3D TECHNIQUES FOR VISUALIZING USER ACTIVITIES ON MICROBLOGS

Masahiko Itoh

Institute of Industrial Science, The University of Tokyo, JapanEmail: imash@tkl.iis.u-tokyo.ac.jp

ABSTRACT

Microblogging platforms such asTwitter have re-cently attracted attention and gained popularity. Byusing them, people can easily write their opinions andcomments on events in the real world in real time.Content on the timeline ofTwitter immediately re-flects real and virtual activities. The purpose of thispaper is to provide 3D components and methods ofvisualization that enable us to visualize user activitiesand the roles they play in their networks, i.e., whenand how much theytweet, how and when informa-tion spreads in their networks, or what kinds of topicsthey tweet in each community. This paper presents3D interactive components for visualizing relationsbetween users, their messages, and changes in thenumber of messages over time. Moreover, we explainmethods of comparing variations caused by differenttiming and by multiple groups of users.

1. INTRODUCTION

Microblogging platforms have recently attracted at-tention and gained in popularity.Twitter 1 is one ofthe biggest microblogging platforms, which has over75 million user accounts2, and its users have spreadall over the world (Java et al. (11)). By usingTwitter,people can easily write their opinions and commentson events in the real world in real time. Content onthe timeline ofTwitter immediately reflects both realand virtual activities.

Twitter enables users to write and read text messagescalled tweetsthat are up to 140 characters long, aredisplayed on user home pages, and are also shown ontheir followers’ home pages. They can read thetweetsof their followings and alsoreply to such tweets.Moreover, users can selecttweetsby others and re-transmit them, calledretweets. We can obtain thesedata through thetwitter public API in XML format.

Tweetingand retweetingsometimes cause informa-tion to spread such as that on restaurants or newproducts by word-of-mouth (WOM) communication.Therefore,Twitter has attracted attention as a tool

1 http://twitter.com/2 http://themetricsystem.rjmetrics.com/2010/01/26/new-

data-on-twitters-users-and-engagement/

for WOM. Gladwell’s ”The Tipping Point” discussesthree types of key people for WOM such as connec-tors, mavens, and salesmen (Gladwell (6)). Informa-tion on Twitter is occasionally spread by these kindsof key people. It is important to analyze the con-nection between users and message flows onTwitterto consider methodologies for advertising and propa-ganda using WOM on the Internet. For example, wemay effectively bring information to the attention ofothers by finding WOM influencers on the Internet,and then by beingfollowedandretweetedby them.

The purpose of this paper is to provide 3D compo-nents and methods of visualization that enable us tovisualize user activities and the roles they play in theirnetworks, when and how much theytweet, how andwhen information spreads in their networks, or whatkinds of topics theytweetin each community.

Visualizing the time-series of data allows us to answerseven important questions (i) what kinds of elementsappear at specific times, (ii) when do such elementsappear and disappear, (iii) how long do they exist ona timeline, (iv) how rapidly do they change, (v) howoften do they appear, (vi) what kind of order do dataelements appear in, and (vii) which elements appeartogether? (Muller and Schumann (15)). Being ableto visualize the time series of data content onTwitterenables us to display changes in thought and activitiesthat occur in the real and/or virtual world, and allowsus to analyze social phenomena.

This paper proposes five 3D-interactive componentsfor the visualization of relations between users, theirmessages, and changes in the numbers of messagesover time: (i)TimeSlicesfor visualizing user networkswith the time-series of data, (ii)TimeSkewersfor vi-sualizing the content of each user such astweetsona timeline, (iii) TimeFluxesfor visualizing changesin the amount of information such as the number oftweetsfor each user at each timing, (iv)Message-Trails for visualizing message flows caused byreply-ing andretweeting, and (v)TimeCloudfor visualizingthe global tendency of user activities on the timeline.These elements are introduced in Section 3.

Moreover, we provide methods of comparing multi-ple variations caused by different timing and multiple-user groups. To compare different timing, our sys-

tem provides functions for adding multipleTimeSliceswith different time stamps, and it providesparallelviewsandoverlay viewsin a 3D environment. Theyenable us to seamlessly change these viewing modeswithout losing user cognition. To compare differ-ent communities, our system provides functions foradding multiple layers representing different groupsof users, and also provides different viewing modessuch as anaggregate view, apile view, or asplit viewto compare different layers that sometimes have over-lapping nodes, and that change differently along atimeline. These methods are introduced in Section 4.

Our approach provides 3D interactive environmentsand visualization techniques using multiple 2Dplanes, lines, and plots in 3D space, which enableusers to dynamically and simultaneously visualizeuser networks, changes in their attribute values, mes-sages and message flows, and the global distributionof their activities. Our techniques enable us to observeoverviews and their details from global and local as-pects by seamlessly switching them. Moreover, com-binations of our visualization techniques allow us todisplay the relationships between interactions amongusers and statistical changes in trends in 3D space atthe same time.

2. RELATED WORK

Four main methods have been proposed to enable theevolution of information structures to become visual-ized; however, these have their own pros and cons.The first is (a) usinganimation to dynamically dis-play changes in structures (Toyoda and Kitsuregawa(23), Nakazono et al. (16)). Although this enablesusers to dynamically observe changes in structures,it reduces user recognition, because they lose the con-text in previous situations. Users occasionally misswhere changes have occurred and when they havechanged throughout the entire space. The second is(b) mapping atimelineto one of the axes in a 3D en-vironment (H.Chi et al. (9)). This enables users toobserve global differences between multiple graphs.However, it is difficult to check local differences indetail. The third is (c) using multipletiled viewstodisplay multiple Web graphs (Toyoda and Kitsure-gawa (23), H.Chi and K.Card (8)). Users can comparethe differences between Web graphs in parallel, en-abling them to comprehend global differences. How-ever, it is difficult to intuitively understand time in-tervals between Web graphs. Users occasionally can-not determine how long it has taken for changes tohave occurred. The fourth is (d)overlayinggraphs fordifferent time periods on one view (Nakazono et al.(16), Brandes and Corman (1)). This is advantageousfor comparing graphs in detail; however, is difficult todisplay global changes in structures.

LifeLines (Plaisant et al. (20)) andTimeMachine(Rekimoto (21)) enable users to display recordedevents in personal histories on timelines.VisuaLinda[12], andTimeTunnel(Notsu et al. (18)) use the com-bination of 3D space and a timeline to visualize thetime series of events or attribute values. They effec-tively use 3D spaces to simultaneously represent twokinds of relations including time relations to avoiddisrupting user cognition caused by them having toreconstruct their mental models.

Minard’s famous chart for showing the terrible fateof Napoleon’s army in Russia is a classical exam-ple of mapping changes in values over time (Tufte(24)). ThemaRiver(Havre et al. (7)) andWormplots(Matthews and Roze (14)) provide methods of vi-sualizing changes in values of multiple attributes in2D or 3D spaces. Dwyer and Eades (2) visualizesa flow graph, and simultaneously demonstrated time-dependent changes in the values of elements on a flowgraph using 3D space.

There has been research on visualizing both time-dependent changes in elements in a graph and mes-sage flows in it (Koike et al. (13), Dwyer and Eades(2)).

Moving Phenomenon(Kim et al. (12)) visualize lev-els of activities or the distribution of moving objectsusing scatter plots in 3D environments. They enableus to display time-dependent changes in distributionfrom a global viewpoint.

Many systems for different data domains use a 2.5-D representation to visualize multiple situations in a3D environment. The 2.5-D representation is used forthree kinds of visualizations that involve: (i) visual-izing different content (Fung et al. (5), Erten et al.(3)), (ii) visualizing time sequential changes (H.Chiet al. (9), Brandes and Corman (1), Nocke et al. (17)),and (iii) using different visual representations and/ormodels (Shen et al. (22)). Our framework supportsthe functions for (i) and (ii); however, the function for(iii) is still not supported, and we intend to explorethis in future work.

Techniques for coordinated multiple visualizationssuch as linked views of a histogram or aThemaRiverand multiple 3D graphs also enable us to observetime-sequential changes in graphs. However, it isdifficult to freely add arbitrary numbers of graphs.Moreover, multiple tiled views require adequatelysized display areas.

3. VISUALIZATION OF USER ACTIVITIES

This section presents 3D techniques for visualizinguser networks,tweetsincluding repliesandretweets,

flows of replies and retweets, and changes in thestrength and distribution of user activities. We utilizetheIntelligentBoxsystem (Okada and Tanaka (19)) asthe platform to implement these. This is a component-based visual software development system for inter-active 3D graphics applications.

3.1. TimeSlices

A TimeSlice(Itoh et al. (10)) is a plane for visualizinga user graph arranged on a timeline, which is one axisin 3D space. It represents a snapshot of the states ofuser networks with specific timing (Figure 1).

Timeline

TimeSlice

TimeSkewer

Figure 1: TimeSliceandTimeSkewer

(a) Image Nodes (b) 2D texture label Nodes (c) Sphere and Label Nodes

(e) Line Edges(e) Solid Tube Edges(d) Cone Edges

Figure 2: Representations of nodes and edges

A graph on theTimeSliceconsists offollowers andthe followingsof a specified user. To display these,we first input atwitter user name or id, and selectwhether we only obtainfollowers or followings, orboth of these. We can also obtain friends of friendsby inputting the depth level, or by selecting the visu-alized nodes and expanding them.

To visualize connections between users, we have

adopted automatic and dynamic graphlayout algo-rithms to visualize graphs based on a kind of force-directed model (Fruchterman and Reingold (4)). Wecan select and move nodes and edges to interactivelycheck details on relationship such as the degrees ofseparation between nodes. Moreover, we can zoomand pan a canvas to interactively change the focusingpoint in very large graph spaces.

TimeSlicescan adopt various kinds of representationsfor nodes and edges. We provide images, 2D texturelabels, and spheres and labels for the nodes describedin Figures 2 (a-c), as well as cones, solid tubes, andlines for the edges described in Figures 2 (d-f). Thedirections of edges to distinguish between followersand followings are represented by using colors and/oracute shapes.

This also enables us to filter visible nodes accordingto the number of in- and/or out-links, or their ratiosso that we only focus on hub and/or authority nodes.Such filtering functions enable us to find key people,and spam or bot user accounts.

TimeSlicescan be dragged and we can seamlesslychange their positions along a timeline. Such ma-nipulations generate an animated time sequence ofchanges in the graph and content on it (Figure 1). ATimeslicedisplays the tweets of all users with selectedtiming. The number of actions such as#tweet, #reply,#replied, #retweet, or #retweetedaround the selectedtiming is mapped to the size, color, or transparencyof nodes and edges. Such functions enable us to dis-play which users are active or have strong connectionswith others in a particular timing.

We can interactively add newTimeSlicesalong atimeline, and generate multiple views for visualizinggraphs with different timing in the 3D environment tocompare time-dependant differences as described inFigure 4 (a). To avoid different layouts on the samenodes belonging to differentTimeSlicesand to avoiddrastic movements by nodes in the animation, the po-sitions of nodes on differentTimeSlicescan be com-pletely synchronized with one another even if usersdrag a node. Panning and zooming manipulations arealso propagated to otherTimeSlices.

The system visualizes a histogram on the timelinerepresenting the number oftweets, replies, and/orretweetsat each timing (Figure 1). We can dynami-cally change the scope of the visualized range of timealong the timeline by zooming or panning it.

3.2. TimeSkewers

A TimeSkeweris a line for visualizing events suchas tweets, replies, and retweetson a selected user,

as shown in Figure 1. Moreover, theTimeSkewercan only displaytweetswith specified keywords, orreplies or retweetsfor selected persons. By usingthese functions, we can observe when people talkedabout certain topics and who they were.

3.3. TimeFluxes

A TimeFluxis a line of spheres or solid tubes for vi-sualizing the changes in the number of activities suchas tweets, replies, and retweetsabout selected userswithin a given period of time, e.g. one day or threehours, as shown in Figure 3 (a). We can map#tweet,#reply, #replied, #retweet, and#retweetedto the radii,colors, and transparencies of spheres or tubes. Wecan add two or moreTimeFluxesto one person to dis-play multiple attribute values, or different keywords.In the case of using#reply, #replied, #retweet, and#retweeted, we can select target users to communi-cate with. By using these functions, we can observewhen and how much a person has communicated withparticular people on specific topics.

3.4. MessageTrails

MessageTrailsform a flow graph for visualizing mes-sage flows caused byreplying and retweeting, asshown in Figure 3 (b). We can select target users andsource users toreply and retweetwith, and can se-lect particular messages to find the path the messageshave spread along. We can also specify keywords tofilter MessageTrails. They can have different colorsdepending on the types of messages such asreply orretweet, target or source users, or selected messages.The directions of the arrows are represented by usingthe same method as that in Figures 2 (d-f). Option-ally, a MessageTrailcan display messages ofreplies,retweets, targets ofreplies, sources ofretweets, andtheir time stamps. By using these functions, we canobserve how specified topics have spread, and howlong they have continued.

3.5. TimeCloud

A TimeCloudis a 3D scatter plot mapped on user net-works and a timeline for visualizing global tendencyon user activities such astweets, replies, andretweets,as shown in Figure 3 (c). Each plot can change colorand transparency according to the kinds of messages,or numbers of messages within a given period of time,e.g., one day or three hours. We can use filters for key-words. By using these functions, we can observe thesizes and positions of user groups that have had inter-est in the keywords, and when the topic has attractedattention.

4. METHODS OF COMPARISON

Comparing situations with different timing and dif-ferent user groups is an important task for observ-ing changes in social phenomena in detail. We havetaken into consideration two kinds of relations, suchas inter-time relations and inter-community relations,and provide methods for comparing them.

4.1. Inter-time relations

Inter-time relations are visualized by multipleTimeS-lices in different positions on the timeline. Our sys-tem allows users to add and compare multipleTimeS-lices. It enables us to explore changes in graphs byusing animation, and by comparing multipleTimeS-lices as described in Figure 4 (a). Our system alsoprovidesoverlay viewsandparallel viewsto compareTimeSlicesin detail in 3D space (Figures 4 (b and c)).These kinds of views enable us to interactively ex-plore information through different perspectives.

An overlay viewis represented by changing eye posi-tions, and by changing projection modes in a 3D en-vironment. We normally use perspective projection in3D environments. The same nodes in differentTimeS-licesare then displayed in different positions becauseof perspective. To solve such problems, we preparedan orthogonal projection mode, where the same nodesin different Timeslicescompletely overlapped posi-tions with one another as can be seen in Figure 4 (b).We also provided a function to change the transparen-cies ofTimeSlicesto avoid background ones from be-ing hidden.

Our framework enables us to seamlessly change anormal view to aparallel view. To achieve this, thesystem can automatically slideTimeSlices. After that,users can obtain aparallel viewby changing eye posi-tions and projection modes, in the same way as that intheoverlay viewin 3D space, as can be seen in Figure4 (c).

4.2. Inter-community relations

Inter-community relations are visualized by usingmultipleTimeSliceson different communities that aregraphs generated from different groups of users.

Our framework provides three types of views to com-pare these, which are similar to the ideas introducedby Fung (Fung et al. (5)) and Erten (Erten et al. (3)),i.e., (a)aggregate, (b) pile, and (c)split views. Anaggregate viewvisualizes two communities in oneTimeSlice(Figure 5 (a)), where edges in differentcommunities are in different colors. Apile viewvi-sualizes two communities in different stackedTimeS-lices (Figure 5 (b)), where common nodes in differ-

(a) TimeFluxes (b) MessageTrails (c) TimeCloud

Figure 3: TimeFluxes, MessageTrails, andTimeCloud

(c) Parallel View

(b) Overlay View(a) Normal View

TimeSlice A

TimeSlice B TimeSlice C

TimeSlice A TimeSlice B TimeSlice C

Figure 4: Comparison of multipleTimeSlices

ent communities have red edges, as seen in Figure 5(b). A split viewvisualizes two communities in dif-ferentTimeSlicesside-by-side (Figure 5 (c)). Inpileandsplit views, the positions of twoTimeSlicesalongthe timeline are synchronized with one another. Userscan also add sets ofTimeSlicesto the timeline.

Several methods of drawing two or three overlappinggraphs have been introduced (Fung et al. (5), Ertenet al. (3)). We provide three types of methods for lay-outs: (a)merge, (b) pivot, and (c)independentlay-outs. We treat the same nodes in different communi-ties as one node in themerge layoutmode, and createa union of nodes in different communities. We thencalculate their layouts (Figure 5 (a)). As two graphsin this mode are treated like one graph, nodes in theresults never overlap. This is advantageous for explor-ing the relationships between nodes in two groups.However, it needs a large space to visualize the re-sults. We treat the same nodes as one node in thepivot layoutmode, and independently calculate thelayouts of nodes for two communities (Figure 5 (b)).The shared nodes are treated like pins in the results ofthis method, and the others are spread around thesepins. The independent layoutmode independently

calculates the layouts of all graphs for communities(Figure 5 (c)). Therefore, the visualized results withthis method appear compact. This is advantageousfor independently exploring changes in each commu-nity. However, if we use this method inaggregateview or pile view, nodes and edges belonging to dif-ferent communities can easily overlap. As the samenodes in different graphs are treated independently inthis layout mode, it is difficult to identify which nodesare shared in both communities. To avoid this situa-tion, the system can add red edges between the samenodes in the same way as seen in Figure 5 (b).

Our framework visualizes inter-community relationsusing a combination of theviewandlayouttypes.

(b) Pile view

with pivot layout mode

(a) Aggregate view

with merge layout mode

(c) Split view

with independent

layout mode

Community BCommunity A

same nodes

Community B

Community A

Community B

Community A

Common nodes

Figure 5: Visualization of multiple communities.

5. CONCLUSIONS

We have proposed 3D visualization techniques. Theyenables us to interactively explore user activities in-cluding their relationships, communications with oth-ers, transitions in the volume of sent messages, andmessage flows onTwitter. These combinations en-abled us to observe user relationships, communica-tions, and statistical changes in trends in 3D space.

In this work, we only focused on a visualizationmethod of structural analysis. However, a combina-tion of structural analysis and content-based analysismethods is required for analyzing social connectionsin detail. We intend to provide methods of analyzingand visualizing the content of messages.

6. REFERENCES

[1] U. Brandes and S. R. Corman. Visual Unrolling ofNetwork Evolution and the Analysis of Dynamic Discourse.Information Visualization, 2(1):40–50, 2003. ISSN 1473-8716.

[2] T. Dwyer and P. Eades. Visualising a Fund ManagerFlow Graph with Columns and Worms. InProceedings ofthe Sixth International Conference on Information Visuali-sation, pages 147–152, 2002.

[3] C. Erten, S. G.Kobourov, V. Le, and A. Navabi. Si-multaneous Graph Drawing: Layout Algorithms and Vi-sualization Schemes. InThe 11th Symposium on GraphDrawing, pages 437–449, 2003.

[4] T. M. J. Fruchterman and E. M. Reingold. Graphdrawing by force-directed placement.Software Practiceand Experience, 21(11):1129–1164, 1991. ISSN 0038-0644.

[5] D. C. Y. Fung, S.-H. Hong, D. Koschutzki,F. Schreiber, and K. Xu. Visual Analysis of OverlappingBiological Networks. InProceedings of the 2009 13thInternational Conference Information Visualisation, pages337–342, 2009.

[6] M. Gladwell. The Tipping Point: How Little ThingsCan Make a Big Difference. Little Brown, Boston, MA,USA, 2000.

[7] S. Havre, E. Hetzler, P. Whitney, and L. Nowell.ThemeRiver: Visualizing Thematic Changes in Large Doc-ument Collections.IEEE Transactions on Visualization andComputer Graphics, 8(1):9–20, 2002. ISSN 1077-2626.

[8] E. H.Chi and S. K.Card. Sensemaking of EvolvingWeb Sites Using Visualization Spreadsheets. InProceed-ings of the 1999 IEEE Symposium on Information Visual-ization, pages 18–25, 1999.

[9] E. H.Chi, J. Pitkow, J. Mackinlay, P. Pirolli, R. Goss-weiler, and S. K.Card. Visualizing the evolution of Webecologies. InProceedings of the SIGCHI conference onHuman factors in computing systems, pages 400–407, 1998.

[10] M. Itoh, M. Toyoda, and M. Kitsuregawa. AnInteractive Framework for Visualizing Time-series of WebGraphs in a 3D Environment. InProceedings of the 14th In-ternational Conference on Information Visualization, 2010.

[11] A. Java, X. Song, T. Finin, and B. Tseng. Why WeTwitter: Understanding Microblogging Usage and Commu-nities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social NetworkAnalysis, pages 56–65, 2007.

[12] K.-S. Kim, K. Zettsu, Y. Kidawara, and Y. Kiyoki.Moving Phenomenon: Aggregation and Analysis ofGeotime-Tagged Contents on the Web. InProceedings ofthe 9th International Symposium on Web and Wireless Ge-ographical Information Systems, pages 7–24, 2009.

[13] H. Koike, T. Takada, and T. Masui. VisuaLinda:A Framework for Visualizing Parallel Linda Programs. InProceedings of the 1997 IEEE Symposium on Visual Lan-guages, pages 174–182, 1997.

[14] G. Matthews and M. Roze. Worm Plots.IEEEComputer Graphics and Applications, 17(6):17–20, 1997.ISSN 0272-1716.

[15] W. Muller and H. Schumann. Visualization Meth-ods for Time-dependent Data - An Overview. InProceed-ings of the 35th conference on Winter simulation, pages737–745, 2003.

[16] N. Nakazono, K. Misue, and J. Tanaka. NeL2: Net-work Drawing Tool for Handling Layered Structured Net-work Diagram. InProceedings of the 2006 Asia-PacificSymposium on Information Visualisation, pages 109–115,2006.

[17] T. Nocke, M. Flechsig, and U. Bohm. Visual Explo-ration and Evaluation of Climate-Related Simulation Data.In Proceedings of the 39th conference on Winter simulation,pages 703–711, 2007.

[18] H. Notsu, Y. Okada, M. Akaishi, and K. Niijima.Time-Tunnel: Visual Analysis Tool for Time-Series Numer-ical Data and Its Extension toward Parallel Coordinates. InProceedings of the International Conference on ComputerGraphics, Imaging and Visualization, pages 167–172, 2005.

[19] Y. Okada and Y. Tanaka. IntelligentBox: A Con-structive Visual Software Development System for Interac-tive 3D Graphic Applications. InProceedings of ComputerAnimation ’95, pages 114–125, 1995.

[20] C. Plaisant, B. Milash, A. Rose, S. Widoff, andB. Shneiderman. LifeLines: Visualizing Personal Histo-ries. InProceedings of the SIGCHI conference on Humanfactors in computing systems, pages 221–227, 1996.

[21] J. Rekimoto. Time-Machine Computing: a Time-centric Approach for the Information Environment. InPro-ceedings of the 12th annual ACM symposium on User inter-face software and technology, pages 45–54, 1999.

[22] Z. Shen, M. Ogawa, S. T. Teoh, and K.-L. Ma. Bib-lioViz: a System for Visualizing Bibliography Information.In Proceedings of the 2006 Asia-Pacific Symposium on In-formation Visualisation, pages 93–102, 2006.

[23] M. Toyoda and M. Kitsuregawa. A System for Vi-sualizing and Analyzing the Evolution of the Web with aTime Series of Graphs. InProceedings of the SixteenthACM Conference on Hypertext and Hypermedia, pages151–160, 2005.

[24] E. R. Tufte. The Visual Display of QuantitativeInformation. Graphics Press, Cheshire, CT, USA, 1983.