1. Web
Beyond Buzz: On measuring a conversation Kate Niederhoffer, Ph.D Marc A. Smith, Ph.D Dachis Corporation Telligent Systems

2. 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.

3. Why are we here?
Demonstrating the depth of buzz; ways to think about signal within vast universe.
Going beyond buzz; learning more about individuals.

4. Why are we here?
Highlighting the unique roles individuals play in communities that afford the conversation.
Illustrating that aggregated relationships are network structures.

5. Why now?

6. 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?

7. 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

8. 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.

9. (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
Social Network
Theory
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
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

10. Context of a conversation
Relevance
Signal
Where’s the signal in the noise?
Mindset
Person
What else do we know about the individuals?
Role
Persona
What is the pattern of connections?
Ecosystem
Environment
What is the dynamic, en masse?

11. We didn’t used to have a holistic overview of the Earth; we don’t have one of social media. It’s hard to find signals in all the noise; sometimes you aren’t even picking up on all the noise

12. Context of a conversation
Relevance
Where’s the signal in the noise?
Mindset
Role
Ecosystem

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

14. 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?
* Source: Nielsen Online, 2008
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.

15. 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

16. 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.

17. Globally coherent activity is a beautiful thing, but really can’t appreciate it in full– or experience it without tapping into the individual constituents producing the signal.

18. Context of a conversation
Relevance
Mindset
What else can we know about the individuals?
Role
Ecosystem

19. Says Who?

20. Mindset What else can we know about the person in conversation?
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
e.g. Pennebaker, Mehl, Niederhoffer, 2003

21. When people make recommendations on blogs, is there something deeper going on?
“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.”

22. 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

23. “Invisible” language gives us clues about individuals, and groups

24. Changes in work atmosphere, captured in words
Engineers, economists programmers collaborating on economic simulations of disasters
Complexity of thought (-)
Cohesion (-)
Work information (-)
Negative emotion (+)
Funding lost
Tausczik, Scholand, and Pennebaker, 2009

25. “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)

26. 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.

27. Beyond thoughts and feelings, who comes to roost?

28. Context of a conversation
Relevance
Mindset
Role
What is the pattern of connections?
Ecosystem

29. Social Network Analysis with NodeXL: Identify different roles in social media spaces

30. Identify core groups in the network

31. Distinguishing attributes:
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

32. 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

33. Answer Person Signatures
See Picturing Usenet in JCMC
Discussion
People

34. Reply oriented Discussion
Discussion Starter
Spammer
Reply oriented Discussion
Flame
Warrior

35. 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

36. What is the mix in the neighborhood?

37. Context of a conversation
Relevance
Mindset
Role
Ecosystem
What is the dynamic, en masse?

39. The Ties that Blind?
Pajek without modification can sometimes reveal structures of great interest.

40. Darwin Bell

41. Meso-scale soc media viz.

43. Mapping Newsgroup Social Ties
Microsoft.public.windowsxp.server.general 43
Two “answer people” with an emerging 3rd.

44. Research shows social media spaces vary and roles are present
Adamic et al. WWW 2008

45. 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.

46. 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.

47. Thank You k.niederhoffer@gmail.com marc.smith@telligent.com Questions?

48. Additional Resources

50. How uniform are social media producing groups?
Small Groups
Individuals
Uniform Large Groups
Heterogeneous
Variable Contribution Large Groups
Variable Contribution Large Groups
How uniform are social media producing groups?

51. Social Science Theory and Method
Interactionist Sociology
Collective Action Dilemmas
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)
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)