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Quantitative Analysis of User-Generated Content
on the Web
Xavier Ochoa, ESPOL, EcuadorErik Duval, KULeuven, Bélgica
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TopicsWhy?StudiesFindings
Implication of the Findings
ConclusionFurterWork
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Why?
• UGC economy:– Supply: Users publishing their content
–Demand: Users viewing content from others
–Currency: Attention
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Why?
• Demand (Popularity) is relatively well understood:
• But Supply (Publication) is not....
How a ‘hit’ is born (S Sinha, RK Pan, 2006)
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Studies
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Studies
1. Descriptive Statistics
2. Distribution Fitting
3. Concentration Analysis
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Findings
• Distribution of supply is not Normal
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Findings
• Distribution of supply has a heavy tail
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Findings
Lotka (“fat-tail”) Weibull (“fat-belly”)
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Implications of the Findings
There is not such thing as an “average user”
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Low Class
Middle Class
High Class
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Implications of the Findings
The production of different UGC types is similar, but not
the same.
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Implications of the Findings
Pareto Rule (80/20) applies to UGC
(but no substitute to measuring)
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Implications of the Findings
“Fat-tail” UGC production is similar to professional
production.
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Implications of Findings
The distribution is not affected by site size
or production effort
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Implications of the Findings
Make your bet, head or tail?
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50% of Content is generated here
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50% of Content is generated here
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Implications of the Findings
Informetrics can help us to understand UGC production
(and vice versa)
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Conclusions
• Measuring is our only way to test our hypothesis about how Web works
• If you admin a UGC-based site, measure production to gain insight on the other side of your economy
• Inequality of Contribution of UGC is real and should be dealt with in all its variations.
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Further Work
• Modeling Production of UGC• Integrate UGC inside the Informetrics /
Scientometrics / Webometrics framework• Expand the data collection and analysis– Measure growth (size and contributors)– Measure production rate– Use at least 3 examples for each type of UGC