The document discusses how social capital theory can help explain the social effects of the Internet. It argues that individuals and communities with higher levels of social capital, as measured by factors like generalized reciprocity and social ties, will be more socially active online as well as offline. Empirical evidence from studies in Los Angeles and of U.S. states supports this, finding those with more offline social connections and belonging were more likely to make online friends or participate in online groups. The document calls for more research examining social capital and online activity over time and across different communities.

1. Studying the social effects of the Internet with a “magnifying glass” Sorin A. Matei

8. Belonging Mass Communication On-line connections Metamorphosis research strategy Chinese Greater Monterey Park Mexican East LA Caucasian South Pasadena Korean Greater Koreatown Caucasian Westside African-American Greater Crenshaw Central American Pico Union Bilingual Telephone Interviews 1812 Households

37. Study started as part of an undergraduate research methods class Paper presented in Maastricht, at the 3rd Conference of Internet researchers, under review at the Journal of Broadcasting and Electronic Media Methodology Using states as units of analysis: Do states with higher capacity for producing social capital generate more on-line sociability?

40. Clubs per 100,000 inhabitants dependent variable

42. % Yes “Most people can be trusted”

44. Results The higher the social capital, the more numerous the groups The more homogeneous the population, the more numerous the groups Adjusted R 2 =.18 .01 -2.546 -.420 .032 -.08 Percent population foreign born .02 2.285 .327 .014 0.03 % Yes: Most people can be trusted β Std. Error B p t Standardized Coefficients Unstandardized Coefficients

46. Follow up analysis results Dependent variable: Yahoo! groups per 100,000 Adjusted R-square = . 12 The more numerous the non-profit organizations, the more numerous the on-line groups The more homogeneous the population, the more numerous the groups -- ns

55. Belonging and new/old media connectedness: a communication infrastructure model Connections to Community Organizations Local/Community Media Connections Participation in Interpersonal Storytelling BELONGING Internet connection Mainstream Mass Media Connections 1.8 1.7 5.6 1.4 1.6 Metamorphosis study: English-speaking samples 1.4

56. On-line sociability predicted by belonging ( dv: “Have you ever met someone on-line you consider a personal friend?”) Variable B S.E. Wald Sig Exp(B) BELONGING .0639 .0296 4.6612 .0309 1.0660 GENDER .5391 .3013 3.2019 .0736 1.7144 AGE -.0095 .0143 .4344 .5098 .9906 EDUC .1811 .1156 2.4549 .1172 1.1985 INCOME -.1030 .0863 1.4238 .2328 .9021 IMMIG.GEN. -.1362 .1296 1.1045 .2933 .8727 KOREATOWN 3.2065 1.3314 5.7996 .0160 24.6915 KOREAN/BELONG.-.1231 .0702 3.0737 .0796 .8842 CRENSHAW .2197 .5752 .1459 .7025 1.2457 ELA -1.2143 .8942 1.8441 .1745 .2969 MONTEREY PARK .5827 .5824 1.0010 .3171 1.7908 WESTSIDE .1334 .5405 .0609 .8051 1.1427 PICO UNION -.5566 .8220 .4586 .4983 .5731

58. Results Curvilinear relationship between trust and weighted average group activity Sparser populated states generate more active clubs Adjusted R 2 =.21 .012 -2.637 -2.634 .000 -.0007 % Answered Yes “Most people can be trusted” squared .028 2.292 2.277 .023 0.05 % Answered Yes “Most people can be trusted” .065 -1.905 -.315 .000 -0.0003 Population density Beta Std. Error B Sig. t Standardized Coefficients Unstandardized Coefficients

59. Curvilinear relationship between trust and on-line activity

60. Curvilinearity of relationship between trust and activity mirrored by that between off-line involvement and on-line involvement