the 1990s • PhD in Sociology, research project on statistical visualization in the 2000s • Blog Research in the 2000s („metaroll“) • Social Media Intelligence @ethority • Now: Data Science in Media & Advertising @dcore_munich • Twitter: @furukama • Blog: beautifuldata.net • Mail: [email protected] • Github for this talk: https://github.com/furukama/pydata2014-berlin INTRODUCTION
-> Data Science (Pete Skomoroch) • New use for data that is already there (e.g. geo-position in tweets -> movement) • New use for old methods (e.g. genome sequencing in advertising research) • A lot of Ad-hoc research (e.g. citation networks at political events) • Improvisations • Publishing your recipes (no more secret sauce) WHAT IS STREET FIGHTING TREND RESEARCH? http://en.wikipedia.org/wiki/File:Fightingmanstones.jpg
more or less impossible (Yogi Berra theorem) • But:„The Past does not repeat itself, but it rhymes“ (Mark Twain) -> Telling stories about possible futures and developments • And: Predicting the present (Hyunyong Choi and Hal Varian) • Early indicators • Retrospect revisions • Data with little cost http://static.googleusercontent.com/media/www.google.com/de//googleblogs/pdfs/google_predicting_the_present.pdf PREDICTING THE PRESENT
(Elina Hiltunen): • Megatrends: fundamental changes affecting many people in the world and lasting many years (e.g. ageing population, climate change, urbanization) • Trends: shorter and more local changes (e.g. messaging, blogging) • Wild Cards: sudden big events (e.g. 911) • Weak Signals: first signs of emerging change, often overlooked, not important – yet! Elina‘s dissertation: http://epub.lib.aalto.fi/pdf/diss/a365.pdf MEGATRENDS, TRENDS AND WEAK SIGNALS
aka Coolhunting • Scenarios • Visioning … “Google Cayce and you will find "coolhunter," and if you look closely you may see it suggested that she is a "sensitive" of some kind, a dowser in the world of global marketing. Though the truth, Damien would say, is closer to allergy, a morbid and sometimes violent reactivity to the semiotics of the marketplace.” COOLHUNTING IN LITERATURE
Horvitz: „Mining the Web to predict future events“ (2012) http://research.microsoft.com/en-us/um/people/horvitz/future_news_wsdm.pdf PREDICTING FUTURE EVENTS
to predict than others. • Precision = How many of the predicted events were right • Recall = How many of the reported events were correctly predicted. http://research.microsoft.com/en-us/um/people/horvitz/future_news_wsdm.pdf PREDICTING FUTURE EVENTS
Topics, Foursquare Trending Locations are great, but … • No access to raw data • (Almost) no context • Closed source • Parameters can‘t be customized • Missing APIs • Not always the right topics • => Do It Yourself approach! QUANTITATIVE TREND RESEARCH
Topics from the Web • Example 2) Mining Conference Proposals • Example 3) Identifying Trending Locations on Foursquare • Code at https://github.com/furukama/pydata2014-berlin EXAMPLES
konifar
himorishige
masahiro_nishimi
thatjeffsmith
javiergs
ahuglajbclajep
nearme_tech
inc2734
manfredsteyer
hareyakayuruyaka
ajstarks
uxyall
csswizardry
katiecoart
addyosmani
orderedlist
jonoalderson
nikkihalliwell
afnizarnur
jnunemaker
eileencodes
tmiket
bkeepers
