Stochastic Models of User-Contributory Web Sites
Interested in modeling how to people view and rate existing content. The talk is an extended example using Digg.
Votes on stories is a combination of visibility (do they see the story) and interest (do they like it, vote on it). In this experiement, they don’t have info on visibility so they need to model it.
Their model captures key Digg qualitative features: slow initial, fast growth as it gets more views.
A model for promotion of an article is created.
Stochastic process approach used to connect user and system behaviors. Applies to users with limited information and tasks
Personal Information Management vs. Resource Sharing: Towards a Model of Information Behavior in Social Tagging Systems
Why do people tag? Towards a model of tagging as info interaction behavior.
Is tagging a way to get around the vocabulary problem (different communities, different terms)
Emerging tag models for Language (Linguistic Tag model), function, tag-relationship. Found almost all tags relate to content, not time, task, emotion
TACS – web based tool for tag analysis
Used Amazon Mechanical Turk as a cheap way to get survey subjects, although there may be some problems (verification, biased population, platform)
Assume different motivations for tagging. Organizing your own content (PIM) vs Media and information sharing.
Designed a questionnaire of Delicious, Connotea, Flickr, YouTube users 7pm Likert scale
Qualitative analysis showed strong differences in motivation for using different sites.
Ease of tagging not significantly different. Tagging is useful (connotea users really think so).
Compares to Shneiderman 2002 Two dimensions of social interaction (activity vs. social sphere)
In terms of IR, people thought tags on flickr/youtube were more helpful than delicous/connotea. I’m surprised by that…I use tags on delicous to locate information all the time. For me, its one of the key features. When I asked the speaker, he said his qualitative/quantitative results had no indication of that type of behavior. I think that’s really interesting. Time for a paper?
Activity Types (Cool & Belkin 2002). May be applicable, but lacks a social dimension.
Motivation, Structure, and Tenure Factors that Impact Online Photo Sharing
Why do people in online communities share? Photos, info, meta-information, code. Want to quantify drivers for sharing and actual behavior. Can look at the area in terms of WHY people share, WHAT they share, and WHERE.
Note a difference between creating and sharing. They are separate, but many studies assume creation is coupled with sharing. Looked at Flickr data; combined survey data with system reported data.
Looks at 3 factors: Motivation (Intrinsic vs Extrinsic, Self vs. others)
Structure: Number of contacts
Tenure: Years since started sharing
Looked at artifact sharing per year tenure.
I wonder why they went shares/year, not per month. Seems like you could really see different outcomes for people that post habitually, vs people that share their one time trip.
Commitment, Number of contacts positively correlated with sharing. Personal Enjoyment is not correlated (maybe because people motivated by creating more than sharing). Self-development is negatively correlated with sharing (maybe because they are more interested in quality than quantity). Time since first upload strongly negatively correlated with sharing (the longer you’re with a community, the less likely you are to share). Maybe because of loss of interest.
Modeling Blog Dynamics
The blogosphere is a system of interactions of posts, topics, links, etc. The purpose here is to create a generative model of the blogosphere that matches properties of the real blogosphere for prediction and motivation.
Actually 2 networks combined into one: Blog network and post network.
Goal: Model micro-level interactions to create the macro-level patterns (structure, and dynamic over time) of the blogosphere.
Structure/Topological Patterns: Power Laws (interposting time)
Temporal/Dynamic Patterns: Burstiness and Self-similarity
Proposed Model: ZC
In every timestep, for every blog, assign a state as part of an FSM, depending on how likely they wil blog. If they blog, randomly decide if they will create a link to a neighbor or ‘random blog’.
This creates a post distribution, burstiness, post popularity similar to real blogosphere.