A Networked Take on Influence: what we learn from data
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Analyzing data from social interactions on Twitter and Facebook gives us clues as to how people choose to manage their attention. When intelligently predicted, captured and visualized, we witness emerging patterns. Some swear by a certain time of day. Others spend an inordinate amount of time trying to reach certain users deemed "influencers." But its clear that the networked dynamics at play are much more complicated. While there is no simple recipe for predicting information flows in online social spaces, there's plenty of room for optimization.
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A Networked Take on Influence: what we learn from data
- 1. A Networked Take on Influence Symposium & Workshop on Measuring Influence on Social Media | #Influence12 Social Media Lab, Dalhousie University, Halifax Gilad Lotan | @gilgul
- 3. Sculpting Audiences Alex Dragulescu
- 4. Sculpting Audiences Alex Dragulescu
- 5. Influence: The ability to disproportionately affect the spread of information purchasing patterns of peers television viewing behavior Within Context => Wanted Action
- 6. “Influence” as an Exposed Metric is Problematic • Simple quantifiable measure • Innate nature • People aren’t always rational – may trust someone despite past transgressions • Exogenous – may come from outside the observed network • Based on Context
- 7. Receptivity of your Audience Flickr: mangpages
- 9. f(Topic, Network, Timing, Authority, Trust, Historical Behavior, Network Density, Activity…)
- 13. Given tweet(s) => if/when to post
- 14. Diurnal Patterns
- 15. Daily Routines
- 16. Geographic Distribution USA Germany Indonesia
- 17. Data Reflects Us
- 18. Identifying Spikes 49ers: 36, Saints: 32
- 19. Typical Sports Event Game ends! 49ers Game starts
- 20. TV Show Social Movement Awards Ceremony
- 21. Breaking News (zipfian distribution)
- 23. Audience & Regional Trends
- 24. Trends by Categories Meme Entertainment Lifestyle Sports Poli(cs Holidays or Date Place or Loca(on #UglyNames #TheDarkKnightRises #WhenIGetDrunk Eli Manning Obama Halloween Chilean #truthbomb JonahHill #TodayImWearing #ElClassico StateOfTheUnion Thanksgiving Spain #TextMessagesFro #IWishIHad Emma Stone mAStalker #GoLakers World War Web 9/11 Staples Center Edward Scissorhands The Boy is Mine ESPNU Arab Spring Pi Day Wembley #ThoughtsWheniLookI nTheMirror Foo Fighters #mydivorcesong Giants Won Michele Bachmann Happy Mothers Day Baghdad #10ThingsIFindA.ra c(ve
- 25. GeoSpan
- 27. Volatility (across cities) Vola(lity = sum of edit distance in consecu(ve slots of the trending topics list
- 28. Audience Attention: Real World Events How do audiences of different clients react to real world events?
- 30. Audience Activity @Pepsi @AP Pepsi Promo(on @AJEnglish Whitney Houston’s Tragic Death Announced
- 31. Shape of an Audience #Kony2012
- 32. Noblesville, IN Birmingham, AL Oklahoma City Engelwood, Dayton, OH Pi.sburgh
- 33. #IGoToASchool
- 34. Miami
- 35. Vancouver
- 36. New Jersey
- 38. Audience Authorities • Highly central users often have many followers • Some people are exceptions: not that many followers, but very central (e.g. Jezebel) • Most valuable users to engage with? Fewer demands on attention (less popular globally), but well-connected within network.
- 44. Bridging by Providing Context
- 45. Instead of “Influencer”, think about: • Deviation from the Norm • Audience Receptivity • Network Attributes: shape, centrality, clustering • Bridges: generating trust • Context Setting
- 46. Thank You Gilad Lotan | @gilgul gilad@socialflow.com