe. For example, d (t) can be any value of very pleasant, pleasant, medum, unpleasant and very unpleasant, wth very pleasant beng 1 and very unpleasant beng 5. Overall mood of user n day t s m (t), where m (t) =(d (t),t (t),te (t) ). As explaned above, the three dmenson of mood s not totally ndependent. In fact, dspleasure s an overall evaluaton of mood states, whose value s affected by tredness and tensty. Generally, people who feel less tred and more relax may feel more pleasant. Modelng mood as three dmensons gves us flexblty to fnd relatonshps between daly behavor and a partcular mood dmenson, whch s often more remarkable. For example, mcromoton (whch s explaned n Secton IV-A) s more related to degree of tensty than degree of tredness. What s more, the relatonshp between dfferent dmensons can help valdate user-report data, and thus ncreases the relablty of the data. Defnton 2: Daly behavor features. We have defned a set of daly behavor features extracted from moble sensor data and communcaton data. All features of user n day t s denoted as X (t). Specfcally, x (t) j s the value of feature j of user at day t. Next we wll defne the followng features: Locaton. We have recorded the user locaton trace (usng lattude and longtude). the recorded locaton s smply clustered usng K-Means [13], and transformed nto regon. Mcromoton. Mcromoton s defned as the followng user behavor a user pcks up the phone and does nothng useful for no longer than a few seconds. Ths feature s extracted usng accelerometer raw data. Communcaton frequency. We calculate the frequency of user communcatng wth others usng the moble phone, consderng both text messages and call logs. Ths feature s the accumulated number of communcaton tmes per day. Actvty. We also use accelerometer data to extract user actvty. Actvty ncludes walkng, runnng, sttng, and standng [14]. We calculate the proporton of dfferent actvtes durng the day. Fnally, the learnng problem can be defned as: Learnng problem. Gven a feature set X (t), and the prevous mood state m (t 1) the goal s to establsh an assessment functon f that outputs mood m (t), more specfcally, d (t), t (t) and te (t). Formally, f s defned as: f(x (t),m (t 1) ) m (t) Generally speakng, ths can be treated as a classfcaton problem, wth the classfcaton target beng dscrete ordnal levels. Wth the Factor Graph theory, we desgn an algorthm to solve ths classfcaton problem, whch wll be descrbed n Secton IV-C. B. Deployment and Observaton We have deployed the applcaton on 15 users and collected data for 30 days. Most of the users are college students and teachers, others beng urban whte-collar workers. Each clent generates about 1 MB data per day, and the data s transfered to and stored n the central server database. We conduct a seres of analyss on the data, and then present the results n ths secton. We have studed the mood dstrbuton of users. Data reveals that user-reported mood follows an approxmately normal dstrbuton, wth central levels takng a large proporton (as s seen n Fgure 2). Ths mples that for ordnary people, extreme mood s very unusual. We have also nvestgated relatonshps between dfferent features of a person and temporal correlaton of each feature. For example, Fgure 3 shows the dstrbuton of dfferences between two successve tme perods (two days). In ths fgure, horzontal axs represents the change of one s mood level compared wth yesterday (0 means no change). It can be observed that daly mood has a strong tme correlaton, whch means people tend to have smlar mood as yesterday, wth dramatc changes (more than 2 levels) are rarely observed. There are also some correlaton patterns observed between mood and sensor features. For example, we have studed 145

5 Number of Days (Percentage) Number of Days (Percentage) Fgure Fgure 2. Mood Level Mood level dstrbuton Mood Level Dfference Dspleasure Tredness Tensty Dspleasure Tredness Tensty Dstrbuton of mood dfference of two successve days the correlaton between mcromoton and mood, whch s llustrated n Fgure 4. It can be observed that, the amount of mcromoton ncreases wth the level of dspleasure and tensty, but do not show strong correlaton wth the tredness dmenson of mood. Average Mcromoton Amount (Percentage) Fgure 4. Dspleasure Tredness Tensty Mood Level Mcromoton dstrbuton wthn dfferent mood level C. Model Descrpton Based on our data observaton, we have developed a classfcaton algorthm based on factor graph. Factor graph s a bpartte graph used for descrbng algorthms that deal wth complcated global functons of many varables, and the global functon can be factored as a product of local functons, each of whch s a smpler functon of a small subset of the varables [15], [16]. Before dvng nto the model, several reasonable assumptons are made based on expermental observatons to smplfy our model. Frst of all, we assume that user mood has Markov property, whch means that gven m (t 1), m (t) and m (t ) are condtonally ndependent for all t t 2. We also assume that gven m (t), all features x (t) j X(t) are condtonally ndependent of each other. Based on the observatons and assumptons, we defne the followng factor functons: Temporal correlaton factor functon h(m (t 1),m (t) ). It reflects the persstence of mood. Based on Markov property, the decay of user mood can be modeled as: h(m (t 1),m (t) )=α e λ m(t) m (t 1) Feature correlaton factor functons c(x (t) j,m(t) ). There are two knds of user features: mood and behavor features. Accordng to the assumpton, we only have to consder correlatons between mood and behavor features. We adopt a Nave Bayes (NB)-based strategy, so c(x (t) j,m(t) ) s modeled usng Bayes theory, and the pars prevously observed have hgher values. V. EVALUATION The dataset we obtaned by the experment contans 2,872,311 sensor data records (ncludng accelerometer, locaton, etc.), 3,169 SMS records, and 5,871 call records. The raw data s aggregated nto features, wth one value for each feature n one day of every subject. Self-reported mood data s algned nto the levels pre-defned, and for those who marked ther mood more than once a day, we take a weghted average to be the mood value of the day, wth those later reported values havng hgher weghts. Users are requred to run the app n ther phones durng ther actve tme perod of a day, and they can of course stop the app when they are about to go to sleep, because sensor data durng sleep does not effectvely reflect daly behavor and consequently s not useful n mood assessment. Snce what we address s a classfcaton for ordnal dscrete levels, we apply metrcs of accuracy (percentage of days that predcted value s exactly true value) and RMSE (root-meansquare error) to measure the effectveness of our method. Table I PERFORMANCE OF MOODMINER MOOD ASSESSMENT Target Accuracy RMSE Dspleasure 52.58% Tredness 45.36% Tensty 47.42% Dscusson. The assessment model acheved an acceptable performance, wth an accuracy around 50%. Error of the (1) 146

6 output comes n several aspects. Frst of all, because of the subjectveness of mood, sensor data, communcaton data, and hstorcal mood data may fal to reflect mood swngs n some crcumstances t just changes wthout any ndcaton. Furthermore, people show sgnfcant dfference n daly behavor style and use pattern of moble phone, makng t dffcult to buld a model workng well for anyone. For some people, moble phones (though so powerful) s just a tool for makng calls and recevng messages, and they would leave ther phone on the desk forever unless there s an ncomng call. The model may fal for these users. There are also some knds of moble phone data that we have not taken nto consderaton, lke the moble apps usage data and other communcaton data apart from calls and SMS (e.g. Talkbox 5 ). Such mssng data may also affect the performance of our model. VI. CONCLUSION In ths paper, we propose a novel approach to assessng ndvdual s daly mood usng moble phone. We frst extract several features from moble phone data, and then propose a method based on factor graph for assessng mood usng these features. We have bult a system for data collecton and model mplementaton. The clent applcaton works on Androd platform, whch collects sensor data and communcaton data, whle the algorthm runs on the server applcaton, whch receves data and provdes mood states as output. Expermental results on 15 users for 30 days shows that our system can effectvely assess daly mood objectvely, wth mnmal user nterventon. The problem of moble phone-based mood assessment s an nterestng topc n sensor data mnng and stll needs more exploraton. For future work, more socal networkng nformaton wll be taken nto consderaton, lke the mood propagaton, nstead of just countng communcaton frequency. Then the personal assessment model can be transformed nto a model on a dynamc socal graph. We can also ntegrate nto our model wth more context nformaton, such as the weather and the publc events of places the user vsts. In sensor data processng, cross-sensor features may also help mprove the performance of our model. ACKNOWLEDGMENT Ths work s supported by the Natonal Scence Foundaton (NSF) of Chna (NO ). REFERENCES [1] R. E. Thayer, The Bopsychology of Mood and Arousal. New York, NY: Oxford Unversty Press, [2] P. Wlhelm and D. Schoeb, Assessng mood n daly lfe - structural valdty, senstvty to change, and relablty of a short-scale to measure three basc dmensons of mood, European Journal of Psychologcal Assessment, vol. 23, pp , [3] R. E. Thayer, The Orgn of Everyday Moods: managng energy, tenson and stress. New York, NY: Oxford Unversty Press, [4] N. Lane, E. Mluzzo, H. Lu, D. Peebles, T. Choudhury, and A. Campbell, A survey of moble phone sensng, Communcatons Magazne, IEEE, vol. 48, no. 9, pp , sept [5] C. Seeger, A. Buchmann, and K. V. Laerhoven, myhealthassstant: A phone-based body sensor network that captures the wearer s exercses throughout the day, n BodyNets, [6] N. Eagle and A. (Sandy) Pentland, Realty mnng: sensng complex socal systems, Personal Ubqutous Comput., vol. 10, no. 4, pp , Mar [7] J. Cu, G. Sun, and B. Xu, Ad-sense - actvty-drven sensng for moble devces, n The 9th ACM Conference on Embedded Networked Sensor Systems (SenSys 11). [8] Y. Arase, F. Ren, and X. Xe, User actvty understandng from moble phone sensors, n Proceedngs of the 12th ACM nternatonal conference adjunct papers on Ubqutous computng, ser. Ubcomp 10. New York, NY, USA: ACM, 2010, pp [9] I. Constandache, R. Choudhury, and I. Rhee, Towards moble phone localzaton wthout war-drvng, n INFOCOM, 2010 Proceedngs IEEE, march 2010, pp [10] S. Moturu, I. Khayal, N. Aharony, W. Pan, and A. Pentland, Usng socal sensng to understand the lnks between sleep, mood, and socablty, n Prvacy, securty, rsk and trust (passat), 2011 eee thrd nternatonal conference on socal computng (socalcom), oct. 2011, pp [11] J. Tang, Y. Zhang, J. Sun, J. Rao, W. Yu, Y. Chen, and A. Fong, Quanttatve study of ndvdual emotonal states n socal networks, IEEE Transactons on Affectve Computng, [12] Sensorevent documentaton for androd developers. Androd Open Source Project. [Onlne]. Avalable: androd.com/reference/androd/hardware/sensorevent.html [13] J. A. Hartgan and M. A. Wong, Algorthm as 136: A k- means clusterng algorthm, Journal of the Royal Statstcal Socety. Seres C (Appled Statstcs), vol. 28, no. 1, pp. pp , [14] L. Bao and S. Intlle, Actvty recognton from userannotated acceleraton data, n LNCS 3001, 2004, pp [15] F. R. Kschschang, B. J. Frey, and H.-A. Loelger, Factor graphs and the sum-product algorthm, IEEE Transacton on Informaton Theory, vol. 47, pp , Feb [16] J. Hopcroft, T. Lou, and J. Tang, Who wll follow you back? recprocal relatonshp predcton, n The 20th ACM Conference on Informaton and Knowledge Management (CIKM 2011)