ConnectDots: Visualizing Social Network Interaction for Improved Social Decision Making

1. ConnectDots: Visualizing Social Network Interaction for Improved Social Decision Making Deidra Morrison Northwestern University Evanston, IL USA

2. Presentation Outline • Decision Making • Sociality Models • Project Goals • ConnectDots Visualization • Future Work

3. Decisions, Choices, & Frames • There has been a history of investigation of decision making theory of individuals • Kahneman & Tyversky, 1979 • Prospect Theory: • Framing of choice can result in risk averse or risk seeking choice behavior even when calculated outcomes are better or worse for decision maker • Abelson & Levi, 1995 • Translation Effect: • Personal reframing of choice happens when prospects are framed as gains vs. losses • Typically research revolves around choices that result in gains and losses • Games of chance • Medical decisions • Monetary investments

4. Risk Seeking vs. Risk Averse Behavior • When being presented with choices frames that emphasize gains or losses • Risk aversion with emphasis on gains • Risk seeking with emphasis on losses • Social decisions lack the statistical values directly related to the gains or losses associated with a decision Value function is S Shaped. The difference in subjective value between gain decreases as values increase. The subjective loss difference decreases more rapidly as values increase.

5. Social Decision Making • Decision framing typically does not have numerical rates of gain/loss ratios: • Some previous work: • Beisswanger, A (2003) • Study on risk taking in relationship decisions • Participants asked to make decisions on social interaction choices for themselves and their close friends • Risks determined to be emotional factors such as rejection, unhappiness, dissatisfaction, relationship strain etc.

6. Relational Model Theory • A.P. Fisk (1991) • Developed 4 models of sociality that people use to govern the ways in which they interact with others • Communal Sharing (CS) • concept of affiliation with a group where the status of all members is equivalent. • Authority Ranking (AR) • Distinct understanding of hierarchy and the social responsibilities assigned to each level at which a person is considered • Equality Matching (EM) • egalitarian, even balanced interaction is expected. The order or frequency, of interactions, is not as important as the need to keep all interactions equal and fair • Market Pricing (MP) • There is an expectation of personal value being calculated and compared to others. Typically there is a desire to increase individual value relative to the value of the relationship partner.

7. Goal of the Project • Use abstract visualization to reframe data related to their social decision making practices in order to positively affect health of relationships. • Formulate a correlation between interaction frequencies and methods and the sociality models people use to govern their interaction patterns with others. • Investigate possible changes in overall network health as users become more aware of decision practices

8. Introduction to ConnectDots • Web application • Visualization rendered in Flash • Gathers details on the frequency of emails and instant messages (IM) sent and received by the user • Social network is derived from this information • user-centric view of their social network, with branches representing the connections that the user has with those within their network. • Gathers information from users on their perceptions of health of their relationships, and models of sociality they apply to each relationship • (A) Flash ActionScript that passes information between scripts that interface with database and generates visualization of information. • (B) Perl and PHP scripts that take in information from the user and store and retrieve data from database for visualization in flash. • (C) MySQL database system that stores user information and network data. • (D) Visualization pane where visualization is viewed and explored.

9. Data Collection • Perl-based application • retrieves email from inbox and IM logs • Extracts individual information about interaction (partner identity information, date/time of interaction,subject) • Calculates frequencies of interaction across network, with individuals, through different media • Stores data in MySQL database

10. Survey Data Collection • Relational Model Instrument (RMI) administered to the user for all members of their network that have been collected • RMI queries user over a number of interaction factors • Additional questions about the users perception of the health of each relationship is also asked. • Survey information is gathered and stored in the database for each contact • numerical value assigned to level of health and perceived models for relationships and are used in addition to frequency of interaction values to generate a health score for each relationship

11. Visualization Features • The network layout is a ‘tree’ form • Each branch represents user connection with each node in their network. • Length => interaction frequency • Width => health • Location on tree => sociality model most frequently assigned to interactions with the contact • Higher positions represent people whom you have dominance relationships with • Middle of tree represents those that you have affinity relationships with

12. Visual Features (continued) • For each branch there is a flower that represents the interaction history that the user has with the person represented over the different media types (i.e. email, IM, etc.) • Color => media type • Each petal in flower represents an instance of communication between user and contact. • Hue/Position => age As you travel counter-clockwise, you can see how the changes in the hue of the flower indicate the older to newer message petals represent older to newer messages

13. Future Work • incorporate more communication media types into the visualization • implementations of possible web-based logging and transfer software for mobile device communication records • automatic updates of data to the system • Measuring the effectiveness of this tool for decision support for social interaction practices • study of usability factors and issues with the interface, • Comparison study of the task of social decision making • ConnectDots vs. traditional methods

14. Conclusion • Through the presentation of this information, users will be able to view this large data set of interaction information more easily • Person can also become more aware of their current decision making practices for their social network, and observe how their relationships are affected. • Create the opportunity to engage in interventions to prevent relationship dissolution • Give better indicators to users of their methods for establishing and maintaining what they perceive as successful relationships. • This work will also contribute to current efforts to find means of evaluating information visualization systems for their effectiveness in comparison to currently used tools or decision making processes