2013 05 20 field_directors
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2013 05 20 field_directors Presentation Transcript

  • 1. Computational Social Science:The Pros and Cons of Big Data’Cliff Lampe- School of Information, University of MichiganMay 20, 2013Monday, May 20, 13
  • 2. Cliff LampeSchool of Information- associate professorSocial media“Socio-technical systems”Primarily a social scientistMonday, May 20, 13
  • 3. Samples of my research in this areaEffects of participation on FacebookInformation cascades on TwitterUser collaboration on WikipediaDiscussion patterns in large-scale news sitesCoordination in massive online gamesInformation seeking on search engines vs. social mediaMonday, May 20, 13
  • 4. Interactions in social media leave communicationtraces we can mine to understand social processes.These compete with insights from surveys.Monday, May 20, 13
  • 5. Defining “Big Data”“Big Data” is a rough categorization, a marketingterm, and a paradigm shift.Monday, May 20, 13
  • 6. Why “big data” has becomea big deal...More devices collecting dataMore data born digitalEasier/cheaper to storeBetter processorsNew skills / techniquesInsights have proven effectiveMonday, May 20, 13
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  • 8. “Big Data” started in thephysical sciencesMonday, May 20, 13
  • 9. Big Data is increasingly beingapplied to social science questionsMonday, May 20, 13
  • 10. What counts as “big”?LHC: .001% of sensors leadto 25 petabytes annually.Wikipedia: 17 terabytesTwitter: ~ 10 GB/dayHow many observationsneeded to count as “big”?Monday, May 20, 13
  • 11. ‘Big Data’ require multiple,interlinked skills and tools.Monday, May 20, 13
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  • 13. Challenges in “Big Data”CaptureCurationStorageSearchSharingTransferAnalysisVisualizationMonday, May 20, 13
  • 14. Social Media is often linked withBig Data because it is the likeliestsource for human trace data.Monday, May 20, 13
  • 15. What is social media?Monday, May 20, 13
  • 16. Common characteristicsUser generated contentDirect user-to-user interactionBundles of applicationsMore than Facebook and TwitterMonday, May 20, 13
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  • 25. Social media cover a widevariety of sites.Monday, May 20, 13
  • 26. Trends in social media useMonday, May 20, 13
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  • 30. Social media skillNearly 1 million people join Facebook every weekPeople spend on average 16 hours a month onFacebookThere are about 250 million Tweets per dayPeople upload 3000 pictures to Flickr every minuteWikipedia has 17 million articles by 91,000 editorsYouTube has 490 million unique visitors per monthGoogle + reached 10 million users in 16 daysMonday, May 20, 13
  • 31. The social media landscapeis constantly changing.Monday, May 20, 13
  • 32. People are increasinglyenacting their social lives incomputer mediatedchannels.Monday, May 20, 13
  • 33. Examples of social media /big data social insightsMonday, May 20, 13
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  • 40. Social media are being usedto capture big social data.Monday, May 20, 13
  • 41. Obtaining social media trace data“Scraping”APIs(rare) public datasetsPartnershipsMonday, May 20, 13
  • 42. Scraping - using software todownload and store datafrom publicly available site.Monday, May 20, 13
  • 43. Application Performance InterfacesMonday, May 20, 13
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  • 45. Available datasetsMonday, May 20, 13
  • 46. PartnershipsMonday, May 20, 13
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  • 48. Issues with social mediatrace dataAccessRepresenting resultsRepresentativenessValidityCross-channel difficultyAppropriate skill setsEthicsMonday, May 20, 13
  • 49. AccessData often owned byprivate corporations.Need special skills toaccess.Monday, May 20, 13
  • 50. Representing resultsProbability testing breaksdown.Visualization is common,but limited.Training audiences for newdata analysis.Monday, May 20, 13
  • 51. Representativeness*How accurately dosocial media usersrepresent the largerpopulation?How do you rigorouslysample from socialmedia?Monday, May 20, 13
  • 52. ValiditySocial media users areperforming (thoughdon’t know scientistsare observing them)Different sites havedifferent purposes.Monday, May 20, 13
  • 53. Cross-channel useHow do you track oneuser over multiplesocial media sites?Monday, May 20, 13
  • 54. Appropriate researcher skillsetsCombination oftechnical and researchskills are required.New generation of“data scientists”coming now.Monday, May 20, 13
  • 55. EthicsHow can a user optout?More on a panel nextsession...Monday, May 20, 13
  • 56. Challenges in “Big Data”CaptureCurationStorageSearchSharingTransferAnalysisVisualizationMonday, May 20, 13
  • 57. “Pros”of big dataIt’s relatively cheapMassive scale coverssome sinsIt’s inevitable(?)In some cases, itworks.Monday, May 20, 13
  • 58. Insensitive Borg says...Big Data will makesurveys obsolete.Monday, May 20, 13
  • 59. Humble SuggestionsMore interdisciplinary work.Propose and fund work to test these issues.Don’t pretend it isn’t coming OR is a panacea.We’re just at the beginning of the journey.Monday, May 20, 13
  • 60. Social media and surveysprojectCan social media data ever replace and/or supplementsocial measurement, especially for official statistics,based on self-reported answers to questions asked ofa representative sample?Fred Conrad, Michael Schober, Josh PasekMonday, May 20, 13
  • 61. Thanks!Cliff Lampecacl@umich.eduTwitter: @clifflampeSlideshare: clifflampeMonday, May 20, 13