Proprietary & Confidential — © 2008 DachisCorporation

Beyond Buzz: On

measuring a conversation

Kate Niederhoffer, Ph.D Marc A. Smith, Ph.D Dachis Corporation Telligent Systems

Web 2.0 4.1.09

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Why us?

Kate Niederhoffer

•  Ph.D UT Social Psychology

•  BuzzMetrics/Nielsen Online, Measurement Science

•  Dachis Corporation - Methodology, Social Business Design

Marc Smith

•  Ph.D UCLA Sociology

•  Microsoft Research, Community Technologies Group

•  Telligent Systems – “Harvest” reporting and analysis tools for social media platforms and systems

Note: This is a conceptual address. We’re talking about ideas; each of our companies have distinct methodologies in place related to these concepts.

Why are we here?

1. Demonstrating the depth of buzz; ways to think about signal within vast universe.

2. Going beyond buzz; learning more about individuals.

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Why are we here?

3. Highlighting the unique roles individuals play in communities that afford the conversation.

4. Illustrating that aggregated relationships are network structures.

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Why now?

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Blogs were all the rage

In 2005, clients attracted by novelty:

Simple question: What’s my buzz?

- How much?

- Good or bad?

Incremental improvement: How “important” is it?

- Are “Influencers” talking?

- How many eyeballs exposed?

- Engagement?

However, all superficially measured;

limited scope of what’s important: what kind of influence?

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Blogs are now features

•  Today’s “media” enable richer social interaction-- and, leave a path of data with more opportunities to capture depth

•  Buzz levels, page views, followers, in isolation miss big picture

•  Must take advantage context to tell whole story and capture value

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Social networks are all the rage, but

rarely do we think about social metrics

We need to stop blackboxing: "When a machine runs efficiently, when a matter of fact is settled, one need focus

only on its inputs and outputs and not on its internal complexity. Thus, paradoxically, the more science and technology succeed, the more opaque and obscure they become." - Bruno Latour

Even if a conversation is running smoothly, we must figure out what makes it tick.

Central tenet: Social structure emerges from

the aggregate of relationships (ties) among members of a population

Phenomena of interest: Emergence of cliques and clusters

from patterns of relationships Centrality (core), periphery (isolates),

betweenness

Methods: Surveys, interviews, observations, log file analysis,

computational analysis of matrices

Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of

Communication, Simon Fraser University. pp.7-16

Social Network Theory

(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

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Context of a conversation

Relevance

Role

Mindset

Ecosystem

What is the pattern of connections?

What is the dynamic, en masse?

What else do we know about the individuals?

Where’s the signal in the noise?

Persona

Person

Environment

Signal

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Context of a conversation

Relevance

Role

Mindset

Ecosystem

Where’s the signal in the noise?

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Relevance today

•  As a user, easy to relate to issues with pre-determined filters.

•  As an enterprise, complexity increases.

We don’t always know what we want to know!

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Relevance: Which filters are in place to strengthen the signal?

•  Identifying your filters can be inductive:

•  What are people really saying?

•  Which concepts differentiate the posts that mention you vs. posts that don't?

•  All terms on your map have a correlation to the central concept; the closer a word appears to the center, the stronger the association.The groupings of terms indicate the dimensions of discussion: micro-conversations within a broader discussion.

* Source: Nielsen Online, 2008

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Relevance is multi-faceted

• Rather than looking at associations with, as compared to without, consider discussion this week as compared to discussion over the past year.

• Not what’s being said about her in a more recent timeframe, but instead when you control for what’s said about her in general, what pops?

* Source: Nielsen Online, 2008

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Relevance - Summary

• Information can be visualized in so many different ways; don’t take it for granted. • Listening can be limited if you’re exclusively looking for

something in particular; broaden your net. Be inductive. Let the data speak for itself.

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Context of a conversation

Relevance

Role

Mindset

Ecosystem

What else can we know about the individuals?

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Says Who?

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Mindset

•  By measuring the types of words used, we can tap into how people ‘slice’ their worlds.

•  Linguistic style is closely tied to:

•  Demographics (e.g. age, sex, class)

•  Emotion (e.g. depression, deception)

•  Cognitive style (e.g. complex thinking)

•  Personality (e.g. Neuroticism)

Findings Linguistic Cues

Are you self-oriented? Pronoun use: I and We

Are you living in ‘the now’?

Past, Present, Future tense

What is your emotional tone?

Positive vs. Negative

Are you abstract or concrete?

Articles: “a” vs. “the” Nouns vs. verbs

What else can we know about the person in conversation?

e.g. Pennebaker, Mehl, Niederhoffer, 2003

When people make

recommendations on blogs, is

there something deeper going on?

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“Got the next three PW/GS games for my birthday. And I am one happy gal, there was some stuff that I absolutely LOVED and I

would definitely recommend the game to anyone who owns a PS3 regardless of its flaws -- which really were at their heart personal quibbles of mine so your mileage may vary. Plus, I cried like a b*$$ at the end. That's g o t t o b e s a y i n g s o m e t h i n g . ”

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Getting into the Engaged Mind

•  Recommendations have:

•  More pronouns: intimacy with both the brand/product/ service being recommended, and those to whom they’re recommending.

•  More verbs: sharing experience more than discussion of concrete features.

* all differences significant at p<.01 level

“Invisible” language gives us clues

about individuals, and groups

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Changes in work atmosphere,

captured in words

Tausczik, Scholand, and Pennebaker, 2009

Engineers, economists programmers collaborating on economic simulations of disasters

•  Complexity of thought (-) •  Cohesion (-) •  Work information (-) •  Negative emotion (+)

•  Funding lost

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“Connected Age”: relationships are

groundwork of work

Social: niceties (lol), affirmations (cool), coordination (call), broad communication

(http, thinking)

Work: economic (production, supply), analytic (results, problem)

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Mindset- Summary

• Language is a good way to go beyond the surface and better understand constituents without self- report biases (or effort).

•  Metrics in the hands of users (yourselves) are helpful: know thyself, know how you’re perceived.

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Beyond thoughts and feelings, who comes to roost?

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Context of a conversation

Relevance

Role

Mindset

Ecosystem

What is the pattern of connections?

Social

Network

Analysis

with

NodeXL:

Identify

different

roles in

social media

spaces

Identify core groups in the

network

• Answer person – Outward ties to local isolates – Relative absence of triangles

– Few intense ties

• Reply Magnet – Ties from local isolates often inward only – Sparse, few triangles

– Few intense ties

Distinguishing attributes:

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Distinguishing attributes:

• Answer person – Outward ties to local isolates – Relative absence of triangles

– Few intense ties

• Discussion person – Ties from local isolates often inward only – Dense, many triangles

– Numerous intense ties

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AnswerPerson

SignaturesDiscussionPeople

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SpammerDiscussionStarter

ReplyorientedDiscussion Flame

Warrior

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Role – Summary

•  Network awareness, like court vision enables strategic play. Know which positions/players are on your team.

•  Social media behavior is differentiated. Rare (~.5-2%) roles are critical and must be cultivated.

•  E.g. Clear and consistent signatures of an “Answer Person

•  Light touch to numerous threads initiated by someone else

•  Most ties are outward to local isolates

•  Many more ties to small fish than big fish

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What is the mix in the neighborhood?

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Context of a conversation

Relevance

Role

Mindset

Ecosystem What is the dynamic, en masse?

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Pajekwithoutmodifica>oncansome>mesrevealstructuresofgreatinterest.

The Ties that Blind?

DarwinBell40

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Two “answer people” with an emerging 3rd.

Mapping Newsgroup

Social Ties

Microsoft.public.windowsxp.server.general 43

Adamicetal.WWW2008

Research shows social media

spaces vary and roles are present

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Ecosystem- Summary

•  Social media is about collective action. •  A balance of roles and strategies is critical for a healthy/ successful collective good.

•  Harvesting the common good takes many forms, and is the ultimate goal of social media.

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Why does this matter?

•  This is not measurement for the sake of measurement; we need to measure conversations in order to manage social business.

•  Measuring conversations is about measuring the context in which those conversations arise.

•  Value is an intermediate step in calculating ROI. Moot to bypass it.

•  Techniques from social science help capture “the immeasurable” in social media and the enterprise.

•  The future of conversations- the enterprise being one-- is about cultivating ecologies of the right balance of relationships.

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Thank You

k.niederhoffer@gmail.com

marc.smith@telligent.com

Questions?

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Additional Resources

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How uniform are social media producing groups?

IndividualsSmallGroups

VariableContribu>onLargeGroups

UniformLargeGroups

HeterogeneousVariableContribu>on

LargeGroups

Social Science Theory and

Method

Interactionist Sociology Central tenet

Focus on the active effort of accomplishing interaction

Phenomena of interest

Presentation of self

Claims to membership

Juggling multiple (conflicting) roles

Frontstage/Backstage

Strategic interaction

Managing one’s own and others’ “face”

Methods

Ethnography and participant observation

(Goffman, 1959; Hall, 1990)

Collective Action Dilemmas Central tenet

Individual rationality leads to collective disaster

Phenomena of interest

Provision and/or sustainable consumption of collective resources

Public Goods, Common Property, "Free Rider” Problems, Tragedies

Methods

Surveys, interviews, participant observation, log file analysis, computer modeling

(Axelrod, 1984; Hess, 1995; Kollock & Smith, 1996)