Social Media
Marketing 2010
harnessing the power of social
          networks
Viral
what it isn’t
Original Tweet: 0834hrs
Cascade: 1
Cascade: 2
Cascade: 3 (shows 2)
Cascade: 4 (shows 3)
Cascade: 5 (shows 4)
Initial tweet responsible for approx 2K visitors
Friday, 11 Sep 2009 1115hrs
Friday, 11 Sep 2009 1115hrs
“Viral”? No.
Just “Amplified”
      R0 < 1
cf. Leskovac, Adamic, Huberman: “The Dynamics of Viral Marketing” ACM Transactions on the Web, 1, 1 (May 2007)
http://bit....
Probability of retweet increases with close relationships?
About Social Media
Audience has an audience
1: Use people as a channel
           (naïve)
Filters
2: Bypass filters
     sophisticated
3: Use the filters
       (zen)
Demonstration 1
                                                     20 people stand up




cf. Altheide and Johnson, "Cou...
http://webfinger.org - http://code.google.com/p/webfinger/ -
Enrich Your Data
                   Explicit                                           Inferred
                      •   ...
Enrich Your Data
                   Explicit                                           Inferred
                      •   ...
Homophily
Social Norms
Homosexuality
              If more than 1.89% of your Facebook friends are gay...
                          then you prob...
Obesity
                 171% increased risk of obesity if your close friend is
                                        ob...
Teach Yourself SNA
Tools           Data                Books & Papers
 • UCINet/       •   Twitter API     • Wasserman &
 ...
Homophily     Social Norms




Two Sides of One Coin
Inferring Influence
Inferring Influence
Indegree       # followers/fans

Eigenvector        “Page Rank”

Betweeness     # shortest paths
Warning!
mileage may vary
Demonstration 2
                                                      clapping in time




Challenging the Influentials Hyp...
Start Collecting Social
      Data Now
• Not just a channel
• Enrich your CRM data
• Better Segmentation & Targeting
• Ide...
Please follow
@mediaczar
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
Social Media 2010: Harnessing the Power of Social Networks
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Social Media 2010: Harnessing the Power of Social Networks

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Talk given on Thursday June 17, 2010 at the Social Media Marketing 2010 conference

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  • Social media needs to be measured in different ways

    &amp;#x201C;Civilization and its discontents&amp;#x201D; where Freud talks about the conflict between the individual&amp;#x2019;s desire to maintain their individuality and society&amp;#x2019;s pressure to conform.

    I work as a marketeer; for years I&amp;#x2019;ve been part of that pressure.

    Social Media presents -- express their individuality -- new set of unconscious pressures.



  • Social media needs to be measured in different ways

    &amp;#x201C;Civilization and its discontents&amp;#x201D; where Freud talks about the conflict between the individual&amp;#x2019;s desire to maintain their individuality and society&amp;#x2019;s pressure to conform.

    I work as a marketeer; for years I&amp;#x2019;ve been part of that pressure.

    Social Media presents -- express their individuality -- new set of unconscious pressures.













  • Most of the people who retweeted are already tightly linked -- recognized more than ONE name on the retweet schedule. There&amp;#x2019;s a SOCIAL act going on here; not simply sharing information, but using the act of sharing information to reinforce their relationships w/in the wider network

  • People are talking about your products, messages, about your ideas, about your policies.
  • Think about how many brands, NGOs and political organizations are trying to get you to do this.

    ethically grey.
  • Twitter

    Where do you hear about stuff FIRST?

    Unconscious subtle instantaneous decisions

    respect like love trust
  • Filters are almost fractally complex and individual. That&amp;#x2019;s why they WORK.

    So here are some of the ways we&amp;#x2019;ve tried to get around it.












  • Well -- it turns out we can. Up to a point. Here&amp;#x2019;s a map of Twittering MPs. The colours tell you what their party affiliation is.

    Only, I didn&amp;#x2019;t colour them by hand. I asked the computer to work out what the most likely cliques and factions were.

    It got one person wrong.
  • Derek Wyatt is actually Labour. But it&amp;#x2019;s not bad for a small data set.
  • And here&amp;#x2019;s the same exercise performed on the US Congress. We got six out of 46 wrong. Still feels pretty good. We&amp;#x2019;re beginning to be able to tell something about someone based on nothing more than WHO THEY KNOW.



  • Here&amp;#x2019;s another example. This time its Republicans and Democrats on Twitter. If you take out only two betweeners,
  • You get a world where the Republicans don&amp;#x2019;t know what the Dems are saying and vice versa.

    Betweeners are people who span two or more worlds. Let&amp;#x2019;s take a closer look at why there aren&amp;#x2019;t MORE of them. It&amp;#x2019;s because of a thing called Homophily.




  • Social Media 2010: Harnessing the Power of Social Networks

    1. 1. Social Media Marketing 2010 harnessing the power of social networks
    2. 2. Viral what it isn’t
    3. 3. Original Tweet: 0834hrs
    4. 4. Cascade: 1
    5. 5. Cascade: 2
    6. 6. Cascade: 3 (shows 2)
    7. 7. Cascade: 4 (shows 3)
    8. 8. Cascade: 5 (shows 4)
    9. 9. Initial tweet responsible for approx 2K visitors
    10. 10. Friday, 11 Sep 2009 1115hrs
    11. 11. Friday, 11 Sep 2009 1115hrs
    12. 12. “Viral”? No. Just “Amplified” R0 < 1
    13. 13. cf. Leskovac, Adamic, Huberman: “The Dynamics of Viral Marketing” ACM Transactions on the Web, 1, 1 (May 2007) http://bit.ly/aNiZdk
    14. 14. Probability of retweet increases with close relationships?
    15. 15. About Social Media
    16. 16. Audience has an audience
    17. 17. 1: Use people as a channel (naïve)
    18. 18. Filters
    19. 19. 2: Bypass filters sophisticated
    20. 20. 3: Use the filters (zen)
    21. 21. Demonstration 1 20 people stand up cf. Altheide and Johnson, "Counting Souls: A Study of Counseling at Evangelical Crusades", The Pacific Sociological Review,Vol. 20, No. 3, (July 1977), pp. 323-348 http://bit.ly/cJxAt8
    22. 22. http://webfinger.org - http://code.google.com/p/webfinger/ -
    23. 23. Enrich Your Data Explicit Inferred • Name • Age cohort • Age • Gender • Gender • Location • Location • Music preference • Contact details • Gender preference • Social graph • Obesity • etc • etc cf: ‘Inference of Profile Elements of Individuals Using Publicly Available Social Web Data’ (Kozikowski 2009) [Masters Thesis] http://bit.ly/aBRIxW
    24. 24. Enrich Your Data Explicit Inferred • Name • Age cohort • Age • Gender • Gender • Location • Location • Music preference • Contact details • Gender preference • Social graph • Obesity • etc • etc cf: ‘Inference of Profile Elements of Individuals Using Publicly Available Social Web Data’ (Kozikowski 2009) [Masters Thesis] http://bit.ly/aBRIxW
    25. 25. Homophily
    26. 26. Social Norms
    27. 27. Homosexuality If more than 1.89% of your Facebook friends are gay... then you probably are as well Homophily Jernigan, Carter, Mistree, Behram. "Gaydar: Facebook friendships expose sexual orientation" First Monday [Online],Volume 14 Number 10 (25 September 2009) http://bit.ly/8YTy2X
    28. 28. Obesity 171% increased risk of obesity if your close friend is obese Social Norms ‘The Spread of Obesity in a Large Social Network over 32 Years’: Christakis & Fowler, (New England Journal of Medicine, July 26 2007) http://bit.ly/9TcH3W
    29. 29. Teach Yourself SNA Tools Data Books & Papers • UCINet/ • Twitter API • Wasserman & NetDraw • Facebook API Faust • Pajek • Crawlers • de Nooy, Mrvar • Gephi • Blog links & Batagelj • NodeXL • Email data • Granovetter • yEd • Own data • Watts • GraphViz • etc • Leskovec • etc • etc
    30. 30. Homophily Social Norms Two Sides of One Coin
    31. 31. Inferring Influence
    32. 32. Inferring Influence Indegree # followers/fans Eigenvector “Page Rank” Betweeness # shortest paths
    33. 33. Warning! mileage may vary
    34. 34. Demonstration 2 clapping in time Challenging the Influentials Hypothesis (Watts 2007) Forget influentials (Bentley & Earls 2008)
    35. 35. Start Collecting Social Data Now • Not just a channel • Enrich your CRM data • Better Segmentation & Targeting • Identify Influentials (caveat.)
    36. 36. Please follow @mediaczar

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