by Jeff Patton

Pragmatic personas can be fast, based on user research, but can also draw on your own experiences (according to Don Norman)

Identify User Types or Roles

• Different kinds of people are user types (student, professional)
• User Role describes the relationship a person has with your product (thing-doer, eg late night pizza buyer, daytime lunch seeker)

Profile User Types

Personify User Types to Create an Example User

Consider Product Design Impact

Mike Cohn – (slides)

Software requirements are a communication problem; balance is critical.  If business side dominates, functionality and dates mandated with little regard for constraints.  If tech dominates, we make them speak technical jargon and we lose the business need, drivers.

We cannot perfectly predict a software schedule.

So…we make decisions based on the information we have, but do it often

We spread decision-making across the project, rather than making one set of decisions…

Stories

Stories are (Three C’s, Ron Jeffries)

• Card (Note card) – Most visible part
• Conversation – Promise from dev team to product owner: “We will come talk to you before we start”
• Confirmation – Acceptance criteria

Short story about a system feature told from the perspective of a user.

As a …<user> I want to…<goal> so that…<reason>

Sometimes, you need more detail about a story.  You can:

• Create new, smaller, more specific stories.
• Define ‘conditions of satisfaction,’ which are really acceptance tests.  “What does the product owner need to see so that we can know this story is done?”  This basically becomes the ’script’ for the Sprint review.

User Roles

Broaden scope from looking at one user

• Allow users to vary by:
• What they use the software for
• Hwy they use software

This is different than a persona, where personas are about a specific user (based on research), designed to induce empathy in the design team.  User roles are more broad, describe types of users.

Can do user role brainstorming to identify different roles:

Thinking about your product, everyone writes a role on an index card.

• Brainstorming, no judgement on what the roles are
• Put related roles near each other
• Combine, consolidate, remove

Advantages of using roles

Avoid saying “The user”.

Can also have system and programmer users.  “As a payment verification system, I want all transactions to be well-formed XML…”

Stories, Themes, Epics

User Story – Description of desired functionality told from user perspective
Theme – Collection of related user stories
Epic – Large User Story

Themes, Epics don’t really imply size…Themes aren’t necessarily bigger or small than Epics.  They’re just labels.

This is the followon session to the previous Guerilla User Research session.  The theme here is taking the insights learned from User Research and turning them into actionable design.  The goal of this series of talks is to demo an application on Thursday this week.

Communicate Design

• Personas
• Workflow Stories / Scenarios
• Task Analysis Grids

Personas

• Quote
• Day-in-the-life story.  Contextual/specific to what the person is doing right now.
• Goals
• Pain Points/Frustration
• Opportunities
• Activities

Design tool

Storytelling

Way to build empathy on users

Based on user research

Todd talked about the DNA chart…

Workflow Stories

Scenarios are stories that happen in the day of a life of a key user.  A narriative form that tell a story of people using the system.  Tasks are the granular items, connected to how important it is to different users.  This can be used to prioritize possible features.

Empathy is one of the key points of why we do user research.  This is a bit of contention with the Agile methodology and ‘user stories.’  User stories may refer to ‘average user,’ which often means ‘me.’

Good to use multiple points in order to generate personas:

• Project Stakeholders
• Customer Representatives (Customer Service Reps)
• Actual Customers
• Someone you know

Task Analysis Grids

Work back and forth between personas and Task Analysis Grids.  The idea is to get a holistic view of what the system is supposed to do overall.  Then go back to figure out the order/priority in which features are built/released.

• Scenario Based
• Incorporate personas
• Prioritized

Persona Creation and Critique

We broke into several groups.  Each group created a persona based on the user research generated in the previous session.

Russ Unger, Todd Zaki Warfel

Traditional barriers to UX research: Time and Money; customer/stakeholder perceived value, and the attidude that “We don’t have time and money to do this.”

Argue, “We don’t have time and money to NOT do this…”  Spend a few hours doing some research, in order to allow you to make data driven design decisions

Each methods have strengths and weaknesses.  It’s good to combine multiple methods

The Burrito Lunch

• Send out an email, if you fit a profile, come do this, and we’ll give you lunch
• Chocolate snacks are a helpful way to get people to fill out surveys

Crowd Sourcing

• Using social media, other tools to get people to give feedback.  E.G.  Twitter, Facebook
• Coupled with web analytic data

Man on the Street

• Simply just going out and asking people, note trends.

User Research: “You never ask the question you really want answered.  If you ask the question you want answered, you’ll miss all kinds of rich information.”

User Research…one of the benefits of the agile UX methods is that you can bring prototypes to user research sessions: gives you access to users, do validation of current design, and research for future sessions.

Designing the Box

• Get people together, some Sharpies, paper, ask people to design the box the product, tool, service would come in.

If this was a COTS product, what are the key features that need to be front and center.  As a UX designer, gives you insight into the thought processes involved in prioritizing features, etc.

Guidelines for asking better research questions

• Provide context: “Did you have coffee yesterday?  How much coffee did you have yesterday?  Was that a normal day?  How about the day before that?”  It’s our job as researchers to ask questions and identify trends, amounts, etc, rather than asking them directly?
• Helpful to start with a most recent or most memorable experience.
• Start broad and open ended
• Funnel and narrow questions

People want to tell you about their lives.  If you can facilitate in a way that allows them to tell their own stories, people are willing to talk.  Another way to help people talk: “I’ll share a story about me, then you share a story about you.”

ICWSM Paper Titles – Wordle

Originally uploaded by fitzgeraldsteele

This tag cloud was generated from all the paper titles that were presented at the ICWSM ‘09 conference (http://www.icwsm.org). I don’t think anyone is surprised that ’social’ is the major term.

ICWSM Liveblog – Wordle

Originally uploaded by fitzgeraldsteele

This tag cloud was generated from my liveblog of the ICWSM conference (http://fitzgeraldsteele.wordpress.com/tag/icwsm). I think it is interesting that people shows up as the biggest term here, where it hardly registers in the paper titles.

Tagcloud generated by www.wordle.net

Intersection of news media, technology, and the political process.  Modern SM technology is a disruptive technology, similar to radio/TV in the 20th century.  How does information transmitted broadly by the media interact with the personal influence arising from social networks?

SM erases difference between global and local influence, making more of a continuum.  Speed of media reporting increasing, contributing to a 24 hour news cycle.  “A Challenge to healthy discourse.”  Online media also adds complexity to how political info flows through social networks.

The dynamics of the global news cycle

Examined if the ‘news cycle’ is a metaphorical construct, or is it visible in data.  If it’s visible, can we measure it, describe it?  Used data from Spinn3r, looked at 1M news articles and blog posts per day, 20K sources.

What basic “units” make up the news cycle?  Need some aggregate of articles, vary over the order of days, and handles half-terabyte of data.  Look for “memes”, identify text fragments, phrases, quotes that travel through many articles.  They create a weighted, directed, acyclic graph of mutational variants, that delentes min total edge weight such that each component has a single “sink” node.  This problem is NP-hard, but can apply heuristics based on selecting a single edge out of each quote.  Produces a neat stacked histogram graph that shows the relative frequencies of stories related to a particular quote over time.

Use some analogies to describe temporal variations: eg species competing for a resources in an eco system, or biological systems that synchronize to favor a small number of individuals at any point in time.  A model to describe this might include: imitation term, recency term.

Found a 2.5 hour gap between peak intensity of the story in mainstream media, vs when it peaked in the blogs.

Can also use the data to find stories where blogs lead the media.

The spread of political messages through social networks

Might look at Chain-letter petitions as ‘tracers’ through global social network.  These are good because 1) they are viral – only get via email, 2) comes with its own tracer (signatures on it).  Can’t see the full tree, but copies get posted to mailing lists, which can be found by search engine.  So they can build a partial tree, compensating for the mutations in the signature tree.

It turns out genetic mutation analogies are good…all kinds of mutations happen (people erase names, put funny names in the middle, etc).

Built the tree from two chain letters, and it looked funny.  If we’re in a small world network (six degrees of separation), why is the tree very deep and narrow, like a depth-first search tree.  Why?  Possible timing effects, assuming that nodes act on messages according to some delay.

So we can make some initial analogies like mutation, biology.  But these are really complex, global phenomena, that require richer models and knowledge of human behavior.  Ideas from computing and online media will be crucial to the next steps.

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.

System Design and Community Culture

The role of rules and algorithms in shaping human behavior

Panelists:

• Lukas Biewald, Dolores Labs
• Rashimi Sinha, Slideshare
• Cameron Marlow, Facebook

Dolores Labs – Making Crowds Efficient and Reliable.  They pay people to perform tasks, aka Amazon Mechanical Turk.

Slideshare – Focus on social design.  Presentations are fundamentally social – you don’t make them for yourself.  The social networking tools (commenting, favoriting, tagging) has lead to the creation of a community.

Facebook – Runs the Data Science team, which uses machine language and research to understand how users use the site, and that leads to design changes.

Examples of Unexpected Community Behavior?

RS: What gets spam, what does not.  Particularly in their comment system.  They went through lots of iterations

LB: Prompting a task affected the outcome.  So now they work with people to define

What sort design decisions are based on difficulty?

LB: Try to break a task into the smallest possible unit.

RS: Presentations are less frequent than say photos, so there are different rules.  Also differentiate between user types: content creator, readers, aggregators.

CM: Facebook isn’t really designed around a task.  They do lots of things to enable use at different levels.

Range of tasks across the three systems.  How do you learn how social interactions change tasks?

RS: Observed real life events (people gather around a presentation). Create a unit, and a construct around that.

CM: FB tries to lower the barrier of trying now tasks.  For example, someone can upload a photo, others can tag photos, add metadata, etc.

Design by Intuition vs. Design by Data.  What is your approach/process in developing new features?

RS: Start with intuition, primary hypothesis.  Look at what data in the world.  Once its up, there’s lots of data to see what people like, what people talk about.  Also do AB Testing.

LB: Can nicely segment users along whatever dimension you want, so you have lots of options.

CM: People react to change.  Some like it, some hate it.  What fraction of the population respond to the change.

We know you can prompt people to get certain types of behavior.  How do you compensate for that?

RS: Not so worried about that — doesn’t have to be scientific.  Of course, you can also do experiments to deal with it.

CM: There are many sources of bias in these large ecosystems.  Important that decision makers know about them.

Community, communicate, share.  What makes for successful conversation?

CM: Allow them to happen at a different scale, use aggregated tools to understand entire conversation.  For example, they have a tool that can find a term/keyword across all of Facebook, as a percentage of all text.  Helps them make sense (in some small way) of everything.

RS: Twitter hashtags are a really good, scalable way to communicate a topic.  Well, maybe partially scalable.  When a hashtag makes twitter trending topics, bots take over.  But things are good up until then.

How do people discover your content, features?

RS: Email, social network links, but mainly Google search

CM: The Wall.  Now have two feeds: 1 real time, 1 algorithm driven.

Twitter innovations: #hashtags, @replies, ReTweets – users came up with those.  How do you design so that users can extend the design on their own?

RS: Initial version of Slideshare was barebones.  Keep the initial design to the core, get feedback, refine.  Build new features based on what works.  Also, develop and API so people can extend your site.

CM: Design a platform so that people can build their own specific tools.

How do you enable the conversation/feedback between designers and community?  How do you differentiate edge case complaints vs real problems.

LB: Designers do customer support

RS: Ditto.  Also, use numbers, percentages of people that complain.

CM: Collect as many signals as possible.  If something shows up across many areas, it may be a real problem.

A Categorical Model for Discovering Latent Structure in Social Annotations

This paper describes a model to the structure of semantic topics over documents using tags.

They propose a community-based categorical annotation model.  Communities form around interests, expertise, language, etc.  Each community has a number of categories as its world view.  Therefore the community draws tags about a document from its list of categories.  Use Gibbs Sampling to recover communities and categories.  This gives you a distribution of communities and categories.

Used a corpus from flickr and delicious.com to do experiments.

First looked at similarity between content-based topics and tag-based topics.  No real similarity.

Propose an example application: Information Access via Topic and Category.  Given a particular page, you can see pages that are similar in content, tags, both, or neither.

My question is about a person’s membership in a community.  When I look at an object, I’m influenced by the tags…it becomes part of my vocabulary.  So really I have a distribution of membership in the different categories.  I’m not sure if/how this work takes that into account.

Content Based Recommendation and Summarization in the Blogosphere

Given a set of blogs related to a topic, find a subset of blog feeds to read that have interest in the topic.  Previous work is on link popularity (PageRank, HITS).  This one looks at content similarity.  It builds a blog post network graph, where directed and weighted edges indicate links from one post to another).  Blog importance defined as the importance of the adjacent blogs.  Post similarity defined by TF-IDF.

This also defines a diversity ranking, and discounts nodes that are too similar to previously selected nodes.  It also adds a user-defined quality factor.

Experiments used BLOG2006 dataset.  Calculate node quality using a Linear Threshold diffusion model.  Compared this algorithm to a random selection of blogs, a simple heuristic for selecting blogs, and a greedy algorithm.

Leskovec has a good paper on selecting interesting blogs using a diffusion model.  It takes into account the fact that there are a lot of repeated stories/topics — I don’t need to read all of them.  I wonder if he could compare this model to that one.

Also, I’m not clear is to whether he’s selecting whole blogs or blog posts.  The algorithm is based on blog posts, and that may not be very accurate.  For example, the most popular post on this blog (by far) is about Pinax deployment, but I haven’t really written about that much in the last year.  Would this entire blog be flagged with this algorithm?

I think there are a lot of techniques for solving this problem.  I propose to evaluate them by accumulating the blogs of the social media researchers

Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations

Sentiment Expressions – single or multi-work phrases that express evaluation.  Assumes binary polarity (positive, negative).

Target – word/phrase which is the object of evaluation.  Sentiment expressions only like to physical targets.

So, given annotated mentions of sentiment anaylsis, find the target.  Did lots of manual annotations to come up with a gold standard against which they can test the targeting system (I feel for those summer interns).

Baseline approach: proximity.  Nearest mentione selected as target.

Baseline 2: Run a dependency parcer.

We use a supervised ranking.  Build independent classifiers for each sentiment expression/possible target pair, and a ranking algorithm to help select between them.  Classifiers trained using RankSVM.

Results: better than baselines.  80% precision/recall/f-score are the human max: this system is around 70%.  This approach definitely beats bag of words, proximity.