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Web Science: How is it different? Daniel Tunkelang Head of Query Understanding

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Daniel Web Science: How is it different? Daniel Tunkelang Head of Query Understanding


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tl;dr: The scientific method is alive and well. Big data has just changed the economics.


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How have the web and big data changed science? Let’s ask some of the experts.


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“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 Moran Do It Wrong Quickly: How the Web Changes the Old Marketing Rules, 2007 Cited by Kohavi in Online Controlled Experiments at Large Scale, 2013


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Web Science = faster, cheaper experiments.


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“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 Schrage Value-Creation, Experiments, and Why IT Does Matter, 2010


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Web Science = more experiments, less analysis?


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“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 Anderson The End of Theory, 2008


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?


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No.


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Let’s rewind.


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What makes it science?


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Hypothesis


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Model


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Test


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The scientific method still works today. What’s changed is the economics.


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Scientific Method 1747


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Scientific Method Today


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It’s the economy, science. Yesterday Experiments are expensive, choose hypotheses wisely. Today Experiments are cheap, do as many as you can!


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What about Web Science?


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A/B testing: everybody’s doing it.


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Google: 20k search experiments per year


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hypotheses


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The Myth of Insight


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Scientists gain insight by staring at data.


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Big data tools improve data exploration.


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In hypothesis generation, quantity trumps quality.


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Except when it doesn’t.


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Easier to analyze data than research humans.


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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


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When local optimization is cheap, you neglect the rest.


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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.


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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.


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Daniel Tunkelang dtunkelang@linkedin.com https://linkedin.com/in/dtunkelang


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