Peer Ratings in Massive Online Social Networks

Peer Ratings in Massive Online Social
Networks
Dmitry Zinoviev
Suffolk University
Boston
Sunbelt-2012

SUNBELT-2012 / SUFFOLK UNIVERSITY 2
To Like or not to Like?
● Like/dislike ratings provide instant peer feedback in massive
online social networks (MOSNs)
● Objects subject to ratings: posts; photographs; comments
● Questions:
➢ How many stops should rating scales have?
➢ How do MOSN users perceive the ratings?
➢ How do MOSN users react to the ratings?

Rating Types
● Single-valued (Facebook ”Like”, Google+ ”+1”); do
not allow negative attitudes
● Binary a.k.a. ”Thumbs Up/Down” (Pandora, Yahoo
Answers); do not allow fuzzy answers
● Multivalued (Yahoo, Amazon); may be hard to use
if there are many stops; these ratings are of
particular interest to us

“Odnoklassniki.Ru”—Our Testing Range
● Largest Russian-language social network (100 mln users, 31 mln
daily visits)
● Most users have more than one personal picture and reasonably
credible demographics (age/gender)
● 6-way ratings (“1” through “5+”*) applicable to personal pictures
● Anyone can leave comments to any personal picture of to the profile
in general
● The system records and displays all profile visits (and visitors)
*The grade of “5+” requires a symbolic payment from the grader

A Profile, as Seen by a Visitor
My PageMy Page
Personal PhotographsPersonal Photographs
DemographicsDemographics
FriendsFriends
Profile PictureProfile Picture

Picture Rating Interface
PicturePicture
Picture CommentsPicture Comments
DemographicsDemographics
This is a ”5+” pictureThis is a ”5+” picture
Your ratingYour rating
DescriptionDescription

Method
● Select a random avatar Q from the three avatars on the right
(30/Female, 40/Male, 30/Male)
● Select a random currently logged user and record the user's age
A (≥18) and gender G
● Select a random grade Rin
between “1” and “5+” and post it
● Record the user's response:
➢ did the user visited the test profile? (V)
➢ did the user post a comment? (C)
➢ what is the response grade Rout
?
●
Apologize to the user for a low grade. Posted grades and
comments can be easily removed without any traces

Descriptive Statistics
● 10,799 observations
● 3,600 observations per avatar
● 1,800 observations per grade
● Distribution by gender:
➢ 54% female
➢ 46% male
● Mean age 34
Russia in 2009

Empirical Parametrized Mapping
{Rout
, V, C} = f (Rin
; Q, A, G )
Free variable
Parameters

Response Details with Trend Lines
Responders, by kind and by age:

Response Statistics
All Responders
Of them:
Visitors Graders
Rate
M 47.0% 39.7% 19.7% 4.8%
F 42.4% 31.7% 19.5% 5.0%
M -0.1% 0.0% -0.4% -0.1%
F 0.5% 0.6% -0.1% 0.1%
Commenters
Age Trend
(%/year)
● Visits are the most common responses, followed by grades, followed
by comments
● Male subjects visit the experimenter's profile more often
● Older female visitors are more active than younger
● Younger male graders are more active than older
● Grading/commenting rates are gender-agnostic

OutGrade Distribution
● The grades of “5” and “5+” are grouped (“5+” is special because of its
associated price)
● Response grades have a sharp bi-modal distribution
● Only grades “1” and “5” matter!

OutGrades vs InGrades
● Response grades are sharply distributed around “1” and “5” for any
stimulus grade (the red lines show best-fit bi-exponential
distributions)
● Only two stops on the scale are necessary!

RG=R+G
● Response Grade = Reciprocity + Generosity
➢ Reciprocity: Social norm of in-kind responses to the behavior of
others (a.k.a. “eye for an eye”)
 Responding with the same grade
➢ Generosity: Habit of giving freely without expecting anything in
return
 Responding with a better of worse grade (positive
vs negative generosity)
● How popular are these mechanisms?

Reciprocity (1)
● Define reciprocity as the “fraction of reciprocally equal grades out of
all grades”:
● Calculate reciprocity for each avatar and for each age-gender group
● Calculate linear best-fit estimate
● Notation:
➢ Blue lines for male subjects, red lines for female subjects
➢ Solid lines for the 40/M avatar, dashed lines for 30/M, dotted lines
for 30/F
Rec=
N Rin=Rout
N

Reciprocity (2)
● Older subjects
are less
reciprocating
● Male subjects are
on average less
reciprocating
● Younger avatars
generate more
reciprocity from
the subjects of
the same gender

Generosity (1)
● Define generosity as the average value of the difference between the
stimulus grade and the response grade:
● It can be positive and negative
● Calculate generosity for each avatar and for each age-gender group
Gen=∑
i
Rout−Ri n
N

Generosity (2)
● Older subjects
are more
generous
● Older avatar
generates less
generosity
● Subjects are less
generous to the
avatars of the
same gender
● Younger avatars
generate less
generosity from
the subjects of
the same gender

Benevolence (1)
● Let negative comments have the value of -1, positive comments—the
value of 1, and neutral comments—the value of 0. Define
benevolence as the average value all comments
● Calculate benevolence for each avatar and for each age-gender
group

Benevolence (2)
● Older subjects
leave better
comments
● Female subjects
leave better
comments
● Younger avatars
get worse
comments from
the subjects of
the same gender

Grading/Commenting Overview (1)
Generosity G
Reciprocity R
Benevolence B
● Most behaviors are avatar-specific; however, the dependency on age
is universal

Grading/Commenting Overview (2)
● Generosity and benevolence grow with age
● Generosity and benevolence are weaker for same-gender
avatar-subject pairs
● Reciprocity slowly declines with age
● Reciprocity is stronger for same-gender avatar-subject pairs
● All three values can be approximated using quadratic functions

Latent Hostility
● Age 18–22: subjects are
all negative
● Age 22–36: subjects
combine positive
generosity and negative
benevolence: they post
higher grades but leave
negative comments
➢ Hypothesis: Higher
response grades make
subjects feel good, but
comments reveal true
feelings
● Age 36–80 subjects are all
positive
18–22
36–80
22–36

THANK YOU!

Peer Ratings in Massive Online Social Networks

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