online trends analysis - itnig friday
DESCRIPTION
Almost everybody knows what an internet meme is. But what most people don't know is that there's a science behind memes and internet trends. What's the definition of meme? How does it propagate and why? How and where can we find trending memes? What are the key properties for a successful meme? And most important... where to find them?TRANSCRIPT
Online trends Analysis Francesc Gomez-Morales @francescgo [email protected] me.francescgo.com
#itnigfridays
WHO AM I?
Analy/cal Heart & Mashup Mind
2007 2008 2009 2010 2011 2012 2013
LET’S ASSUME YOU KNOW WHAT YOU’RE TALKING ABOUT…
Web is about measurement
Too many things to measure
Gatorade Mission Control
A framework proposal (from a marke/ng perspec/ve)
hBp://www.flickr.com/photos/nocallerid_man/
Web Analysis Framework (Marke/ng perspec/ve)
On-‐site
Owned Media
Web Page
“Web Analysis”
Google Analy/cs / Kissmetrics / CrazyEgg (…)
Social Media and other
Public Profiles
“Social Media Analysis”
Facebook insights / SocialBro / Bit.ly (…)
Off-‐site
Compe/tors owned media
Web Page
Web Compe//ve Intelligence
Alexa / Compete / Builtwith
Social Media and other
Public Profiles
Social Media Compe//ve Intelligence
SocialBakers / DKS Social Smart
Consumers media
Web Pages and Social
Media Profiles
Web Monitoring or netnography
Radian6 / Brandchats / Websays /
Google Trends
Search Engines
Organic
SERP Monitoring
SEOmoz / Link Assistant / Raventools
Paid
PPC Campaigns Monitoring
Semrush / Wordstream
Let’s focus on trends
hBp://www.flickr.com/photos/20116729@N00/
What the trend?
A trend (fad) is any form of behavior that develops among a large popula/on and is collec/vely followed with enthusiasm
for some period (generally as a result of the behavior's being perceived as novel in some way)
Sociology in a Changing World By William Kornblum, Carolyn D. (COL) Smith Page 213
WHY TRENDS & MEMES ARE NOW SO TRENDING?
Thank you, Tim Berners-‐Lee
Massive adopYon of online services has contributed to the unique and unprecedented possibility of register and analyze micro-‐trends
in almost-‐real-‐Yme
hep://www.flickr.com/photos/rishibiswas/
Key technological innova/ons (IMHO) hep://www.flickr.com/photos/rishibiswas/
Rich Internet Applica/ons (RIA) ex. Use of javascript
Web-‐oriented Arquitecture (WOA)
Apache Hadoop + MapReduce + Google File System
The biggest Social Graph in human history
One graph to rule them all!
Visualize your own graph
heps://apps.facebook.com/touchgraph/
TALKING ABOUT TRENDS IS TALKING ABOUT PEOPLE
People and trends Diffusion of Innova/ons, 5th Edi/on Everee M. Rogers 2003
Social Technographics • Trends are generated by
people’s ac/vity
• Social Technographics classifies people according to how they use social technologies
• Forrester's Social Technographics data classifies consumers into seven overlapping levels of social technology par/cipa/on
People and trends > Social Technographics
hep://empowered.forrester.com/ladder2010/
People and trends > Social Technographics
Forrester Ladder
Differences between countries People and trends > Social Technographics
Different between Ages People and trends > Social Technographics
all animals are equal… People and trends > Influence
… but some animals are more equal than others
klout People and trends > Influence
hep://klout.com/
klout People and trends > Influence
hep://klout.com/#/topic/social-‐media
SOURCES OF TRENDS
Sources of trends
• Every single ac/vity on the web is registered and could be analyzed
• Different ac/vi/es, different sources of trends
• The more “passive” behaviour, the easiest analysis. Analyze Visits is simplier than analyze blog posts.
Sources of trends: classifica/on by user ac/vity
Creators
Conversa/onalists
Cri/cs
Collectors
Joiners
Espectators
Inac/ves Forrester Ladder
Ranking of the main social networking sites
Sources of trends > Visits/Pageviews of websites/content
Top sites in spain -‐ Sept 14, 2013
Sources of trends > Visits/Pageviews of websites/content
Top sites by niche
hep://www.alexa.com/topsites/category/Top/Sports/Table_Tennis
Sources of trends > Visits/Pageviews of websites/content
heps://adwords.google.com/da/DisplayPlanner
Sources of trends > Visits/Pageviews of websites/content
Youtube Consump/on in Spain
Sources of trends > Visits/Pageviews of websites/content
hep://www.socialbakers.com/youtube-‐sta/s/cs/country/spain/
hep://youtube.com/trendsmap
Sources of trends > Visits/Pageviews of websites/content
hep://www.youtube.com/trendsdashboard
Sources of trends > Visits/Pageviews of websites/content
hep://google.com/trends/explore
Sources of trends > Use of keywords in search engines
Sources of trends > Use of keywords in search engines
hep://google.com/trends/explore
Sources of trends > Use of keywords in search engines
Sources of trends > Use of keywords in search engines
hep://appannie.com/matrix
Sources of trends > Installa/on of mobile apps
Sources of trends > Installa/on of mobile apps
hep://appfigures.com/reports/ranking
Sources of trends > Installa/on of mobile apps
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Sources of trends > Installa/on of mobile apps
hep://appfigures.com/reports/reviews
hep://www.appdata.com/
Sources of trends > Use of social games or social apps
hep://www.appdata.com/
Sources of trends > Use of social games or social apps
Top10 stories of the day (From May 24, 2007 to May 23, 2008)
Sources of trends > News aggregators ac/vity
hep://www.chrisharrison.net/index.php/Visualiza/ons/DiggRings
hep://www.socialbakers.com/facebook-‐pages/celebri/es/spain/
Sources of trends > Social support of profiles
Celebri/es in Facebook (Spain)
hep://www.socialbakers.com/facebook-‐pages/celebri/es/spain/
Sources of trends > Social support of profiles
Poli/cs in Twieer (Spain)
hep://www.socialbakers.com/facebook-‐pages/7703918541-‐internet-‐explorer
Sources of trends > Social support of profiles
hep://www.pagedatapro.com/pages/leaderboard/tac/tac_wg
Sources of trends > Social support of profiles
hep://es.favstar.fm/all-‐/me-‐most-‐favorited-‐tweets
Sources of trends > Support and/or Difussion of content
hep://es.favstar.fm/all-‐/me-‐most-‐retweeted-‐tweets
Sources of trends > Support and/or Difussion of content
Sources of trends > Support and/or Difussion of content
hep://monitor.wildfireapp.com/count_reports/display?facebook
Sources of trends > Support and/or Difussion of content
hep://twtrland.com/
Sources of trends > Find Places and Check-‐in
hep://www.4sqmap.com
Sources of trends > Use of keywords in Twieer
hep://trendsmap.com/
Sources of trends > Use of keywords in Twieer
hep://trends24.appb.in/barcelona
Sources of trends > Use of keywords in Twieer
hep://whaehetrend.com/top10
The business of trends
heps://blog.twieer.com/2013/study-‐the-‐value-‐of-‐promoted-‐trends
Sources of trends > Contribu/ons to open source projects or wikis
hep://artzub.com/ghv/#repo=jquery&user=jquery
Sources of trends > Contribu/ons to open source projects or wikis
heps://github.com/twbs/bootstrap/contributors
Sources of trends > Contribu/ons to open source projects or wikis
heps://github.com/twbs/bootstrap/graphs/code-‐frequency
Sources of trends > Contribu/ons to open source projects or wikis
heps://github.com/twbs/bootstrap/graphs/punch-‐card
Sources of trends > Contribu/ons to open source projects or wikis
hep://notabilia.net/
Top 100 most controversial ar/cles in English Wikipedia
Sources of trends > Contribu/ons to open source projects or wikis
hep://gigapan.com/gigapans/4277
Top 100 most controversial ar/cles in English Wikipedia
Sources of trends > Contribu/ons to open source projects or wikis
hep://wikiproject.oii.ox.ac.uk/mapping_wikipedia
Number of words of english geolocated Wikipedia ar/cles
Sources of trends >Adop/on of technology
hep://trends.builtwith.com
Sources of trends >Adop/on of technology
hep://trends.builtwith.com/analy/cs
Top 10k websites Top 1M websites
Sources of trends >Adop/on of technology
heps://trends.builtwith.com/widgets/Facebook-‐Like
TREND DYNAMICS
Meme/cs
Cultural traits are transmieed from person to person, similarly to genes or viruses. Cultural evolu/on therefore can be understood through the same basic mechanisms of reproduc/on, spread, varia/on, and natural selec/on that underlie biological evolu/on.
Trend Dynamics > Meme/cs
Cultural EvoluYon and MemeYcs Francis Heylighen & Klaas Chielens hep://pespmc1.vub.ac.be/Papers/Meme/cs-‐Springer.pdf
Meme
Cultural replicator; a unit of imita/on or communica/on
Trend Dynamics > Meme/cs > Meme
The Selfish Gene (2nd ediYon) Dawkins, R. (1989) Oxford University Press.
1. Longevity 2. Fecundity 3. Copying-‐Fidelity
Memeplex
Collec/on of mutually suppor/ng memes, which tend to replicate together
Trend Dynamics > Meme/cs > Memeplex
Cultural EvoluYon and MemeYcs Francis Heylighen & Klaas Chielens hep://pespmc1.vub.ac.be/Papers/Meme/cs-‐Springer.pdf
Memes and trends Trend Dynamics > Meme/cs
Memes
Trends
Meme Dynamics Trend Dynamics > Meme/cs
Assimila/on • No/ce • Understand • Acceptance
Reten/on • longevity
Expression • Speech • Text • …
Transmission • Internet • Mass Media
Meme Dynamics Trend Dynamics > Meme/cs
Assimila/on
Reten/on
Expression
Transmission
Meme Fitness
The anatomy of a (Twieer) trend
Main takeaways: • the resonance of the content with the users of the social network plays a
major role in causing trends • a majority of the content propagated to cause trends arise from tradi/onal
media sources
Trend Dynamics > Twieer Trending Topics > The anatomy of a trend
The way the trends dissipate fits a normal distribu/on
16.32 million tweets on 3361 different topics over 40 days in Sep-‐Oct 2010
Trend Dynamics > Twieer Trending Topics > The anatomy of a trend
The decay factor of a trend is 1/t Trend Dynamics > Twieer Trending Topics > The anatomy of a trend
Example A trending topic with 10.000 tweets/hour How many tweets will have axer 5 hours? N(t=0) = 10.000 tweets t = 5 hours N(t=5) = N(t=0)·∙[1/t] = = 10.000 ·∙ 1/5 = 2000 tweets
Almost every trending topic is short Trend Dynamics > Twieer Trending Topics > The anatomy of a trend
Strong rela/on between influencers and trending topics
Rela/on between authors and ac/vity: • No propagaYon, no trend Correla/on between number of unique authors with the dura/on (0.8)
• No people retweeYng, no trend The number of retweets for a topic correlates very strongly (0.96) with the trend dura/on
• Many influencers, longer trends Nega/ve correla/on of −0.19 between the domina/on-‐ra/o of a topic to its trending dura/on (The domina/on-‐ra/o for a topic can be defined as the frac/on of the retweets of that topic that can be aeributed to the largest contribu/ng user for that topic)
Trend Dynamics > Twieer Trending Topics > The anatomy of a trend
Twieer doesn’t generate news, but does filter them
Social media, far from being an alternate source of news, func/ons more as a filter and an amplifier for interes/ng news from tradi/onal media
Trend Dynamics > Twieer Trending Topics > The anatomy of a trend
(Twieer) Informa/on diffusion
Main takeaways: • the resonance of the content with the users of the social network plays a
major role in causing trends • a majority of the content propagated to cause trends arise from tradi/onal
media sources
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
Very few people follow or are followed by many users
15 million URLs exchanged among 2.7 million users over a 300 hour period
Both the in-‐degree (followers) and the out-‐degree (followee) distribu/ons have /ght log-‐normal fits
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
Twieer is a small world 15 million URLs exchanged among 2.7 million users over a 300 hour period
Twieer graph is a small world with a mean directed path length of 3.61
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
The majority of users has low ac/vity Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
The majority of URLs have no resonance
If your URL appears in 10 different users, then it's more popular than 99.9% of the rest of URL tweeted
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
Only a very few tweets has several cascades of retweets
RT-‐cascades (the user men/ons the source) F-‐cascades (the user men/ons the source and is its follower) Each cascade consists of one or more subcascades. The number of subcascades per cascade is power-‐law distributed
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
How big are subcascades?
For each cascade the subcascades not only vary in number, but also in size.
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
If your tweet is interes/ng is more likely that I will follow you
The distances to the root are short, even when compared with the already short average path length in the follower graph. One hypothesis explaining this data could be that when a user receives some interes/ng URL along an path longer than 1, then that user is very likely to start following the original source of the URL
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
The more users, the bigger the subcascades
The URLs tweeted by the highly connected users reach large audiences and are likely to be (re)tweeted by their followers. However, the causality is likely to be bidirec/onal: the users’s URLs are tweeted more because they have many folllowers, but also they have accumulated many followers because what they tweet tends to be interes/ng and viral.
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
How long takes to make a RT? The diffusion delay taken across all the (u, i) pairs in the social graph is log-‐normally distributed with a median of 50 minutes
Trend Dynamics > Twieer Trending Topics > Informa/on Diffusion
TREND PARAMETRIZATION
Trending Topic Classifica/on Trend Parametriza/on
TwiBer Trending Topic ClassificaYon Kathy Lee, Diana Palse/a, Ramanathan Narayanan, Md. Mostofa Ali Patwary, Ankit Agrawal, and Alok Choudhary 2011 11th IEEE Interna/onal Conference on Data Mining Workshops
768 trending topics selected randomly from the +23000 collected between 2010 and 2011
Classifica/on Tecniques Trend Parametriza/on
Comparison of classifica/on accuracy using different classifiers for network-‐based classifica/on
TwiBer Trending Topic ClassificaYon Kathy Lee, Diana Palse/a, Ramanathan Narayanan, Md. Mostofa Ali Patwary, Ankit Agrawal, and Alok Choudhary 2011 11th IEEE Interna/onal Conference on Data Mining Workshops
Classifica/on Tecniques Trend Parametriza/on
Comparison of classifica/on accuracy using different classifiers for text-‐based classifica/on
TwiBer Trending Topic ClassificaYon Kathy Lee, Diana Palse/a, Ramanathan Narayanan, Md. Mostofa Ali Patwary, Ankit Agrawal, and Alok Choudhary 2011 11th IEEE Interna/onal Conference on Data Mining Workshops
NBM = Naive Bayes Mul/nomial SVM = Support Vectors Machines (number of tweets, top frequent terms)
“Online Trends Analysis” by Francesc Gómez Morales is licensed under a Crea/ve Commons Aeribu/on-‐NonCommercial-‐ShareAlike 3.0 Unported License