User Group Oriented Temporal Dynamics Exploration

Zhiting Hu1, Junjie Yao2, Bin Cui11Department of Computer Science,

Key Lab of High Confidence Software Technologies (MOE), Peking University2University of California, Santa Barbara

zhitinghu@gmail.com, bin.cui@pku.edu.cn,jjyao@cs.ucsb.edu

Abstract

Temporal online content becomes the zeitgeist to reflect ourinterests and changes. Active users are essential participantsand promoters behind it. Temporal dynamics becomes a vi-able way to investigate users. However, most current workonly use global temporal trend and fail to distinguish suchfine-grained patterns across groups. Different users have di-verse interest and exhibit distinct behaviors, and temporal dy-namics tend to be different.This paper proposes GrosToT (Group Specific Topics-over-Time), a unified probabilistic model to infer latent user groupsand temporal topics at the same time. It models group-specifictemporal topic variation from social content. By leveragingthe comprehensive group-specific temporal patterns, Gros-ToT significantly outperforms state-of-the-art dynamics mod-eling methods. Our proposed approach shows advantage notonly in temporal dynamics but also group content modeling.The dynamics over different groups vary, reflecting thegroups’ intention. GrosToT uncovers the interplay betweengroup interest and temporal dynamics. Specifically, groups’attention to their medium-interested topics are event-driven,showing rich bursts; while its engagement in group’s domi-nating topics are interest-driven, remaining stable over time.

IntroductionSocial media, has become a pervasive and convenient plat-form for billions of users to share their thoughts and feel-ings every day. Prominent examples include social networkand micro-blog service Facebook, Twitter and Weibo1. Ac-tive users are essential participants and promoters behind it.Users are not alone, and they form the interest groups ex-plicitly/implicitly. As the basis for user participation and en-gagement, user groups become a critical factor in participat-ing and promoting social media dynamics (Zhang, Wang,and Feng 2013; Danescu-Niculescu-Mizil et al. 2013).

Different users enjoy varying behavior patterns andpropensities, leading to distinct temporal dynamic behaviorsin response to popular topics or breaking events. One ex-ample of group dynamics diversity is illustrated in Figure 1.We present the temporal distributions of topic “Astronomy”

Copyright c© 2014, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

1facebook, twitter.com, weibo.com

0

0.01

0.02

0.03

12/01/2012 12/31/2012 01/30/2013

Pop

ula

rity

Time

(a)

0.0E+00

6.0E-02

1.2E-01

1.8E-01

12/01/2012 12/31/2012 01/30/2013

Pop

ula

rity

Time

Meteorite crash in Russia 02/15/2013

(b)

Figure 1: Temporal dynamics of topic “Astronomy” withintwo groups, namely, (a) the group mainly interested in “As-tronomy”, and (b) the group mainly interested in “Sports”,from Dec 2012 to Feb 2013. The breaking event of Russianmeteor explosion can be easily identified from (b).

in two user groups of a micro-blog site, interested in “As-tronomy” and “Sports”, respectively. We can clearly observehighly different patterns. The huge burst in group (b) coin-cides with the meteorite crash in Russia, Jan 15, 2013. Incontrast, though group (a) exhibited increasing activities atthat time, the degree is not remarkable.

Temporal dynamic patterns across different groups havepractical implications for content extraction and group mod-eling. It opens up new insights into temporal dynamicsmechanism, e.g., how users’ interest correlates with theirtemporal behaviors; and how a piece of content attracts at-tentions from different users.

In this paper, we combine user group and social mediadynamic analysis by exploring group-specific topic tempo-ral dynamics. Specifically, we extract and analyze temporalvariation patterns of topics within different user groups atthe same time.

Though a growing line of research has focused on the is-sues raised by the rich temporal variation of social mediacontent (Matsubara et al. 2012; Yang and Leskovec 2011;Wang and McCallum 2006), they are limited in the aggregat-ed dynamics of social media, and failed to distinguish suchfine-grain patterns across groups. This problem can be evenchallenging due to volatile user behaviors. Groups can enjoyinterest in multiple topics with various levels, while topicscan exhibit diverse temporal patterns within different group-s. Such interdependence requires simultaneous extraction ofgroups and topics, and accurately modeling the intricate cor-relation is also nontrivial.

This paper tackles the above challenges by presentingGrosToT (Group Specific Topics-over-Time), a unified mod-el that uncovers latent groups and topics. It also extractsgroup-specific topic temporal dynamics. Basic idea of Gros-ToT is to leverage temporal content characters to capturegroups’ changing concern on various topics. Specifically, weassociate each group with a distribution over topics, model-ing its diverse interest. Each topic is also associated witha set of distributions over time, one for each group, flexi-bly capturing the different dynamic patterns within differentgroups. Though seeming challenging, we provide a well de-signed generative model structure to guarantee the perfor-mance.

We investigate the performance of GrosToT on a large-scale micro-blog dataset consisting of 14M posts generatedby 0.52M users, spanning three months period, from Dec2012 to Feb 2013. CrosTot shows significant improvemen-t over state-of-the-art temporal modeling methods, not onlyin topic extraction but also the temporal prediction. We al-so find novel interplay phenomenons between user groupsand temporal dynamics. Group’s attention to their medium-interested topics are event-driven, exhibiting bursty engage-ment in the particular topic. In contrast, dominated topics ofa group usually bear stable popularity for this topic along thetime, showing an interest-driven difference. Take the exam-ple of online marketing: with the better temporal modelingunder user group angle, ad campaigns can be designed andbe launched specifically to different user groups and timeslots.

To summarize, we make the following contributions inour work.• We identify the problem of topic temporal variation across

user groups. To the best of our knowledge, this angle hasnot been offered by previous research.

• We propose a unified probabilistic model, GrosTOT,which uncovers the topics and groups as well as cap-tures the group-specific temporal dynamics of topics. Thismodel achieves better performance over state-of-the-artmethods.

• We present and analyze the interplay between group in-terest and temporal dynamics. This further demonstratesthe potential of GrosToT in temporal extraction and groupmodeling.The rest of this paper is organized as follows: §2 reviews

related work; §3 formulates the problem and describes theproposed model; §4 presents our experimental results and

§5 shows the analysis cases; and finally, §6 concludes thispaper.

Related WorkThere has been an increasing research interest of online tem-poral dynamics. Both content and structure based methodsare proposed.

Topic modeling approaches are used to extract temporalcontent (Blei, Ng, and Jordan 2003; Wang and McCallum2006; Blei and Lafferty 2006). In Latent Dirichlet Alloca-tion(LDA), documents are modeled as a distribution over ashared set of topics, while topics themselves are distribu-tions over words. Topics Over Time (TOT) (Wang and M-cCallum 2006) jointly models the text and time stamp byassuming that both words and time stamps are generatedby latent topics. Specifically, time stamps are drawn froma Beta distribution, which only allows a unimodal tempo-ral distribution over time for each topic. EUTB (Yin et al.2013) distinguishes stable topics (e.g. topics on user inter-est) and bursty topics (e.g. topics on emergencies) in a uni-fied PLSA-based model. It models the topic distribution oftime periods by assuming that topics are generated by usersor time periods. Another set of topic model based methodsmakes Markovian assumption on topic variation. They di-vide time into epoches, and assume that topics evolve basedon their states in the previous epoch (Blei and Lafferty 2006;Zhang et al. 2010; Ren, Dunson, and Carin 2008; Hong et al.2011). In contrast, our proposed model, GrosToT, utilizesmultinomial distribution which is able to model multimodalvariation and thus is more flexible for capturing topic tem-poral dynamics.

Some recent efforts have been made on analyzing tempo-ral behaviors of users w.r.t. certain aspects. A meme-trackingapproach is presented in (Leskovec, Backstrom, and Klein-berg 2009) to monitor dynamics of news cycle by identify-ing and tracking short textual phrases. The K-Spectral Cen-troid clustering algorithm (Yang and Leskovec 2011) findsout six classes of temporal variation pattern in online me-dia. A flexible analytical model SPICKM (Matsubara et al.2012) generalizes theoretical models for the rise and fall pat-terns of propagation. A framework is proposed in (Danescu-Niculescu-Mizil et al. 2013) to track linguistic change of t-wo online communities and identify a determined two-stagelifecycle w.r.t. community members’ susceptibility to lin-guistic evolution. Public opinion shift in response to politicalevents is explored (Lin et al. 2013), by categorizing twitterusers into two biased sets and performing sentiment analy-sis in these two groups respectively. These works focus onprofiling user groups’ reaction to some particular topics orevents. Our model is much more general in the sense of au-tomatically identifying groups and topics, as well as theirinterplay from social media stream.

All the above methods only explore aggregated dynam-ics, and ignore the fact that different user groups with di-verse propensities and behavior patterns tend to have varyingtemporal dynamics. GrosToT distinguishes patterns of top-ic temporal dynamics across different groups, which allowsfine-grained analysis of temporal dynamics.

Group Specific Topics-over-Time ModelPreliminariesWe introduce the notation used in GrosToT and formal-ly define our problem. Without loss of generality, we usemicro-blog as our problem setting. Tweets are messagesposted by users. We consider a stream of D tweets D ={d1, d2, . . . , dD}. Each tweet di is generated by a userui ∈ {1, 2, . . . , U} at timestamp ti ∈ {1, 2, . . . , T}. HereU and T denote the total number of users and time points,respectively. Each tweet di contains a set of words Wi ={wi1, wi2, . . . , wiNi} from a given vocabulary. Here Ni de-notes the number of words in di.

Groups and topics are both latent factors to be extracted,indexed by g ∈ {1, 2, . . . , G} and k ∈ {1, 2, . . . ,K} re-spectively. Here we assume there areG groups andK topicsin the social media.

For each topic k and group g, we define group-specifictopic temporal dynamics as a multinomial distribution overtime points ηkg . Our fundamental task is to uncover ηkg foreach topic within each group from the input tweet stream.Later ηkg can be used for temporal analysis and group mod-eling.

Model StructureNow we present the structure and generative process of pro-posed model, GrosToT (Group Specific Topics-over-Time).Tweets are usually written by users under certain social con-text. Therefore, the temporal characters of tweets effectivelyrepresent the group’s real-time concern on various topics andevents, as well as provide opportunities to accurately capturegroup-specific temporal topic dynamics.

Each topic k has a distribution over words φk. We denotethe number of topics as K. Unlike long documents such asnews articles which are modeled with a mixture of topics(Blei, Ng, and Jordan 2003), a tweet is very short (i.e. lessthan 140 characters) and thus is most likely to be about a s-ingle topic (Diao et al. 2012; Zhao et al. 2011). Each tweet istherefore suitable to be assigned a single latent topic respon-sible for generating its words. This facilitates the followingmodel structure.

We assume there areG groups. Each group g is associatedwith a topic distribution θg to capture its interest in multipletopics with varying strengths. In real life, users are usuallycharacterized by multiple group memberships (Xie, Kelley,and Szymanski 2013; Airoldi et al. 2008). We therefore as-sociate each user u with a distribution over groups πu.

As mentioned above, a tweet reflects the temporal con-text of a certain group. Hence each tweet is associated witha group indicating the group context it is related to, and it-s timestamp is then generated by the time distribution ηkcspecific to the group and topic assigned to it.

Figure 2 shows the graphical representation of GrosToT.Consider a user u who writes a tweet di, she first selects thegroup context gi according to group distribution πu, and thenchooses a topic zi according the group’s topic distributionθgi . The words in the tweet are generated from the worddistribution φzi specific to that topic, and the timestamp is

α θg

δ ui

πu zi wij

ti

λ

β φk

ηkg

gi

G

U

D

Ni

K

K ×G

Figure 2: GrosToT model. A double circle indicates a hyper-parameter; a single hollow circle indicates a latent variable;and a filled circle indicates an observed variable.

generated from the temporal distribution ηkc for that topicand group.

We therefore set up the following generative process forthe tweet stream.

• For each group g = 1, 2, . . . , G,

– Draw the distribution over topics, θg|α ∼ Dirichlet(α).

• For each user u = 1, 2, . . . , U

– Draw the distribution over groups,πu|δ ∼ Dirichlet(δ).

• For each topic k = 1, 2, . . . , K,

– Draw the distribution over words, φk|β ∼ Dirichlet(β).– For each group g = 1, 2, . . . , G,∗ Draw the distribution over time stamps,ηkg|λ ∼ Dirichlet(λ).

• For each tweet i = 1, 2, . . . , D,

– Draw group indicator, gi|πui ∼ Multi(πui).– Draw topic indicator, zi|θgi ∼ Multi(θgi)– For each word j = 1, 2, . . . , Ni,∗ Draw word, wij |φzi ∼ Multi(φzi).

– Draw time stamp, ti|ηzigi ∼ Multi(ηzigi).

Model InferenceExact inference is intractable due to the coupling parametersin GrosToT model. We therefore use collapsed Gibbs Sam-pling (Griffiths and Steyvers 2004) to obtain samples of thehidden variable assignment, based on which the unknownparameters {π, θ, φ, η} can be estimated. For simplicity wefix the hyperparameters to β = λ = 0.01, δ = 50/C andα = 50/K.

The inference process is listed as follows.We first sample the group indicator gi for each post di:

P (gi = g|zi = k, ti = t, g−i, z−i, t−i, .)

∝ ni,g + δ

ni,· + Cδ· ng,k + α

ng,· +Kα· ngk,t + λ

ngk,· + Tλ,

where ni,c is the number of tweets by user ui assigned togroup g; ng,k is the number of tweets assigned to groupg and generated by topic k; nck,t denotes the number oftimes that timestamp t is generated by group g and topic k;Marginal counts are represented with dots. All the countersmentioned above are calculated with the tweet di excluded.

We then sample the topic indicator zi for each post di:

P (zi = k|gi = g, ti = t, g−i, z−i,w, t−i, .)

∝ ng,k + α

ng,· +Kα· ngk,t + λ

ngk,· + Tλ·∏V

v=1

∏ni,v−1q=0 (nk,v + q + β)∏ni,·−1

q=0 (nk,· + q + V β),

where ni,v is the number of times word v occurs in the tweetdi; nk,v is the number of times word v is assigned to topick. Marginal counts are represented with dots. Note that nk,vand nk,· is calculated with the tweet di excluded.

Extraction PerformanceWe conduct empirical study to evaluate GrosToT’s modelingperformance. Both topic extraction and temporal predictiontasks are investigated.

Experiment SetupData Set We use a large real dataset crawled from SinaWeibo2, one of the most popular micro-blog platforms. Werandomly sample users and get their streaming updates. Tosave the API quota, low frequent users are ignored since theyhave low contribution to groups’ dynamics. After removingstop words and low quality posts, we obtain a dataset con-sisting of about 0.52M users, 14M posts and 112M wordswith a vocabulary of size 89K. The posts are distributed al-most evenly in a three-month time period from Dec 2012through Feb 2013. We use one day as the time window.

We randomly select 80% tweets as the training set whilethe remaining 20% as testing set.

Baselines We compare our proposed GrosToT with twostate-of-the-art competitors, i.e., EUTB (Yin et al. 2013) andTopics over Time (TOT) (Wang and McCallum 2006). EUT-B extracts temporal patterns by modeling topic distributionsof time slots. Similar to GrosToT, TOT treats both words andtime stamps as variables generated by latent topics. Both ofthese two approaches only uncover global trends of topicswithout distinguishing dynamic patterns across groups.

Topic ExtractionA fundamental requirement of temporal modeling is that itcan extract intuitive and representative topics of groups ortemporal events. We display one of the extracted topics inFigure 3. From the word cloud we see that the discussedtopic is mainly about “Sports”. It reflects not only generalthemes, e.g., “football” and “basketball”, but also the sea-sonable focuses, e.g., “Real Madrid” and “Kobe Bryant”.

We next proceed to a quantitative way to measure the top-ic extraction performance. Perplexity (Blei, Ng, and Jordan2003) is a common metric in the topic modeling area, mea-suring how well the words of test documents are represented

2http://weibo.com

Figure 3: Word cloud of extracted topic “Sports”.

by the word distribution of extracted topics. A lower per-plexity value indicates better performance of the model.

For a test set of D posts, the perplexity score is measuredby:

perplexity(Dtest) = exp{−

∑Di=1 log p(Wi)∑D

i=1Ni

},

where p(Wi) is the probability of the words in the test doc-ument di. In the case of GrosToT, it is computed as:

p(Wi) =∑g

πuig

∑k

θgk∏j

φkwij.

Figure 4 shows the perplexity values for three competitorsas the numbers of topics (i.e. K) and groups (i.e. G) change.Our proposed model consistently yields better performance,indicating higher quality of extracted topics. Suitable num-bers of topics (i.e. 100) and groups (i.e. 50) give the bestperformance. The result justifies the advantage of leveraginggroup information of social users and extracting fine-grainedrepresentation of topic variation patterns.

K=50 K=100 K=150Number of Topics

4200

4400

4600

4800

5000

5200

5400

Perp

lexit

y

TOTEUTB

GT,G=150GT,G=100

GT,G=50

Figure 4: Topic extraction performance of different methods.

Temporal Dynamic ModelingWe then evaluate the model’s capacity of capturing temporaldynamics by measuring the time stamp likelihood of a held-out test set, i.e., computing the likelihood of time stamps ofpreviously unseen tweets based on their content. Specifical-ly, GrosToT has the time stamp likelihood of a tweet di as:

Ldi =∑g

πuig

∑k

θgkηkgti∏j

φkwdij.

A higher likelihood value indicates better generalization per-formance.

K=50 K=100 K=150Number of Topics

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Negati

ve L

og-l

ikelih

ood

1e 8

EUTBGT,G=150

GT,G=100 GT,G=50

Figure 5: The time stamp likelihood of different methods.

We compare GrosToT with EUTB which has been shownbetter performance over TOT (Yin et al. 2013). Figure 5shows the negative log-likelihood values for the competi-tors with varying numbers of topics and groups. We find thatGrosToT consistently outperforms the baselines. The rea-son is that our model incorporates diversity of different usergroups’ temporal patterns. In contrast, the baseline methodssimply aggregate all temporal actions to infer topics’ globaltrends. Such coarse-grained extraction fails to gain accuraterepresentation of dynamics. It is also notable that the perfor-mance of these methods remains nearly stable as K and Gvary.

Group and Dynamics ExplorationWith the help of GrosToT, we analyze group characteris-tics and topic dynamics. For clarity we fix K = 100 andG = 100 in the following study. We present illustrative casestudies to show the advantage of in-depth exploration. Be-sides, we also quantitatively report their correlations to re-veal the interplay between groups and dynamics.

Group and Dynamics CasesTo identify breaking events related to a certain topic, e.g.,“Sports”, one would intuitively focus on the groups whosemajor interest lies in this topic. The following results, how-ever, suggest that analyzing groups that are not regularlyconcerned with the particular topic may provide an easierway.

Figure 6 shows the topic distributions of two extractedgroups, labeled as Group-A and Group-B, respectively. Wefind that the major theme inside each group has a clear top-ic focus. Members of Group-A mainly concentrate on topic“Sports”. In contrast, though topic “Movie” dominates theactivity of Group-B, the members also pay attentions to var-ious other topics.

Figures 7 (a) and (b) demonstrate the temporal distribu-tions of topic “Sports” within Group-A and Group-B respec-tively. From Figure 7 (b) we can easily identify four burstperiods. For example, the burst at time stamp Jan 26, 2013coincides with the Australian Open Final which the famousChinese professional tennis player Na Li attended. On thecontrary, the timeline in Figure 7 (a) is much smoother andwithout clear bursts.

"Sports": 94% football, fan, season, basketball, player

"Music": 3.1% album, Jay Zhou, fan club, song, idol

"Car": 2.1% vehicle model, engine, Audi, Volkswagen AG, sports car

(a)

"Movie": 63% box office, film, animation, documentary film, 3D

"TV serials": 11% tv serials, scriptwriter, theatre, role, plot

"The Film West Odyssey": 6.7% West Odyssey, Stephen Chow (director), Monkey King, Tang Monk, comedy

"Sports": 0.6% football, fan, season, basketball, player

(b)Figure 6: Topic distributions of (a) Group-A and (b) Group-B. For each topic, top 5 words w.r.t. its word distribution arelisted; we manually label topic using a concise phrase; thepercentage denotes the proportion of the topic in the group’stopic distribution.

0

0.01

0.02

0.03

12/01/2012 12/31/2012 01/30/2013

Pop

ula

rity

Time

(a)

0

0.06

0.12

0.18

12/01/2012 12/31/2012 01/30/2013

Pop

ula

rity

Time

Lionel Messi's record-breaking 86 soccer goals 12/10/2012

Lionel Messi wons his fourth successive Ballon d'Or award 01/08/2013

Chinese professional tennis player Na Li played Australian Open Final 01/26/2013

2013 NBA All-Star Game in Houston 02/18/2013

(b)

Figure 7: Temporal dynamics of topic “Sports” within (a)Group-A (b) Group-B. Group-A bears a probability of 94%on topic “Sports”, while Group-B bears a probability of0.6% on topic “Sports”. Breaking events can be easily iden-tified from (b).

By checking the posts in each group, we find that Group-A does talk about the particular events detected by Fig-ure 7 (b) when they happened. However, these bursty behav-iors are concealed since members keep talking about sportsin their daily life, rendering the events hard to be identifiedby the analysis of topic popularity variation. On the otherhand, members of Group-B generate sports-related tweet-s only when significant sports events happened. Thereforea spike in its timeline typically corresponds to some burstyevent in real life.

Group Behavior and Temporal TopicsWe next focus on the interplay between group interest andfluctuation of topic popularity within groups. Such a newanalysis can be valuable in understanding user’s interest andbehavior patterns in response to topics.

Group Interest and Fluctuation of Topic PopularityGroup interest is modeled as a distribution over topics, i.e.,θ. To quantitatively measure the fluctuation of topic popular-ity within groups, especially how intensive topics burst overtime, we define the burst degree of topic temporal distribu-tion by exploiting sliding window.

Specifically, we assume that the popularity of a stable top-ic in a sliding window can be modeled by a Gaussian distri-bution G(µ, σ2). For a topic k within a group g, we thencompare its real popularity at time t, i.e., ηkgt with slidingwindow containing its recent history periods [t − n, t − 1],and define topic burst degree at t as:

burst degree(k, g, t) =ηkgt − µt

σt,

where µt and σt are mean and standard deviation estimatedby the popularity in the recent history window of t:

µt =1

n

t−1∑i=t−n

ηkgt,

σt =

√√√√ 1

n

t−1∑i=t−n

(ηkgt − µt)2.

We finally compute the sum over all the time stamps, i.e.,∑t burst degree(k, g, t) as the aggregate burst degree of

the temporal distribution of topic k within group g.

Correlation between Groups and Temporal Topics Fig-ure 8 displays the correlation between group interest andburst degree of topic temporal distributions. We can clear-ly conclude that topics tend to exhibit higher burst degreewithin those groups which bear topic probability between0.001% to 10%; while the average burst degree is near ze-ro if topic probability is smaller than 0.001% or larger than10%.

This result is reasonable, and also revealed by the previ-ous examples shown in Figure 7. For medium-interested top-ics (i.e. with probabilities between 0.001% to 10%), groupusers are not concerned with them in the daily life, buttheir attentions would still be greatly drawn when signifi-cant events happen. This event-driven attention leads to the

0

2E+15

4E+15

6E+15

8E+15

Bu

rst

Deg

ree

Group Topic Probability

Figure 8: Correlation between group topic probability θgkand burst degree of group-specific topic temporal distribu-tion ηkg . E.g., Topics within those groups which have top-ic probability between 1.0E-04 to 1.0E-03 have an averageburst degree of 6.0E15.

bursty temporal patterns. In contrast, members in groupswith extremely high preferences for the particular topic keeptalking about it throughout the time period. This interest-driven behaviors result in stable temporal distributions with-out clear bursts. (Note that for topics with extremely lowprobabilities (< 0.001%), the results would largely be toonoisy regardless of the large data set, thus we only focus onthe previous two categories of topics.)

The above exploration clearly reveals that the different be-havior patterns of user groups response to varying kinds oftopics. This research enables fine-grained insights into thecharacteristics of topic temporal patterns and at the sametime provide practical guidance for multiple areas such asonline marketing and content management.

ConclusionIn this paper, we have addressed the problem of group ori-ented temporal dynamics extraction. We discussed the draw-backs of current global temporal extraction methods and p-resented a unified probabilistic generative model, i.e., Gros-ToT (Group Specific Topics-over-Time). GrosToT can si-multaneously uncover latent user groups and temporal topicsand extract group-specific topic temporal variation.

We demonstrated that GrosToT shows advantage not onlyin temporal dynamics modeling but also group content ex-ploration. GrosToT gains significant performance improve-ment over state-of-the-arts methods in modeling temporaldynamics. Novel findings in varying groups and rich dynam-ics were also provided.

This study shows that GrosToT can improve temporal top-ic extraction and user group modeling at the same time.The intuitive result opens interesting directions in the futurework. Probably the most exciting one is the integration oftheme spectrum of temporal evolution and group granularityfor comprehensive temporal modeling.

AcknowledgmentsThis research is supported by the National Natural ScienceFoundation of China under Grant No. 61272155, and 973program under No. 2014CB340405.

ReferencesAiroldi, E. M.; Blei, D. M.; Fienberg, S. E.; and Xing, E. P.2008. Mixed membership stochastic blockmodels. JMLR9:1981–2014.Blei, D. M., and Lafferty, J. D. 2006. Dynamic topic models.In Proc. of ICML, 113–120.Blei, D. M.; Ng, A. Y.; and Jordan, M. I. 2003. Latentdirichlet allocation. JMLR 3:993–1022.Danescu-Niculescu-Mizil, C.; West, R.; Jurafsky, D.;Leskovec, J.; and Potts, C. 2013. No country for old mem-bers: User lifecycle and linguistic change in online commu-nities. In Proc. of WWW, 307–318.Diao, Q.; Jiang, J.; Zhu, F.; and Lim, E.-P. 2012. Findingbursty topics from microblogs. In Proc. of ACL, 536–544.Griffiths, T. L., and Steyvers, M. 2004. Finding scientifictopics. PNAS 101(Suppl 1):5228–5235.Hong, L.; Yin, D.; Guo, J.; and Davison, B. D. 2011. Track-ing trends: incorporating term volume into temporal topicmodels. In Proc. of KDD, 484–492.Leskovec, J.; Backstrom, L.; and Kleinberg, J. 2009. Meme-tracking and the dynamics of the news cycle. In Proc. ofKDD, 497–506. ACM.Lin, Y.-R.; Margolin, D.; Keegan, B.; and Lazer, D. 2013.Voices of victory: a computational focus group frameworkfor tracking opinion shift in real time. In Proc. of WWW,737–748.Matsubara, Y.; Sakurai, Y.; Prakash, B. A.; Li, L.; andFaloutsos, C. 2012. Rise and fall patterns of informationdiffusion: model and implications. In Proc. of KDD, 6–14.Ren, L.; Dunson, D. B.; and Carin, L. 2008. The dynamichierarchical dirichlet process. In Proc. of ICML, 824–831.Wang, X., and McCallum, A. 2006. Topics over time: a non-markov continuous-time model of topical trends. In Proc. ofKDD, 424–433.Xie, J.; Kelley, S.; and Szymanski, B. K. 2013. Overlappingcommunity detection in networks: The state-of-the-art andcomparative study. ACM Comput. Surv. 45(4):43:1–43:35.Yang, J., and Leskovec, J. 2011. Patterns of temporal varia-tion in online media. In Proc. of WSDM, 177–186.Yin, H.; Cui, B.; Lu, H.; Huang, Y.; and Yao, J. 2013. Aunified model for stable and temporal topic detection fromsocial media data. In Proc. of ICDE, 661–672.Zhang, J.; Song, Y.; Zhang, C.; and Liu, S. 2010. Evolution-ary hierarchical dirichlet processes for multiple correlatedtime-varying corpora. In Proc. of KDD, 1079–1088.Zhang, W.; Wang, J.; and Feng, W. 2013. Combining latentfactor model with location features for event-based grouprecommendation. In Proc. of KDD, 910–918.Zhao, W. X.; Jiang, J.; Weng, J.; He, J.; Lim, E.-P.; Yan, H.;and Li, X. 2011. Comparing twitter and traditional mediausing topic models. In Proc. of ECIR, 338–349.