Web 2.0 4.1.09 Beyond Buzz: On measuring a conversation Kate Niederhoffer, Ph.D Marc A. Smith, Ph.D Dachis Corporation Telligent Systems

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

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

Why now?

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?

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

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.

(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)

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?

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

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

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!

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.

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

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.

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.

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

Says Who?

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

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.”

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

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

“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)

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.

Beyond thoughts and feelings, who comes to roost?

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

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

Identify core groups in the network

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

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

Answer Person Signatures See Picturing Usenet in JCMC Discussion People

Reply oriented Discussion Discussion Starter Spammer Reply oriented Discussion Flame Warrior

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

What is the mix in the neighborhood?

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

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

Darwin Bell

Meso-scale soc media viz.

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

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

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.

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.

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

Additional Resources

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?

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)