This document summarizes a study on social media use and its impact on gains from internet use in the Czech Republic. It found that daily social media users and non-users differed in their declared knowledge gains, contact with friends, and life satisfaction from internet use. However, the intensity of social media use itself did not seem to have a direct effect on gains. Instead, informational digital skills, variety of other online activities, and network capital were stronger predictors of gains from internet use.

1. Petr Lupač, Ph.D.
Charles University in Prague
World Internet Project
The Czech Republic
Financed due to Grant Agency of CZR (GA13-21024S)
„World Internet Project – The Czech Republic II“

2. System of
social
inequality
Unequal
Internet access
Unequal
gains/losses
The empirical evidence?

3. Source: Van Dijk, J. A. G. M. (2005) Deepening digital divide: Inequality in the information society. Thousand Oaks.
CA: Sage, p. 22; simplified for purposes of presentation

4. Using the Internet can either improve or worsen people's
lifes. When you think about your personal experience in
the last years, how much influences your Internet use
following areas of your life? Please, answer with the help
of a scale, where -5 means significant worsening and +5
means significant improvement.
[Scale:]
-5 -4 -3 -2 -1 0 1 2 3 4 5
(-5=Significant worsening 5 = Significant improvement)

5.  My knowledge of what's going on in the Czech republic
 My knowledge of what's going on in other countries
 My knowledge of what's going on in your locality
 My involvement in public life in your local community
 Dealing with state authorities {getting subsidy, welfare,
submitting documents, etc.}
 Contact with my family and my family life
 Contact with my friends and acquiantences
 My overall financial situation (i.e., your incomes and
expenses)
 Building up my career and my success on labor market
 Pursuing and developing my hobbies
 Overall satisfaction with my life

6. Rotated Component Matrixa
1 2 3 4 5 6 7
Change in knowledge, CZR ,239 ,858 ,139 ,090 ,042 ,085 ,124
Change in knowledge, world ,095 ,873 ,160 ,108 ,115 ,089 ,105
Change in knowledge, locality ,152 ,379 ,747 -,022 ,136 ,086 ,053
Change in involvement, local public life ,092 ,035 ,880 ,191 ,043 ,062 ,103
Change in dealing with state auth. ,121 ,139 ,110 ,116 ,117 ,954 ,061
Change in family life/contact ,840 ,062 ,115 ,189 -,005 ,112 ,074
Change in contact with friends ,797 ,252 ,070 -,115 ,167 ,076 ,163
Change in financial situation ,069 ,115 ,120 ,837 ,286 ,161 ,072
Change in career / labor market succ. ,098 ,130 ,131 ,243 ,918 ,122 ,065
Change in pursuing hobbies ,225 ,195 ,133 ,108 ,068 ,066 ,932
Change in overall satisfaction with life ,608 ,181 ,166 ,554 -,007 -,055 ,144
(4)
(5)
.
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 6 iterations
74 % of variance (5 factors), 83 % of variance (7 factors)

7.  The supposed role of variety/number of online activities and time
spent online (van Dijk 2005)
◦ -> hours online weekly, nr. of online non SNS activities performed weekly
 The important role of digital skills, age and education in gaining
benefits from Internet use (van Deursen, van Dijk and Peters 2011)
◦ -> operational skills index, informational skills index, age, education
 Previous social skills predict well sociability gains from Internet use
(rich-get-richer hypothesis findings; c.f., Lee 2009)
◦ -> ntw size index (via resource generator, sum of strong and weak ties)
 The role of bridging social capital in acquiring resources
◦ -> bridging = bonding * nr. of structural holes
 The role of network capital in explaining individual state (Wellman
and Frank 1999)
◦ -> share of Internet users in respondent’s social environment
 The ability to benefit from technological development (Rogers 2003)
◦ -> innovativeness index

8.  Donath & Boyd (2004)
◦ SNS use linked to higher amount of weak ties
 Ellison, Steinfiels & Lampe (2007)
◦ The role of FB intensity of use, self-esteem and life satisfaction in predicting
higher bridging social capital
◦ Compensation effect
◦ The role of FB intensity use confirmed by Steinfeld & Ellison (2008)
 Valenzuela, Park & Kee (2009)
◦ Intensity of FB use linked to higher life satisfaction and civic participation,
indirect link to social trust
 Burke, Kraut & Marlow (2011)
◦ The effect of FB use on bridging cpt. varies according to passive x active use
◦ No relation between FB use and bonding cpt.
 Lee, Noh & Koo (2013)
◦ Indirect effect of SNS use for lonely people via establishing strong ties
online

9. 0. SNS use and total gains from the Internet use are sig. correlated
0.1. SNS use and sociability gains from the Internet use are sig.
correlated
0.2. SNS use and knowledge gains from the Internet use are sig.
correlated
I. The intensity of SNS use predicts total gains from Internet use
after controlling for other variables
II. The intensity of SNS use predicts sociability gains from Internet
use after controlling for other variables
III. The intensity of SNS use predicts knowledge gains from Internet
use after controlling for other variables

10.  Pilot study in May
 Data collected in May and June 2014 by a specialized agency
 Method of data collection
◦ CAPI F2F interviews
◦ Stratified random sampling combined with quota sampling
◦ Measures taken to include parts of the population with lower probability of being
interviewed
 Respondents declaring no or very low interest in being interviewed pre-recruited from CAWI
panel (cca 8 % of the sample)
 Trained experienced interviewers instructed to deal with soft-rejection
 Financial incentives (computed or estimated from wage)
 100 % of the interviews were recorded, controlled and problematic respondents were excluded
 1316 respondents in the final sample, 79 % Internet users
 Weighted sample representative for the population of the Czech Republic,
age 15+
 A good fit of results with other data sources (WIP I, CZSO, Facebook)

11. Problem:
We do not know the share of non-questioned busy people in a
general population -> four steps to balance the sample
I. Weighting 92% of the sample (that was not pre-recruited) to fit the
sociodemographic structure of the Czech population
II. Finding the relations between sociodemographics and Internet
use/nonuse as well as the “pure” percentage of Internet users
III. Resulting Internet use added as a fixing variable to the weighting
procedure
IV. Weighting the whole sample by the following auxiliary variables
 Region (14 categories – NUTS3), Size of municipality (5 categories), Gender (2
categories), Age (6 categories), Education (4 categories), Age x education (30
categories), Employment status (6 categories), Attended 2013 elections (2
categories)
◦ 5 iterations, weighting range: 0,5 – 2,0

12.  Core questions
◦ Frequencies of 35 online activities
◦ Places of use, connection devices
◦ Self-disclosure and privacy behavior and attitudes
◦ Importance of the Internet as a source of entertainment and information
 Digital skills
◦ Operational, information, [strategic]
 Social capital
◦ proxy use, binding, bridging, network, structural holes, self-reported sociability
 Cultural capital
◦ Emerging, high-brow, reading books, active foreign language use
 Political capital and behavior
 Innovativeness
 Internet indispensability
 Time online weekly
 Preference of online communication
 Experience with the Internet use/nonuse affecting quality of life in 7
dimensions

13.  How often do you…
A. Visit social networking sites (i.e., Facebook, Google+, Twitter,
LinkedIn, Instagram)
B. Post messages or comments on social networking sites, for example on
Facebook or Twitter (if asked: also G+, Instagram, Linkedin, Lide.cz)
 Possible answers:
 Several times a day, daily, weekly, monthly, less than monthly,
never
 Transformations:
◦ SNSuse index = A+B (Min=1, Max=12; Cronbach α= 0,84; mean=6,3; SD=3,7)
◦ Logical types:
 Daily users: visiting at least daily AND posting at least daily
 Nonusers: Visiting never OR visiting less than monthly OR Posting never
 Weak users: the rest

14. INDEX
 Types of SNS users (% of CZR population 15+ ~ 8,9 mil.)
Inet nonusers (21 %) SNS nonusers (40 %) SNS avg users (22 %) SNS daily users (17 %)
1,9 mil 3,5 mil. 2,0 mil. 1,5 mil.
0
20
40
1 2 3 4 5 6 7 8 9 10 11 12
35,8
27,2
4,8
3,6
7,9
4,2
11,2
9,7
10,3
14,2
8,7
19,9
POSTING
VISITING
Never Less than monthly Monthly Weekly Daily Several times a day

15. SNS NONUSERS (A) SNS AVG USERS (B) SNS DAILY USERS (C)
Age 50BC (14) 35C (14) 30 (10)
Nr. of close friends 5 (4) 6 (4) 6 (4)
Hours online /week 15 (16) 22A (20) 31AB(22)
Nr. of non-SNS online
activities least weakly
7 (4) 12B (5) 16AB (5)
Share of soc.
environment using
the Internet
70 % (20) 76 % (20) 84AB % (20)
Life satisfaction 7/10 (2) 8/10 (2) 8/10 (2)
Mean (SD)
ABC - stat. sig. differences at α=.01 (Tukey post-hoc test)

17. Dependent ⇢
Independent ⇣ Total change
Social contact
change
Knowledge
change
Gender 0,04 -,60 ,08*
Age -,96** -,09* -,03
Education ,11*** -,03 ,09**
SNS use intensity ,18*** ,21*** ,10**
SNSuse (users only) ,10* ,10* ,04
Skills operational ,21*** ,17*** ,10**
Skills informational ,30*** ,22*** ,24***
Innovativeness ,20*** ,18*** ,15***
OA variety – no SNS ,29*** ,24*** ,23***
Time on Internet /weak ,20*** ,15*** ,16***
Bridging soc. cpt. ,16*** ,08* ,10***
Ntw. size ,21*** ,16*** ,14***
Ntw. cpt ,26*** ,15*** ,21***
Life satisfaction ,19*** ,13*** ,09**

18. Model 1 Model 2 Model 3 Model 4
Age -,025 ,025 ,029 ,056
Education ,106** ,038 ,028 ,007
SNS use intensity ,145*** -,072 -,071 -,052
Time spent online ,057 ,055 ,039
OA variety - no SNS ,244*** ,251*** ,22***
Operational skills -,003 ,001 ,011
Informational skills ,253*** ,239*** ,197***
Innovativeness -,039 -,075
Life satisfaction ,126*** ,108**
Bridging cpt. ,079*
Ntw. Size ,06
Ntw. capital ,148***
Adj. R2 ,03 ,13 ,15 ,17

19. Model 1 Model 2 Model 3 Model 4
Age ,030 ,068 ,071 ,088*
Education -,019 -,059* -,078* -,092**
SNS use intensity ,209*** ,092 ,071 ,084
Time spent online ,041 ,025 ,012
OA variety – no SNS ,120** ,042 ,020
Operational skills ,000 ,029 ,022
Informational skills ,175*** ,162*** ,136***
Innovativeness ,029 ,003
Life satisfaction ,090** ,075*
OA comm no SNS 0,14** ,141**
Bridging cpt. ,023
Ntw. size ,084*
Ntw. capital ,085*
Adj. R2 ,04 ,07 ,09 ,10

20. Model 1 Model 2 Model 3 Model 4
Age -,020 -,012 -,018 -,001
Education ,106** -,012 ,010 ,001
SNS use intensity ,081* -,026 -,037 -,030
Time spent online ,037 ,013 ,004
OA variety -,082 -,099 -,104
Operational skills ,134*** ,128** ,124**
Informational skills -,246*** -,206* -,187***
OA info ,385*** ,366*** ,353***
Innovativeness -,009 -,032
Netinfo ,202*** ,193***
Persinfo ,065* ,072*
Bridging cpt. ,063*
Ntw. Size ,022
Ntw. capital ,085**
Adj. R2 ,02 ,16 ,20 ,21

21.  Daily SNS users and SNS nonusers differ with respect to declared
Internet-induced changes in their
◦ Knowledge
◦ Contact with friend
◦ Life satisfaction
 Intensity of SNS use does not appear to have a direct,
independent effect on gains from Internet use
 The strongest predictors in all three cases seem to be
◦ informational digital skills
◦ variety or intensity of other online activities
◦ network capital
 Better predictor than online activities variety seem to be intensity
of relevant types of online activity

22. Petr Lupač, Ph.D.
PETR.LUPAC@FF.CUNI.CZ
@PetrLupac
Department of Sociology
Charles University in Prague, Faculty of Arts
Celetna 13, Prague
The Czech Republic