Online social networks where the main purpose of interaction is the acquisition of specific resources of interest represent a promising venue for the study of social exchange. Sociological theories dating back to the 1960's predict that situations where the possession of resources is not uniform lead to power imbalances. Actors lacking a certain desired resource find themselves in a position of dependence on resource owners. According to Power-Dependence Theory, this power-unequal situation will tend toward balance, an outcome which could be reached in several ways. Among power-balancing mechanisms, \emph{status giving} figures as a way through which a low-power actor may lessen their dependence on a more powerful partner. This mechanism has not received a test in a large, real-world context, however. We analyze data from CouchSurfing.org, an international online hospitality exchange network, to confirm predictions regarding status giving at a massive scale never before addressed by previous work. We consider hosting as a social exchange process: the host offers a resource (i.e., hospitality) to other members (``surfers''), an exchange which creates a degree of dependence. We use mutual user-reported ratings to quantify status. Our investigation demonstrates a statistically-higher tendency of CouchSurfers to give status to hosts, especially when the effect of resource scarcity comes into play.

1. From Power to Status in Online Exchange
Bogdan State (Stanford, Sociology)
Bruno Abrahao (Cornell, Computer Science)
Karen Cook (Stanford, Sociology)

2. Research Question
• Does status accrue to powerful actors in networks?
• Status: esteem, high regard, appreciation, etc.
• Power: access to resources
• Worth exploring
– intuitive principle of social life
– basic process of social theory
– very difficult to measure in practice
– critical factor influencing evolution, growth, and the link structure of social
networks

3. Exchange Networks
• Social networks over which resources flow
• Broad definition of resources
– economic goods, advice, collaboration, and so on
– permeates social life
• Potential to gain deeper understanding of network structure

4. Exchange Theory [Emerson’62, Blau’64]
• Heterogeneity of resource endowments generates a structural
power imbalance
• Power-Dependence Principle:
PAB = DBA
– Powerful users are in a position to grant, deny, or hinder other’s gratification
– Relational definition of power: position in exchange network
• Power-Balancing:
– One-directional dependence is an unstable state
– Dependence induces behavior towards balance:
• Withdrawal
• Seeking Alternative Sources
• Coalition Formation
• Status Giving

5. Power to Status
• Statistical analysis of a sample of interaction dyads on
Couchsurfing.com
– International hospitality exchange network with over 4m members
– Users may choose to offer a place to sleep (a "couch") to others visiting their
area. Alternatively, they may choose to look for hosting themselves ("surf")
– Roles: hosts and surfers
– Key observation: We hypothesize that couches are the
kind of resources that may give power to hosts
– Question: Do couches make surfers dependent on host? If
so, Exchange Theory predicts some form of status giving.

6. Dataset
• Random sample of 80K post host interactions between
January 2003 and November 2011
• Constraint: "verified" users
• Chance to observe status giving through
– ratings of perceived friendship strength (reported to the other party)
– ratings of perceived mutual trust (collected confidentially)

7. Measure 1: Friendship Ratings (Public)
• Surfer-to-host friendship ratings greater in 13,432 dyads
– Reverse happens in 11,458
– Over half of ratings CS Friend - CS Friend: Anchoring

8. Measure 1: Friendship Ratings (Public)
• Surfer-to-host friendship ratings greater in 13,432 dyads
– Reverse happens in 11,458
– Over half of ratings CS Friend - CS Friend: Anchoring

9. Measure 1: Friendship Ratings (Public)
• Surfer-to-host friendship ratings greater in 13,432 dyads
– Reverse happens in 11,458
– Over half of ratings CS Friend - CS Friend: Anchoring

10. arty, Best 1 78 40 57 43 131 14 364
rat- Measure N.A. 47 Ratings 424 208 41 8 32 3,295
2: Trust 2,535 (Private)
Trust Total 541 56,043 13,396 6,571 1,444 347 1,852 80,194
ould
ions Table 3. Host and Surfer’s Reports of Trust
ngs, Host to Surfer to Host
Total
thin Surfer Do Not Somewhat Generally Highly Life N.A.
ddi- Do Not Trust 0 23 70 42 13 12 160
files Somewhat 39 1,218 4,595 3,003 269 548 9,672
tive, Generally 76 3,732 17,023 14,415 1,326 1,588 38,160
Highly 44 1,564 9,189 11,325 1,573 792 24,487
with Life 4 111 679 1,121 358 74 2,347
,194 N.A. 30 639 2,533 1,772 186 208 5,368
rfing Total 193 7,287 34,089 31,678 3,725 3,222 80,194
011.
ough
den-• Surfer-to-host ratings > than host-to-surfer in 25,329 dyads
traded between the two exchange partners. Plotted on the di-
usted – Reverse happens in16,559 cases
agonal are rating pairs of equal magnitude: above the diag-
the
then – 53% differenceinstances where surfers’ ratings were higher
onal we show
than the hosts, and below the diagonal we count cases where
. To
hosts’ ratings were higher. We can compare frequency counts
uces
between cells symmetrical to the diagonal. There were, for
ose a
instance, 7,579 cases where the surfer nominated the host as

11. arty, Best 1 78 40 57 43 131 14 364
rat- Measure N.A. 47 Ratings 424 208 41 8 32 3,295
2: Trust 2,535 (Private)
Trust Total 541 56,043 13,396 6,571 1,444 347 1,852 80,194
ould
ions Table 3. Host and Surfer’s Reports of Trust
ngs, Host to Surfer to Host
Total
thin Surfer Do Not Somewhat Generally Highly Life N.A.
ddi- Do Not Trust 0 23 70 42 13 12 160
files Somewhat 39 1,218 4,595 3,003 269 548 9,672
tive, Generally 76 3,732 17,023 14,415 1,326 1,588 38,160
Highly 44 1,564 9,189 11,325 1,573 792 24,487
with Life 4 111 679 1,121 358 74 2,347
,194 N.A. 30 639 2,533 1,772 186 208 5,368
rfing Total 193 7,287 34,089 31,678 3,725 3,222 80,194
011.
ough
den-• Surfer-to-host ratings > than host-to-surfer in 25,329 dyads
traded between the two exchange partners. Plotted on the di-
usted – Reverse happens in16,559 cases
agonal are rating pairs of equal magnitude: above the diag-
the
then – 53% differenceinstances where surfers’ ratings were higher
onal we show
than the hosts, and below the diagonal we count cases where
. To
hosts’ ratings were higher. We can compare frequency counts
uces
between cells symmetrical to the diagonal. There were, for
ose a
instance, 7,579 cases where the surfer nominated the host as

12. arty, Best 1 78 40 57 43 131 14 364
rat- Measure N.A. 47 Ratings 424 208 41 8 32 3,295
2: Trust 2,535 (Private)
Trust Total 541 56,043 13,396 6,571 1,444 347 1,852 80,194
ould
ions Table 3. Host and Surfer’s Reports of Trust
ngs, Host to Surfer to Host
Total
thin Surfer Do Not Somewhat Generally Highly Life N.A.
ddi- Do Not Trust 0 23 70 42 13 12 160
files Somewhat 39 1,218 4,595 3,003 269 548 9,672
tive, Generally 76 3,732 17,023 14,415 1,326 1,588 38,160
Highly 44 1,564 9,189 11,325 1,573 792 24,487
with Life 4 111 679 1,121 358 74 2,347
,194 N.A. 30 639 2,533 1,772 186 208 5,368
rfing Total 193 7,287 34,089 31,678 3,725 3,222 80,194
011.
ough
den-• Surfer-to-host ratings > than host-to-surfer in 25,329 dyads
traded between the two exchange partners. Plotted on the di-
usted – Reverse happens in16,559 cases
agonal are rating pairs of equal magnitude: above the diag-
the
then – 53% differenceinstances where surfers’ ratings were higher
onal we show
than the hosts, and below the diagonal we count cases where
. To
hosts’ ratings were higher. We can compare frequency counts
uces
between cells symmetrical to the diagonal. There were, for
ose a
instance, 7,579 cases where the surfer nominated the host as

13. arty, Best 1 78 40 57 43 131 14 364
rat- Measure N.A. 47 Ratings 424 208 41 8 32 3,295
2: Trust 2,535 (Private)
Trust Total 541 56,043 13,396 6,571 1,444 347 1,852 80,194
ould
ions Table 3. Host and Surfer’s Reports of Trust
ngs, Host to Surfer to Host
Total
thin Surfer Do Not Somewhat Generally Highly Life N.A.
ddi- Do Not Trust 0 23 70 42 13 12 160
files Somewhat 39 1,218 4,595 3,003 269 548 9,672
tive, Generally 76 3,732 17,023 14,415 1,326 1,588 38,160
Highly 44 1,564 9,189 11,325 1,573 792 24,487
with Life 4 111 679 1,121 358 74 2,347
,194 N.A. 30 639 2,533 1,772 186 208 5,368
rfing Total 193 7,287 34,089 31,678 3,725 3,222 80,194
011.
ough
den-• Surfer-to-host ratings > than host-to-surfer in 25,329 dyads
traded between the two exchange partners. Plotted on the di-
usted – Reverse happens in16,559 cases
agonal are rating pairs of equal magnitude: above the diag-
the
then – 53% differenceinstances where surfers’ ratings were higher
onal we show
than the hosts, and below the diagonal we count cases where
. To
hosts’ ratings were higher. We can compare frequency counts
uces
between cells symmetrical to the diagonal. There were, for
ose a
instance, 7,579 cases where the surfer nominated the host as

14. Resource Valuation
• Does status giving vary with resource valuation?
– Direct valuation
– Availability of alternatives
• 30,703 post-hosting rating pairs
• Collected host and surfer attributes (e.g., city)
• Observe standardized "CouchRequests" and whether or not
they were accepted

15. Resource Scarcity
• Relative Valuation and Availability of Alternatives
and status however. The Surfer's Linear Mixed-Effects Regression of Surfer’s Friendship Rating
- Response: Table 5. friendship rating of Host
of Host.
ource, the higher the valu-
n it, and therefore the more FIXED EFFECTS
heir hosts [6]. The scarcity Independent Variable Coefficient (S.E.) T-value
f in two ways. One is the Intercept 2.809⇤⇤⇤ 0.095 29.503
on to host, relative to other Surfer’s Trust Rating (hidden from host, ref: “Do not Trust”)
popular hosts receive more Trust Somewhat 0.102 0.084 1.215
ccept many requests (com- Generally Trust 0.233 ⇤⇤⇤
0.084 2.783
would be given less status. Highly Trust 0.494 ⇤⇤⇤
0.084 5.896
ty deals with the availabil- Would Trust With Life 1.049 ⇤⇤⇤
0.086 12.219
ence theory predicts that a Host’s Friendship Rating (reference: “Acquaintance”)
mand but where hosts, as a CouchSurfing Friend 0.080⇤ 0.043 1.882
aris) should value the hos- Friend 0.330 ⇤⇤⇤
0.044 7.505
Good Friend 0.700 ⇤⇤⇤
0.046 15.374
they had surfed in a low- Close Friend 1.371 ⇤⇤⇤
0.055 25.159
hus we expect status given Best Friend 1.933 ⇤⇤⇤
0.069 27.904
ted with the number of re- Relative Valuation of Host’s Resourced
y, but inversely correlated Requests received (logged) 0.002 0.007 0.299
sts. Requests accepted (logged) 0.017 ⇤⇤
0.008 2.143
of a novel mechanism im- Availability of Alternatives in the host’s city †
Ln(Req. received / host) 0.035⇤⇤⇤ 0.013 2.585
April 27, 2010. From that Ln(Req. accepted / host) 0.061 ⇤⇤
0.025 2.459
uraged to issue hospitality RANDOM EFFECTS
gh a standardized form, a
Variance Std. dev.
possible to observe whether Host Intercept 0.0288 0.1698
s request, and to compute Surfer Intercept 0.1664 0.4079
g a request. The focal in- Host’s City Intercept 0.0013 0.0364
he 36,156 post-interaction Residual 0.2285 0.4781
be matched with the pre- Source: CouchSurfing dataset. Sample size: 20,917 Scaled de-
surfer to host. We likewise viance: 39,322.29. Log-likelihood: -19,705.3. AIC: 39,446.6.
Notes: 1 was added to all logged quantities before taking the natural
only those CouchRequests

16. Resource Scarcity
• Relative Valuation and Availability of Alternatives
and status however. The Surfer's Linear Mixed-Effects Regression of Surfer’s Friendship Rating
- Response: Table 5. friendship rating of Host
of Host.
ource, the higher the valu-
n it, and therefore the more FIXED EFFECTS
heir hosts [6]. The scarcity Independent Variable Coefficient (S.E.) T-value
f in two ways. One is the Intercept 2.809⇤⇤⇤ 0.095 29.503
on to host, relative to other Surfer’s Trust Rating (hidden from host, ref: “Do not Trust”)
popular hosts receive more Trust Somewhat 0.102 0.084 1.215
ccept many requests (com- Generally Trust 0.233 ⇤⇤⇤
0.084 2.783
would be given less status. Highly Trust 0.494 ⇤⇤⇤
0.084 5.896
ty deals with the availabil- Would Trust With Life 1.049 ⇤⇤⇤
0.086 12.219
ence theory predicts that a Host’s Friendship Rating (reference: “Acquaintance”)
mand but where hosts, as a CouchSurfing Friend 0.080⇤ 0.043 1.882
aris) should value the hos- Friend 0.330 ⇤⇤⇤
0.044 7.505
Good Friend 0.700 ⇤⇤⇤
0.046 15.374
they had surfed in a low- Close Friend 1.371 ⇤⇤⇤
0.055 25.159
hus we expect status given Best Friend 1.933 ⇤⇤⇤
0.069 27.904
ted with the number of re- Relative Valuation of Host’s Resourced
y, but inversely correlated Requests received (logged) 0.002 0.007 0.299
sts. Requests accepted (logged) 0.017 ⇤⇤
0.008 2.143
of a novel mechanism im- Availability of Alternatives in the host’s city †
Ln(Req. received / host) 0.035⇤⇤⇤ 0.013 2.585
April 27, 2010. From that Ln(Req. accepted / host) 0.061 ⇤⇤
0.025 2.459
uraged to issue hospitality RANDOM EFFECTS
gh a standardized form, a
Variance Std. dev.
possible to observe whether Host Intercept 0.0288 0.1698
s request, and to compute Surfer Intercept 0.1664 0.4079
g a request. The focal in- Host’s City Intercept 0.0013 0.0364
he 36,156 post-interaction Residual 0.2285 0.4781
be matched with the pre- Source: CouchSurfing dataset. Sample size: 20,917 Scaled de-
surfer to host. We likewise viance: 39,322.29. Log-likelihood: -19,705.3. AIC: 39,446.6.
Notes: 1 was added to all logged quantities before taking the natural
only those CouchRequests

17. Discussion
• Evidence for the validity of Emerson's basic propositions in
online social networks
– Large-scale study
– Real-world data
– Global reach
• Privately-collected information shows more status giving than
publicly-collected ratings
• Status-giving as potential foundation of a different kind of
exchange system (e.g. Blau's advice networks)
• Potential implications for other organizations in the sharing
economy

18. Future Work
• Status beyond the dyad
- Does more status accrue to people who show willingness to host others?
• From Status back to Power
- Do high-status actors receive more access to resources?
• Status and Pro-Social Behavior
- Does a status increase cause one to contribute (host) more?
• Implications of Status-Giving for Evolution of Network
Topology
• Other data-sources: sharing economy, Wikipedia, OpenSource

19. ! ! ! ! Thank You!
From Power to Status in Online Exchange
Bogdan State (Stanford, Sociology)
Bruno Abrahao (Cornell, Computer Science)
Karen Cook (Stanford, Sociology)