slicing big data: gambling, twitter & time sensitive information

Gambling, Twitter & Time Sensitive Information

IR14 - Denver,[email protected]

@dpwoodford

Wednesday, 23 October 13

mailto:[email protected]

mailto:[email protected]

FORMAT

• Not going to simply repeat the paper.

• I will get to the gambling (& fantasy sports) examples, but want to discuss our wider work with large datasets.

• Happy to answer more specific questions about the use in the gambling industry.

• Examples from Sport, TV, Gambling & Fantasy Sports. A tour-de-force of current research projects


DEALING WITH THE TITLE: TWITTER

• Twitter => Large Data Sets, but specific research questions often require a small data set:– Australian users– Users registering on the platform during natural disasters– ‘Experts’ on Fantasy Sports– Sporting Participants: Golf, Tennis, NFL, College Football, etc..– Reality TV ‘fanatics’– Almost infinite examples

• Goal is to get from “Big Data” to what I’ve been calling “useful data”


DEALING WITH THE TITLE: GAMBLING

• Long term interest in the gambling industry (one case study in my prior work on games).

• Many parallels between Gambling and Fantasy Sports (another current research project).

• When I was an ‘active participant’, Twitter was just becoming popular (2006-2010).

• It quickly became a crucial source of information, and websites started aggregating it.


DEALING WITH THE TITLE: GAMBLING


DEALING WITH THE TITLE: TIME SENSITIVE INFORMATION

• Lines move incredibly fast: Just as much a market as day-trading on the stock exchange


WHY IS DATA SLICED?

• Streaming API is limited to ~1% of total tweets per second & Firehose access is expensive.

• Large data sets are not easily malleable, or visually analyzed (e.g. with Tableau):– Our database of Twitter users is ~3.7TB, and growing.– A weeks worth of selected TV data (current US shows) in JSON

format is 750MB, and 600MB in TSV (selected fields). And millions of rows.

• Analyzing large data sets is slow, if it’s even possible => “Usable Data”


HOW IS DATA SLICED: COMPULSORY


HOW IS DATA SLICED: SELECTING FOR AUTHENTICITY -- WTA


HOW IS DATA SLICED: SELECTING FOR AUTHENTICITY -- FANTASY SPORTS


HOW IS DATA SLICED: SELECTING FOR AUTHENTICITY -- FANTASY SPORTS

CLIP FROM YAHOO FANTASY FOOTBALL RE: CALVIN JOHNSON INJURY & TWITTER REPORTS


BUT YOU STILL NEED A SANITY CHECK


HOW IS DATA SLICED: RANDOM SAMPLING

Source: Tony Hirst (Open University UK)


BUT SOMETIMES YOU NEED THE FULL SAMPLE & REPEATED CAPTURE

Source: Bruns / Woodford [Mapping Online Publics]


HOW IS DATA SLICED: ONLY A SMALL SAMPLE MATTERS

Floods, Earthquake, Tsunami

Media Coverage


HOW IS DATA SLICED: TV -- SEASONAL DATA VS EPISODIC

Impact of Live Feed



Delayed TV sucks


HOW IS DATA SLICED: MOST ACTIVE ≠ REPRESENTATIVE

• Most active (#BB15, #BBLF) users often defend a HM to the death (akin to sporting tribalism), but most users are attackers (forthcoming paper w/ Katie Prowd)

Disclaimer: Scale changed to fit on slide

Source: Woodford / Prowd [Fan Cultures and Hatred in Big Brother 15: Race Rows, EliMsm & SporMng Tribalism -‐-‐ Forthcoming]


TIME SLICES OF TWEET CONTENT IS ENLIGHTENING




• Twitter closed these quickly, yet the BB15 accounts remained active for much of the season...


AND A QUICK NOTE ON NON-TWITTER ANALYTICS



• There’s lots of data out there, but it needs to be sliced to be usable.

• You can work with large, original, data sets, but often this adds extra complexity that isn’t necessary to answer your research questions.

• But don’t delete the data you don’t need!


ACKNOWLEDGEMENTS

• ARC Centre for Excellence in Creative Industries and Innovation (CCI) - http://www.cci.edu.au & http://www.mappingonlinepublics.net

• Social Media Research Group -- http://socialmedia.qut.edu.au

• Queensland University of Technology


http://www.cci.edu.au

http://www.cci.edu.au

http://www.mappingonlinepublics.net




http://socialmedia.qut.edu.au




slicing big data: gambling, twitter & time sensitive information

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