Understanding a Twitter and its followers. David Bessis, tinyclues 20/3/2012

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Understanding a Twitter audience: @VogueParis and

1 Understanding a Twitter and its followers David Bessis, tinyclues 20/3/2012

3 @VogueParis Twitter account of French edition of Vogue magazine managed by Condé Nast Digital followers on Twitter the second largest Twitter audience in France

4 Natural questions why is the audience so large? who are the followers? what are the followers interested in? what is the value of this audience? For all these questions, there is: no standard answer format no standard methodology

5 Twitter data is big data over 300 million users over 300 million tweets per day concurrent structures to study: 1. Text mining: what a user tweets says something about her 2. Social graph mining who a user follows says something about him real time tweet data, ever changing social graph

7 the Twitter API: basics an interface to answer basic questions, e.g.: who is this user (name, location, url, etc.)? what are the last tweets by this user? who does this user follow? who follows this user? freely accessible as a web service

8 the Twitter API: limitations hard limits on the size of answers e.g., when querying the list of followers, you only get them 5000 at a time hence you need 170 API calls to get the full list followers hard limits on the number of API calls: 350 calls per hour for authentified users 150 calls per hour of unauthentified users no access to historicized data

9 the Twitter API: practical limitations getting basic facts (name, location, number of followers, ) is possible getting advanced facts (list of tweets, list of followers, list of followees, ) about a small number of users is possible getting advanced facts about a huge number of users is impossible social graph can only be sampled

10 tough questions which insight do we want to derive from the data? which aspect of the data will the insight be based on? how will we obtain/handle/process the necessary data? which algorithms/statistical models will we need? Big Data is difficult because efficient problem-solving requires simultaneous answers to all these questions

11 360 picture of a Twitter user Tweets text date hashtags urls User data account name location language Social graph follower followee 11

12 360 picture of a Twitter user: latent meaning Tweets text date hashtags urls User data account name location language interest mood influence network gender age Social graph follower followee 12

13 mining the social graph followers followees 13

14 local shape is meaningful «audience» user a few followers (up to 1000) a few followees (up to 1000) «media» user many followers (up to millions) a few followees 14

15 understanding «media» users is key a «media» account is characterized by its audience two media accounts are similar if their audiences overlap more than they would by pure chance (this can be made into a formal mathematical definition) this allows automated clustering of media accounts into natural categories once this clustering is performed, it becomes possible to understand «audience» users in terms of the «media» accounts they are following

16 example: automated detection of accounts 16

17 example: clusters of «media» @LorieOfficiel 17

18 technical aspects data gathering: automated launch of many servers to simultaneously query the Twitter API data processing: relational structure with several hundred of millions of lines data analysis: combined use of several sophisticated tools, including linguistic algorithms graph-mining algorithms clustering algorithms

19 some questions we were able to answer basic reporting (user location, language this is easy) gender distribution users (thanks to linguistic analysis of usernames) dynamics of the follower count (on a typical day, how many users are gained, how many are lost) structuring in the audience into «themes» identifying accounts similar (whether or not there is a direct link between them modelling the «retweet impact» of posts identifying the kind of tweets that had the highest retweet impact

20 Thanks! David Bessis About tinyclues: tinyclues is a full-service predictive analytics provider founded in people (mostly R&D engineers & mathematicians) 100% cloud based, 100% control of data software stack serves several industries: ecommerce digital marketing digital medias market research 20

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