web science - how is it different?
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Web Science: How is it different? Daniel Tunkelang, LinkedIn Keynote Address at ACM Web Science 2014 Conference The scientific method of observation, measurement, and experiment may be our greatest achievement as a species. The technological innovation we enjoy today is the product of a culture of systematized scientific experimentation. But historically scientific experimentation has been expensive. Experiments consumed natural resources, took a long time to conduct, and required even more time and labor to analyze. In order to be productive, scientists have had to factor these costs into their work and to optimize accordingly. Web science is different. Not, as some have speciously argued, because big data has made the scientific method obsolete. The key difference is that web science has changed the economics of scientific experimentation. Thus, even as web scientists apply the traditional scientific method, they optimize based on very different economics. In this talk, I'll survey how web science has changed our approach to experimentation, for better and for worse. Specifically, I'll talk about differences in hypothesis generation, offline analysis, and online testing. Bio Daniel Tunkelang is Head of Query Understanding at LinkedIn, where he previously formed and led the product data science team. LinkedIn search allows members to find people, companies, jobs, groups and other content. His team aims to provide users with the best possible results that satisfy their information needs and help to get insights from professional data. Tunkelang has BS and MS degrees in computer science and math from MIT, and a PhD in computer science from CMU. He co-founded the annual symposium on human-computer interaction and information retrieval (HCIR) and wrote the first book on Faceted Search (Morgan and Claypool 2009). Prior to joining LinkedIn, Tunkelang was Chief Scientist of Endeca (acquired by Oracle in 2011 for $1.1B) and leader of the local search quality team at Google, mapping local businesses to their home pages. He is the co-inventor of 20 patents.TRANSCRIPT
Daniel
Web Science: How is it different?
Daniel TunkelangHead of Query Understanding
tl;dr:
The scientific method is alive and well.Big data has just changed the economics.
How have the web and big data changed science?
Let’s ask some of the experts.
“You have to kiss a lot of frogs to find one prince. So how can you find your prince
faster? By finding more frogs and kissing them faster and faster.”
Mike MoranDo It Wrong Quickly: How the Web Changes the Old Marketing
Rules, 2007
Cited by Kohavi in Online Controlled Experiments at Large Scale, 2013
Web Science = faster, cheaper experiments.
“The cost of experimentation is now the same or less than the cost of analysis. You can get more value…by doing a quick experiment than from doing a
sophisticated analysis.”
Michael SchrageValue-Creation, Experiments, and Why IT Does Matter, 2010
Web Science = more experiments, less analysis?
“with massive data, this approach to science — hypothesize, model, test — is becoming obsolete… Petabytes allow us to say: "Correlation is enough." We can
stop looking for models…analyze the data without hypotheses…throw the numbers into the biggest computing clusters the world…and let…algorithms find patterns
where science cannot.”
Chris AndersonThe End of Theory, 2008
RIP
ScientificMethod
1600 BCE –late 20th century
Killed by Big Data
?
No.
Let’s rewind.
What makes it science?
Hypothesis
Model
Test
The scientific method still works today.
What’s changed is the economics.
Scientific Method1747
Scientific MethodToday
It’s the economy, science.
YesterdayExperiments are
expensive,choose hypotheses
wisely.
TodayExperiments are cheap,do as many as you can!
What about Web Science?
A/B testing: everybody’s doing
it.
Google: 20k search
experiments per year
hypotheses
The Myth of Insight
Scientists gain insight
by staring at data.
Big data tools improve
data exploration.
In hypothesis generation,
quantity trumps quality.
Except when it doesn’t.
Easier to analyze data than research
humans.
But we pay the price.Example: search engine improvements in
batch evaluations don’t always predict real user benefits.
[Hersh et al, 2000] Do Batch and User Evaluations Give the Same Results?
[Turpin & Hersh, 2001] Why Batch and User Evaluations do not Give the Same Results
[Turpin, Scholer, 2006] User Performance versus Precision Measures for Simple
Search Tasks
But also see…
[Smucker & Jethani, 2010] Human Performance and Retrieval Precision Revisited
When local optimization is cheap, you neglect the rest.
To summarize: how is web science
different?•Online testing is cheaper and scalable.
•Data exploration tools make hypothesis generation cheaper and easier.
•But the experiments that are easy and cheap aren’t always the most valuable.
•Easy to forget our biases as scientists.
Take-Aways•The scientific method is alive and well.
Big data has just changes the economics.
•Cheaper hypothesis testing and generation has already been transformative. That’s why big data matters.
•But we neglect the human side of scientific experimentation at our peril.
