beyond buzz - web 2.0 expo - k.niederhoffer & m.smith
Post on 18-Oct-2014
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A framework to measure a conversation based on approaches from social psychology and sociology. Beyond quantity of buzz, we propose measuring the context of conversation: the signal, person, role, and ecosystem.TRANSCRIPT
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.
1.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.
3.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 relationshipsCentrality (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 NetworkTheory
(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*$$ at the end. That's got to be saying something.”
“Got the next three PW/GS games for my birthday. And I am one happy gal, there was some stuff that I absolutely LOVED
and I would definitely recommend the game to anyone who owns a PS3 regardless of its flaws -- which really were at their heart personal quibbles of mine so your mileage may vary. Plus, I cried like a b*$$ at the end. That's got to be saying something.”
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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
Signatures
DiscussionPeople
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SpammerDiscussion
Starter
Reply orientedDiscussion 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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100
0 1 2 4 8 16 32 64
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What is the mix in the neighborhood?
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Context of a conversation
Relevance
Role
Mindset
EcosystemWhat is the dynamic, en masse?
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Pajek without modification can sometimes reveal structures of great interest.
The Ties that Blind?
Darwin Bell40
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Two “answer people” with an emerging 3rd.
Mapping Newsgroup
Social Ties
Microsoft.public.windowsxp.server.general43
Adamic et al. WWW 2008
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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Additional Resources
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How uniform are social media producing
groups?
IndividualsSmall Groups
Variable Contribution Large Groups
Uniform Large Groups
Heterogeneous Variable Contribution
Large Groups
Social Science Theory and MethodInteractionist 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)