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Learning Modules > Social Visualizations

Description | Usage Hints | Learning Task | Discussion | References | Acknowledgments


Social visualizations are a special type of information visualizations that focus on analysis of social behavior. For example, lifeline visualizations reveal migrations, transitions and trajectories of users or user groups.

Other research aims at the visualization of very large-scale conversations, such as those that take place on Usenet, or visualizations of Web activity or user trails.

Visualizing group behavior. Shown is the map of a virtual world with overlaid user trails. Members of one group share the same trail color. The trail of the team leader is slightly darker (Börner & Penumarthy, 2003).

Temporal-spatial distribution of attendees during the six hour virtual Conference on Learning in Three Dimensions, VLearn3D in December 2002 (Börner et al, 2004). Click images to enlarge.

The representation of people in text-based or graphical virtual worlds by avatars is yet another topic. Avatars are varied and range from purely textual descriptions over 2-D smiley faces, cartoon characters or photographic images, to abstract or highly realistic 3-D models.

Further, there is interest in visualizing and supporting social interactions in text-based or 2-D graphical systems. Chat Circles is a 2-D graphical interface for synchronous conversation. It visualizes the non-textual components of online chatting, such as pauses and turn-taking behavior, that can be key to fully understanding the nature of discussion and that are lost in regular chat log files. The conversational archive of Chat Circles can be visualized as a 2-D, interactive conversational landscape in which each vertical line shows the activity of one participant and the horizontal lines are postings. PeopleGarden uses a particularly apt flower metaphor to create individual data portraits of chat participants and a garden metaphor for combining these portraits to present the conversation activity of a group of participants. Work by Marc Smith and colleagues analyzed gestures and movement of users in VChat, a graphical chat system. They compared the average distance and orientation of users in relation to users targeted in their chat and randomly selected users. They concluded that people were standing closer to their chat target, but kept some distance from targeted users to maintain personal territories.

Another line of research focuses on mapping MUDs and 3-D virtual worlds. Martin Dodge’s Atlas of Cyberspaces section on MUDs & Virtual Worlds provides an excellent overview. Elaborated maps by Andrew Smith show the urban density and the teleport systems of his 3-D world. The AlphaWorld Mapper (http://mapper.activeworlds.com/aw/) by Greg Roelofs and Pieter van der Meulen provides access to a complete, zoomable 2-D map of a virtual world that is roughly the size of California (429,025 square km).

Maps help users to orient themselves in an environment; they also equip their users with survey knowledge that may be hard to acquire purely by navigation of the environment. Maps have been used to support users’ navigation in virtual environments (e.g. by allowing them to drag an icon of themselves to a desired new position) as well as in graphical multi-user domains (MUDs) consisting of spaces and landmarks.

Usage Hints

The Activeworlds Visualization Tool is a stand alone application that allows one to generate maps of 3D virtual worlds as well as record and visualize user behavior.

World Maps
Maps for Activeworlds worlds can be generated using the online Active World Mapper ( see its interface in the figure below). Simply enter basic information about your world, a world propdump file with information on the number and properties of the three dimensional objects that make up your world, an optional world registry file with information on the size of virtual objects and select 'map it'.

nw-random click to enlarge


User Tracking
To track the activities of users in virtual worlds generated with Activeworlds technology, one needs to set up a bot that logs all user activity. These bots are programs that place themselves (as avatars) at any place in the world (generally underground, so that they don't interfere with activities and experiments) and track the following user characteristics:

  • movement (x, y, z coordinates)
  • orientation (which direction they are facing)
  • actions (clicking - object ID, object location, talking - chat text)

A sample of the log file looks like this:

<timestamp>|<username>|<action>|<x>|<y>|<z>|<orientation>|<other action-specific info>
502002851|user1|Talking|7359|0|-5946|1400|"where are you going user2?"
502002905|user2|Talking|7359|0|-5946|936|"to the exit"
502002528|[OTAK]|Talking|8000|0|-5000|100|"« user3 JUST ENTERED THE WORLD »"

The last line above records the talk by the [OTAK] admin bot which sends out messages of who entered and left the world at what time. The second to last line reports a click event, including the location and ID of the object clicked. The location of the object is in terms of the cells in the world (2,-5).

It is important to know how big the worlds are because bots have a limited "hearing range". If the world is very large, then multiple bots are required to cover the entire area.

Learning Task

Generate a map for a virtual world. Install a bot and use the resulting user log data to map and analyze user positions and activities.

According to Erickson (2003), the design of representations of groups in online environments are enhanced if the visualizations

  • are identical for everyone - no customization.
  • portray actions, not their interpretation.
  • allow deception.
  • support micro/macro readings.
  • suggest rather than inform.
  • use a third-person point of view so that the users seet their own behavior reflected in it.

Analyze how well those claims are met by the visualizations shown above.


The high spatial and temporal resolution of data collected in virtual worlds as well as the completeness of coverage of world and participant logs enables the correlation and analysis of data which was previously impossible to analyze and correlate.

Given the increasing ease with which social activity can be monitored in both the real world and virtual space, care has to be taken to ensure that extensive user tracking and analysis benefits the users rather than invading their privacy. At any point in time, users need to be fully aware of what interaction data is being recorded, analyzed, and visualized.



This documentation was compiled by Katy Börner & Shashikant Penumarthy.

Information Visualization Cyberinfrastructure @ SLIS, Indiana University
Last Modified May 14, 2004