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Dynamics of Internet-mediated partnership formation

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A talk summarizing works on Internet dating and prostitution by me, Fredrik Liljeros and Luis Rocha.

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Dynamics of Internet-mediated partnership formation

  1. 1. Dynamics of Internet- mediated partnership formation Petter Holme with Luìs Enrique Correa da Rocha, Christofer Edling & Fredrik Liljeros
  2. 2. romantic & sexual relations
  3. 3. romantic & sexual relations media
  4. 4. romantic & sexual relations media
  5. 5. romantic & sexual relations media data
  6. 6. romantic & sexual relations media data
  7. 7. romantic & sexual relations media data
  8. 8. Computers can analyze survey data. Compared with the internal working speed of a computer, the rate of operation of its peripheral equipment—in particular, the input and output mechanisms—is slow. (Simpson) 1961
  9. 9. 1966 Computer matching studies. Despite the evidence on the romantic nature of women ... the present data indicate that in a first dating situation, men more often than women experience romantic attraction for their partners. (Coombs & Kenkel, J. Marriage & Family) Computers can analyze survey data. Compared with the internal working speed of a computer, the rate of operation of its peripheral equipment—in particular, the input and output mechanisms—is slow. (Simpson) 1961
  10. 10. 1966 Computer matching studies. Despite the evidence on the romantic nature of women ... the present data indicate that in a first dating situation, men more often than women experience romantic attraction for their partners. (Coombs & Kenkel, J. Marriage & Family) Computers can analyze survey data. Compared with the internal working speed of a computer, the rate of operation of its peripheral equipment—in particular, the input and output mechanisms—is slow. (Simpson) 1961
  11. 11. 1966 Computer matching studies. Despite the evidence on the romantic nature of women ... the present data indicate that in a first dating situation, men more often than women experience romantic attraction for their partners. (Coombs & Kenkel, J. Marriage & Family) Computers can analyze survey data. Compared with the internal working speed of a computer, the rate of operation of its peripheral equipment—in particular, the input and output mechanisms—is slow. (Simpson) 1961 1970 The information age / information explosion. (Toffler)
  12. 12. 1966 Computer matching studies. Despite the evidence on the romantic nature of women ... the present data indicate that in a first dating situation, men more often than women experience romantic attraction for their partners. (Coombs & Kenkel, J. Marriage & Family) Computer communication democratize information. Death of distance. Scientists in obscure universities ... will be able to participate in scientific discourse more readily. (Folk) 1977 Computers can analyze survey data. Compared with the internal working speed of a computer, the rate of operation of its peripheral equipment—in particular, the input and output mechanisms—is slow. (Simpson) 1961 1970 The information age / information explosion. (Toffler) 1967-77 Golden age of social network analysis. Small-world experiment. Centrality indices. Similarity indices. Strength of weak ties.
  13. 13. Computer communication & society. As more and more people use computer-mediated communication, its societal effects are becoming critical research topics. (Kiesler, McGuire) 1984 Linguistic changes might occur as a result of computer mediated communication. (Baron)1984 Internet Relay Chat Open chat-rooms. Net.romances was a designated dating channel1988 Prostitution advertised on Usenet groups alt.sex.services, later alt.sex.prostitution1986 Communicating emotions electronically. Is electronic communication depersonalizing? … Communicators must imagine their audience, for at a terminal it almost seems as though the computer itself is the audience. (Kiesler, McGuire) 1982
  14. 14. 1999 Scale-free networks.A large class of networks have power-law degree distributions. Triggered search for universal features & mechanisms (Barabási & Albert). WWW dating sites. match.com1995 First scholarly work on IRCAnonymity make love- seekers braver. Special netiquette develops. Internet communication as a data source about social interaction. (Reid) 1991 Datamining. Ways to find patterns in data beyond regression.late 1980’s Data driven social network studies. E-small-world, bursty dynamics, crowd intelligence2000’s 2003 Holme, Edling, Liljeros, Structure and time-evolution of an Internet dating community, Social Networks 26:155–174. Rocha, Liljeros, Holme, Information dynamics shape the sexual networks of Internet-mediated prostitution, PNAS 107:5706–5711. 2010
  15. 15. 7 Romantic networks
  16. 16. P.Holme,C.R.Edling&F.Liljeros. Structureandtime-evolutionofan Internetdatingcommunity. SocialNetworks26:155–174,2004. You are logged in as: user Z P20 You have one new message Message box Hey you Friday, July 5, 200 User A F20 Here User A has space to write about herself . . . » Community /user A F all in one place N u
  17. 17. rC −0.04 −0.05 0 100 200 300 400 500 t (days) 100 200 300 400 5000 0.02 0.01 0 0.007 0.006 400 500300 t (days) rewiredoriginal
  18. 18. 0.001 p,p' 0.01 0.1 100 τ (days) 10�� 10��� 10�� 10�� 10110�� 10� e-print e-mail pussokram.com p p'
  19. 19. 0.001p,p' 0.01 0.1 100 τ (days) 10�� 10��� 10�� 10�� 10110�� 10� e-print e-mail pussokram.com p p' accumulated ongoing 1 0.1 0.01 k P(k) 10�� 10�� 10�� 10� 10�101Holme, 2003. Network dynamics of ongoing social relationships Europhys. Lett. 64:427–
  20. 20. 13 Sexual networks
  21. 21. a Cumulativedistribution,P(k) Number of partners, k Females Males α 10�� 10�� 10�� 10� 10�� 10�10�10�
  22. 22. bCumulativedistribution,P(ktot) Total number of partners, k totα Females Males 10� 10�� 10�� 10�� 10� 10�� � 10�10�10�
  23. 23. Liljeros et al., 2001. The Web of Human Sexual Contacts Nature 411:908–909. ba Cumulativedistribution,P(k) Cumulativedistribution,P(ktot) Number of partners, k Total number of partners, ktot totα Females Males Females Males α 10 10�� 10�� 10�� 10� 10�� 10�� 10�� 10�� 10� 10�� 10�10�10� 10�10�10�
  24. 24. Degree / activity correlations Nordvik, Liljeros, 2006. Sexually Transmitted Diseases, 33:342–349.
  25. 25. Degree / activity correlations Nordvik, Liljeros, 2006. Sexually Transmitted Diseases, 33:342–349.
  26. 26. 16 Sexual networks in prostitution
  27. 27. Who buys sex & why? Pitts et al., 2004.Arch. Sex. Behav. 33:353–358.
  28. 28. Cultural differences Wikipedia legal and regulated legal but pimping, brothels, etc. are illegal illegal unknown
  29. 29. Peculiar economics Edlund et al., 2009. “The Wages of sin” working paper.
  30. 30. What determines the price? Increasing the number of prostitutes by one adds to the totalp (i)Fm ipn expenditure on prostitutes. If then the revenue per∗ p (i)F ! p (n)F ,m ipn npi prostitute must fall; conversely, if the revenue must∗ p (i)F 1 p (n)F ,m ipn npi increase. Formally, ∗ ! 0 if p (i)F ! p (n)Fm ipn npi ∗′ ∗ p (n)F p 0 if p (i)F p p (n)F (13)npi m ipn npi { ∗ 1 0 if p (i)F 1 p (n)F .m ipn npi Condition (13) implies that there is a unique if∗ n ෈ (0, N ) and To see this, note that∗ ∗ p (0) 1 p (0) ϩ w(0) p (N ) ! p (N ) ϩ w(N ).m m then and cross at most once since and∗ ′ p (n)F p (i) p (i) 1 0npi m m if∗′ ∗ p (n)F p 0 p (i) p p (n)F .npi m npi As before, if N is sufficiently large. Moreover,∗ p (0) 1 p (0) ϩ w(0)m ∗ p (N ) ! p (N ) (14)m is a sufficient condition for To see that condition∗ p (N ) ! p (N ) ϩ w(N ).m (14) holds, note that if the richest man buys the services fromn p N, more than one prostitute; there are no wives, and Hence,∗ ˆp (N ) ! y(N ). he would be willing to pay to one woman to be his wife∗ p (N ) 1 p (N )m instead of his full-time prostitute. Q.E.D. Edlund, Korn, 2002. A theory of prostitution, J. Pol. Econ. 110:181– 214.
  31. 31. How is the information shared between sex-buyers. What is the relation between different types of prostitution? What are the trends? Can we project into the future? What are the implications for disease spreading? What determines the price? Human dynamics.
  32. 32. Rocha, Liljeros, Holme, 2010. PNAS 107, 5706-5711. Elizabeth Pisani, http://www.wisdomofwhores.com/ Swedes make sex boring, even in Brazil Another thumbs down for the Swedish model. Not the leggy blonde, not even Sweden’s moralistic approach to the sex trade. This one is the Swedish research model, which has managed to turn the fascinating subject of on-line rating of hookers by Brazilian punters into something indescribably dull.
  33. 33. Rocha, Liljeros, Holme, 2010. PNAS 107, 5706-5711. Elizabeth Pisani, http://www.wisdomofwhores.com/ Swedes make sex boring, even in Brazil Another thumbs down for the Swedish model. Not the leggy blonde, not even Sweden’s moralistic approach to the sex trade. This one is the Swedish research model, which has managed to turn the fascinating subject of on-line rating of hookers by Brazilian punters into something indescribably dull.
  34. 34. location
  35. 35. type of sex
  36. 36. date
  37. 37. grade
  38. 38. Metrics Buyers Sellers Number of vertices 10,106 6,624 Size of largest component 9,652 6,158 Number of edges 40,895 Number of encounters 50,185 Original Randomized Diameter of largest comp. 17 13.2 ± 0.1 Average distance 5.78 4.921 ± 0.002 Number of 4-cycles 231,439 64,360 ± 302 Assortativity −0.110 −0.0896 ± 0.0005
  39. 39. numberoffutureposts 0 10 20 30 40 50 −1.0 −0.5 0 0.5 1.0 /20,TG final 615≤s<20 20≤s<4 0 ≤ 4s< number of posts /2,TGfinalTfinal 0 0.2 0.4 0.6 0.8 1 1 3 10 30 100 300 Feedback
  40. 40. Degree distribution & preferential attachment 1 1 10 K 1 10 K 1 p(k≥K) p(k≥K) 0.1 1 sampling time 0.9 1 1.1 δ 0.9 1 1.1 δ sampling time 0.1 1 sellers buyers 10�� 10�� 10��10�� 0.1 10�� 0.1 10�� 10� 10�
  41. 41. 0.6 0.7 0.8 0.9 1.0 frequencyofnocondomuse 0.7 0.8 0.9 1.0 10 100 post number, τ post number, τ 1 10 100 1 frequencyofnocondomuse sellers buyers Trends in risky behavior
  42. 42. 1 10 10 F 1 10 10 F T (days) ~ T 0.5 ~ T 0.5 T (days) sellers buyers ~ T 1.2 ~ T 0.6 ~ T 0.6 ~ T 1.2 10� 10� 10� 10� 10��10�� 0.1 0.1 Detrended fluctuation analysis
  43. 43. Geography
  44. 44. LMA Bettencourt et al., PNAS 104:7301–7306 (2007).
  45. 45. LMA Bettencourt et al., PNAS 104:7301–7306 (2007).
  46. 46. fractionofedgesbetweencities intercity dist. (km) slope–2 10�� 10�� 10� 10� numberofsellers population size sellers slope110� 10� 10� 10� numberofbuyers population size buyers slope1 10� 10� 10� 10� City size & spatial scaling
  47. 47. 6,7,9 B C 1,2,4,5 11 10,15 A Temporal effects
  48. 48. time 6,7,9 B C 1,2,4,5 11 10,15 A Temporal effects
  49. 49. Empirical Randomized 0 0.2 0.4 0.6 0 200 400 600 Average�actionofinfected Time (days) Time-stamps randomized 0 200 400 600 Time (days) 800 Randomized tim� & contacts, keeping activity & correlatio� Temporal effects for disease spreading
  50. 50. 0 0.2 0.4 0.6 0.01 0.1 1 Averageoutbreaksize Transmission rate 0.18 0.20 0.22 0.24 0.26 0.28 0 300 600 900 1200 Crossingpoint Initial Time (days) ρ* SIS Thresholds and convergence(?)
  51. 51. s.d. of c, σ avg.numberofpartners/year,c 0.5 1 1.5 2 0.5 1 1.5 2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 relativedifferenceinR₀,d Change in R₀
  52. 52. http://www.tp.umu.se/~holme/ Thank you! Luìs Enrique Correa da Rocha Christofer Edling Fredrik Liljeros

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