THE SCIENCE OF SOCIAL MEDIA

Duncan Watts

Yahoo! Research

• In 1940’s Paul Lazarsfeld :– “Who talks to whom about what, and with what effect?”

• Proven difficult to answer– Measuring “who talks to whom” hard at scale– Measuring the effect of talking even harder

• Web 2.0 brings the answer within reach- Important implications for web companies, marketers

and users

SOCIAL MEDIA SCIENCE?

Attribution: D. J. Watts. A 21st Century Science. Nature, 445, p. 489 (2007)

EIGHT YEARSFOUR EXPERIMENTSMANY INSIGHTS

• 1960’s: Milgram and Travers “small world” experiment

• Protocol generated 300 “letter chains” - 64 reached target

• Led to the famous “six degrees” phrase

• 2002: recreate w/email• Milgram: one target, 300

chains• Now: 18 targets around

world, 24,163 chains, 61,168 hands, 166 countries

• 400 reached targets

IS IT A SMALL WORLD?

ORIGIN OF 6 DEGREES 6 DEGREES – WEB EDITION

Attribution• P. S. Dodds, R. Muhamad, and D. J. Watts. An experimental study of search in global social networks. Science, 301, 827-829 (2003). • Sharad Goel, Roby Muhamad, and D. J. Watts. “Social Search in ‘Small-World’ Experiments” Proceedings of the 18th international conference on World Wide Web, 701-710 (2009)

• Results consistent with Milgram’s findings– Half of all chains estimated to complete within 7 steps

• But chains more egalitarian than Milgram thought• Do not concentrate in “hubs”• Senders do not prefer highly connected friends– Geography and occupation most important

• Also managed to run an experiment with over 60,000 participants, on a global scales, at virtually zero cost– Discovery of the “bored at work network”

• What to do next?– Small-world experiment not really a “lab” experiment– Could we create a “virtual lab” on true web scale?

IT IS (MOSTLY) A SMALL WORLDFINDINGS:

• “Hits” are many X more successful than average

• Success seems obvious in retrospect, but hard to predict

• Can inequality and unpredictability be explained by social influence?

• Problem: experiment would require 1,000s of participants

– Each “market” requires hundreds of participants

– Need to compare many markets

FROM CONNECTIONS INFLUENCE:

CULTURAL MARKETS:

Attributions:•M. J. Salganik, P. S. Dodds, and D. J. Watts. Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854-856 (2006).•Matthew J. Salganik and Duncan J. Watts. “Leading the Herd Astray: An Experimental Study of Self-fulfilling Prophecies in an Artificial Cultural Market.” Social Psychology Quarterly 71:338-355 (2008)•Matthew J. Salganik and Duncan J. Watts. “Web-based experiments for the study of collective social dynamics in cultural markets.” Topics in Cognitive Science, 1, 439-468 (2009).

• Individuals are influenced by observations of the choices of others

– The stronger the social signal, the more they are influenced

• Collective decisions are also influenced– Popular songs are more popular (and unpopular songs are less popular)– However, which particular songs become popular becomes harder to predict

• The paradox of social influence:– Individuals have more information on which to base choices– But collective choice (i.e. what becomes popular) reveals less and less about

individual preferences

• Manipulating social influence not so easy– Can create self-fulfilling prophecies at level of individual songs, but not for

entire market

INFLUENCE IN CULTURAL MARKETS

FINDINGS:

• Music Lab showed importance of influence• But influence in real life diffuses through networks

• Twitter is ideally suited to study diffusion– Fully-observable network of “who listens to whom”– Every tweet corresponds to a “cascade” of information – URL shorteners enable us to track each cascade

– No matter how small or large• Objective is to predict cascade size as function of

– # Followers, # Friends, # Reciprocated Ties

– # Tweets, Time of joining

– Size of previously triggered cascades

INFLUENCE & TWITTER

•D. J. Watts and P. S. Dodds. “Networks, influence, and public opinion formation.” Journal of Consumer Research, 34(4), 441-458 (2007).•D. J. Watts. Challenging the “Influentials Hypothesis.” Measuring Word of Mouth, Vol. 3. Word of Mouth Marketing Association (2007).•D. J. Watts. “The Accidental Influentials.” Harvard Business Review, p. 22-23 (February, 2007)•D. J. Watts and J. Peretti. Viral marketing in the real world. Harvard Business Review (May, 2007)

Cascades on Twitter

• Two months data• Late 2009• 1.6M users

posted 39M bit.ly URL’s

• Hence 39M cascades total

• Average cascade size 1.14

– Median cascade size 1

• Large cascades extremely rare

• Large cascades are rare, hence:– Probably impossible to predict them or how they will start– Better to trigger many small cascades

• Highly visible users tend to be more influential- But only on average – lots of randomness- Also from a marketing perspective, prominent users may also

be expensive

• “Ordinary Influencers” are promising– May influence less than one other person on average– But may also be relatively cheap– Targeting thousands or millions of unexceptional individuals

may be more effective than targeting a few exceptional users

“ORDINARY INFLUENCERS”

FINDINGS:

BIRDS OF FEATHER?

• Influence is important, but also important is another sociological tendency: that friends are more similar than strangers– Sociologists call this “the homophily principle”

• Homophily has been observed for many attributes like race, gender, age, income, education, etc.– Friendships, marriages, workplace relations, etc.

• But what about beliefs?– Seems plausible (look at Congress!)– But some evidence suggests that ordinary Americans

disagree more with their friends than they realize.

• How to test both real similarity of beliefs, and also perceived similarity?

The “Friend Sense” App on Facebook

• Facebook is great for network surveys– Huge population

– Built-in network

– Personal data declared on profiles

• Friendsense generated nearly 12,000 responses from 900 participants, on 47 questions

• For each question q, and each pair (A,B), we record– What A thinks about q

– What B thinks about q

– What A thinks B thinks about q

Results

Friends are more similar than strangers… But not as similar as they think they are

Implications of Friend Sense

• Overestimate of similarity not just that “people don’t discuss politics”– Even good friends fail to detect disagreement more than 50%– Talking about politics helps, but not much (+6%)– Minority opinion holders actually make bigger errors than

majority opinion holders– Suggests a combination of stereotyping and projection

• What does this mean for influence?– Influence assumes knowledge of differences. FS

suggests awareness is itself unusual.

• Goel, Mason, and Watts (JPSP, 2010)

The Future?

• All four experiments explore various aspects of Lazarsfeld’s Question– Small World measured networks– Music Lab measured influence– Twitter measured diffusion– Friendsense measured perceptions

• Haven’t yet put all the pieces together

• But we’re working on it…

Thank You!