# “Woman-Metal-White vs Man-Dress-Shorts”:
Combining Social, Temporal and Image Signals to
Understand Popularity of Pinterest Fashion Boards

Suman Kalyan Maity§, Anshit Chaudhary∗† and Animesh Mukherjee‡
§Northwestern University; ‡Dept. of CSE, IIT Kharagpur, India
Most of the work was done when the author was at IIT Kharagpur, India.

###### Abstract

Pinterest is a popular photo sharing website. Fashion is one the most popular
and content generating category on this platform. Most of the popular fashion
brands and designers use boards on Pinterest for showcasing their products.
However, the characteristics of popular fashion boards are not well-known.
These characteristics can be used for predicting popularity of a nascent
board. Further, newly formed boards can organize their content in a way
similar to the popular fashion boards to garner enhanced popularity. What
properties on these fashion boards determine their popularity? Can these
properties be systematically quantified? In this paper, we show how social,
temporal and image signals can together help in characterizing the popular
fashion boards. In particular, we study the sharing/borrowing behavior of pins
and the image content characteristics of the fashion boards. We analyze the
sharing behavior using social and temporal signals, and propose six novel yet
simple metrics: originality score, retention coefficients, production
coefficients, inter-copying time, duration of sharing and speed coefficients.
We further study the image based content properties by extracting fashion,
color and gender terms embedded in the pin images. We observe significant
differences across the popular (highly followed or highly ranked by the
experts) and the unpopular (less followed) boards. We then use these
characteristic features to early predict the popularity of a board and achieve
a high correlation of $0.874$ with low RMSE value. Our key observation is that
likes and repin retention coefficients are the most discriminatory factors of
a board’s popularity apart from the usage of various color, gender and fashion
terms.

## Introduction

Pinterest is an image-based online social network which has grown with
unprecedented pace attaining a mark of 110 million monthly active users. It
was also the fastest site to break the 10 million unique visitors
mark111https://techcrunch.com/2012/02/07/pinterest-monthly-uniques/. Although
Pinterest is fairly new in the social media gamut, it is being heavily used by
many big business houses like Etsy, The Gap, Allrecipes, Jettsetter, Nike,
Adidas etc. to advertise their products. Further, Pinterest drives more
revenue per click than Twitter or
Facebook222https://en.wikipedia.org/wiki/Pinterest. This stupendous growth
makes it interesting to study Pinterest.

### Fashion industry and social media

Social media is an amazing marketing tool for the fashion industry. Fashion
brands can leverage public perception available on social media over various
fashion items. The continuous feedback received by the fashion brands in the
form of likes and comments on their social media posts lets them gauge and
further viralize their product chain in the market. Image-based social media
platforms like Pinterest, Instagram have become popular venues for fashion
brand marketing and advertising.

### Role of Pinterest in fashion industry

Fashion is an integral part of Pinterest. All the major brands like Nike,
Adidas Originals, Dolce Gabbana, Louis Vuitton etc. have their presence on the
Pinterest platform. What are the characteristic features of these popular
brands? Do they bear certain signatures – social, temporal or image based –
that make them distinct from the not-so-popular ones?

In Pinterest, users save images (pins) and categorize them on different
boards. Thus, a board is an important entity in Pinterest, and it has various
influences on interest-driven pin propagation or pin sharing. Sharing is an
important aspect in social media. If one likes a content, one might tend to
use it in the same/modified form. On Pinterest, sharing and borrowing of pins
(images) from various boards is a routine phenomena. This motivates us to
consider sharing/borrowing behavior in understanding the popularity of the
fashion boards. Similarly, we hypothesize that the (image) content of the post
should also be a key factor determining the popularity of a fashion board.

There are multiple boards where Pinterest advertises various fashion contents.
In this paper, we study the popularity of fashion boards by analyzing their
originality, sharing/borrowing behavior, and the characteristics of the image
content.

### Research objectives and contributions

In this paper, we analyze a massive Pinterest dataset consisting of pins and
boards and make the following contributions.

  * •

We investigate three major factors that can potentially characterize
popularity of a fashion board: social, temporal (i.e., how pins are shared or
borrowed across boards) and image content characteristics (usage of fashion,
color and gender terms etc. in the image) of the pins belonging to a board.

  * •

We observe that generally popular fashion boards are able to make an existing
non-popular pin popular, whereas less popular fashion boards do not exhibit
this characteristic. Note that this is a very non-intuitive finding indicating
a non-assortative behavior (the ‘popular’ pins making the non-popular pins
popular) as opposed to what is usually observed in most social networks.

  * •

Another key observation is that same content in different boards achieve
different levels of popularity. If a pin has originated from a popular board,
it achieves higher popularity on the originating board than the subsequent
boards to which it gets shared possibly pointing to dampening of the
popularity due to re-sharing. In addition, pins keep getting shared for longer
durations in popular boards.

  * •

We perform extensive image analysis of the pins on the boards and extract
fashion, color, and gender terms from them. Popular fashion boards have more
female faces than the unpopular ones. Further, the popular boards have a rich
collection of pins in which both the gender co-appear. We also observe
significant characteristic differences between popular and unpopular boards in
the usage of color and fashion words.

  * •

Our characterization further helps us to predict whether a given fashion board
would become ‘popular’ or not. In precise, we attempt to predict the
popularity in terms of the future number of followers of the boards. We
achieve a very high correlation coefficient of 0.874 with very low RMSE. A
post-hoc analysis of the importance of the features indicates that the likes
and the repin retention coefficients are the most discriminative ones followed
by the color and gender terms embedded in the image.

### Lessons for newbie fashion houses

The insights gained from this work can highly impact the new and upcoming
fashion brands. For instance, allowing for more female faces or both male and
female faces together, certain color terms (white, black, blue, brown, pink
etc.) and color combinations (blue-pink, black-pink etc.) can increase the
chances of the boards getting popular. Also they could ‘engineer’ campaigns to
promote their boards in such a way that the originating boards are able to
retain the ‘likes’ and ‘repins’ of their pins in the face of constant sharing
of these pins.

## Related work

### Content characteristics, sharing, and engagement

Content sharing ensures user engagement and commitment in future (?). There
are diverse motivations to share content on social media (?). Apart from
network structure, the content matter also play important role in sharing.
Users may share useful content to appear knowledgeable or simply to help out
others (?). The emotional valence behind content also drive its extent of
being shared (?; ?).

Though there have been various studies on diffusion, sharing, engagement in
social media, very less work has been done in the domain of visual analysis of
image content. Hochman et al. (?) show differences in local color usage,
cultural production rate, varied hue intensity (blue-gray in New York vs red-
yellow in Tokyo) by analyzing images from New York and Tokyo posted on
Instagram. Bakhshi et al. (?) study the engagement characteristic of images
containing human faces. They observe that images with human faces in them,
have higher chances of receiving likes and comments. Bakhshi and Gilbert (?)
study the role of color in online diffusion of pins in Pinterest. They observe
that color significantly impacts the diffusion of images and adoption of
content. Red, purple and pink seem to promote diffusion, while green, blue,
black and yellow suppress it.

Popularity: There have been few studies in the domain of Fashion trend and
popularity. Sanchis-ojeda et al. (?) explore various statistical models using
clients’ temporal reaction to style units change for identification and
quantification of linear and cyclical fashion trends. Lee et al. (?) propose a
classifier for identification of fashion-related Twitter accounts whereas we
focus on understanding the popular fashion boards. Hessel et al. (?) propose a
relative popularity prediction framework based on content characteristics with
minimal influence of other external factors like timing effects, community
preferences, and social networks. Wu et al. (?) study the sequential
prediction of popularity for image posts using a deep learning framework by
incorporating temporal context and temporal attention into the framework.

### Fashion brand marketing

Yamaguchi et al. (?) study the effects of visual, textual, and social factors
on the popularity in a large real-world network focused on fashion. There are
several studies that focus on understanding the growing interest in social
media marketing (?; ?; ?). Manikonda et al. (?) study the influence of social
media in various behavior of fashion brand marketing. They also analyze
fashion brands’ audience retention and social engagement.

Color in affective marketing: There are several research works that have
studied the role of color in affective marketing. Most of these works focus on
various kinds of advertisements, for example, the research on role of specific
colors used in magazine ads (?; ?), the efficiency of color ads compared to
black and white ads (?; ?). There are also studies on understanding the
effects of colors on consumer responses (?; ?). This line of research suggests
that red backgrounds elicit greater feelings of arousal than blue ones,
whereas products presented against blue backgrounds are liked more than
products presented against red ones (?; ?). Gray et al. (?) study the
relationship between color coordination and ‘fashionableness’. They observe
that maximum fashionableness is attained by selecting a color combination that
is neither completely uniform, nor completely different, i.e., fashionable
outfits are those that are moderately matched, not those that are ultra-
matched (“matchy-matchy”) or zero-matched (“clashing”). This balance of
extremes supports Goldilocks principle regarding aesthetic preferences that
seeks to balance of simplicity and complexity.

### Studies on Pinterest

There have been several works on Pinterest.
Gender Roles: Gilbert et al. (?) perform analysis focusing on the influence of
gender, geography, language usage on Pinterest. They identify features of pins
that could predict the activity of the board. They observe that being female
on the site leads to more repins while having fewer followers. Our work draws
motivation from this work where we study various social, temporal and image
content factors as driving factors of popularity. Ottoni et al. (?) study
differences in gender role in platform usage and social interaction on
Pinterest. They observe that females invest more effort in reciprocating
social links, are more active and generalist in content generation whereas
male are more likely to be specialists and tend to describe themselves in an
assertive way. Also men and women possess different interests. Chang et al.
(?) study users’ topical specialization and homophily.
User interaction and experience: Zarro et al. (?) investigate professional and
personal uses of Pinterest with interview data and observations of online
activity. Zhong et al. (?) perform analysis on how borrowing behavior
facilitates social interactivity and experience. Yang et al. (?) study
recommendation of Pinterest boards for the Twitter users. Linder et al. (?)
investigate social and cognitive aspects of creativity that affect the digital
curation practices of everyday ideation with Pinterest users. Miller et al.
(?) study perception on Pinterest of the users and non-users and show that
there exist differences among these two groups and how exploring Pinterest
changes the non-users’ experience.
Diffusion, Popularity: Zhong et al. (?) study the impact of social ties on
Pinterest. Lo et al. (?) study user activity and purchasing behavior on
Pinterest for characterization of temporal user purchase intent. Lo et al. (?)
in another paper characterize the growth of Pinterest boards (size) and
analyze how initial growth can be used to predict future growth behavior. Han
et al. (?) study popular and viral image diffusion in Pinterest. Deeb-Swihart
et al. (?) study selfie presentation in everyday life on Instagram. You et al.
(?) study various spatio-temporal patterns of Facebook photographs as well as
its diffusion pattern via social ties.

The present work: Our study is different from the above ones in that we
analyze the popularity aspects of (fashion) boards and attempt to understand
its relationship with (i) social and temporal sharing/borrowing behavior and
(ii) the gender, fashion and color terms embedded in the images posted on
these boards. Our analysis sheds light on strategies and mechanisms an
upcoming fashion brand could adopt to make itself popular on Pinterest which
can eventually enhance their overall business.

## Pinterest terminologies and the dataset

### Entities on Pinterest

There are several entities on Pinterest. We shall provide a brief discussion
on the essential functional entities of Pinterest platform.

  * •

Pin: A pin (analogous to a post in Facebook or a tweet in Twitter) is an image
which is forms the basic building block of Pinterest. Pin is a visual
bookmark. Each Pin one can see on Pinterest site links back to the website it
came from. The activity related to posting a pin is known as ‘pinning’, and
the user who posts a pin is known as the ‘pinner’. Pins can be liked and
shared. Each of these pins has the following meta-data associated with it -
unique pin-id, description, number of likes, number of comments, number of
repins, board name, source, and content of the comments. Sharing an already
existing pin is referred to as ‘repinning’ (similar to retweet in Twitter).

  * •

Board: A board is a user-generated collection where one saves pins. Boards can
be made in secrecy or publicly. One can add collaborators to boards. Each
board has a url, a name, a description (optional) and a category (optional,
e.g. Art, Architecture, Celebrities, Food and Drink, Entertainment, Education,
Fashion etc.). This analogy of pins and boards replicates the real-world
concept of classifying images into photo albums.

### Dataset

The dataset used in this study contains information about 0.3 million boards
and their 63 million pins. We use Pinterest API v1 to crawl information about
boards and pins. Board information constitutes of the following: board
description, number of followers, and the creator. Pin information has the
following attributes: pin description, number of likes, number of comments,
number of repins, board name, and the creator. The data collection process is
divided into two parts as follows

#### Crawling of the massive dataset

We crawl a large dataset which should be useful for doing various analysis of
the fashion boards.

  * •

Initial pin collection: We initiate the data collection process by obtaining
the pin-id of 1000 pins from http://www.pinterest.com/popular/ by generating
automatic scrolls. Now, each pin-id of these pins are picked and the trailing
6 digits were permuted to generate new pin-ids. About 10 million new pin-ids
are generated by this process. Information of all these pins are crawled
separately.

  * •

Massive information collection: We extract the board-url of each of these pins
from the information crawled above. About 0.3 million unique board urls are
obtained. Now, for all the board-urls, board information and their individual
pin’s information are crawled. This results in 59 million unique pins out of a
total of 63 million pins. This massive dataset is used to find out the origin
of the pins.

#### Fashion boards dataset

We extract names of fashion boards from the following sources: i) Fashion
categories on Pinterest333https://www.pinterest.com/categories/ and ii) Expert
rankings from Ranker444http://www.ranker.com/list/world_s-top-fashion-
brands/business-and-company-info,
Mashable555http://mashable.com/2012/08/06/top-fashion-pinterest-accounts and
Stylecaster666http://stylecaster.com/fashion-pinterest-accounts. Finally, we
could obtain information about $\sim 3600$ fashion boards. We discard those
boards that have very less number of pins. A total of 4 million pins are found
on these 3600 boards. For each of these board urls, we crawl the detailed
information about the board and its pins from Feb, 2016 till March, 2016.

We then further categorize the 3600 boards above into following two categories
of popularity: popular boards and unpopular boards. In popular boards we
define two popularity classes - Highly Followed (socially ranked) Boards (HFB)
and Expert Ranked Boards (ERB). We denote the unpopular ones as the Less
Followed Boards (LFB). From the collection of the fashion boards, we assume
the top 20% most followed boards as HFB and the bottom 20% as LFB. We have
tried to use other percentage values also but choosing 20% allowed us to have
a sizeable data for conducting meaning experiments. The 1200 boards which we
obtain from the expert rankings are noted as expert ranked boards (ERB).

## Characterization of fashion boards

In this section, we shall discuss the various factors which characterize the
popularity of fashion boards. There are several factors that governs the
popularity of a board - the originality/novelty, sharing/borrowing behavior as
well as the content on the board (the image-characteristics of the pins).

### Originality

Originality/novelty of boards is an important aspect. If one observe the
creation of pins over the years (see figure 1), one can conclude that the
total no. of pins are continuously growing whereas the no. of unique pins have
increased only in the early few years but then started decreasing. This
indicates that originality in this social media is on a decline over time due
to heavy content sharing. Motivated by figure 1, we study board originality as
an indicator of popularity.

Figure 1:  Evolution of unique (i.e., absolute) and total number of pins
created per year.

Pin originality: Pins on a board can be classified into two types: original
pins and duplicate pins. If a pin has originated from the board $b$, then it
is called an original pin with respect to the board $b$ whereas, if a pin has
not originated from the board $b$, but is a result of a copy from another
board to $b$, then it is called a duplicate pin with respect to $b$.

Board originality score: Using the concept of pin originality, we define a
measure to compute the originality score of a board. Originality score
($orig_{score}$) of a board ($b$) can be defined as the ratio of the original
pins ($o_{b}$) on it to the total number of pins ($t_{b}$) on it.

$orig_{score}(b)$ = $\dfrac{o_{b}}{t_{b}}$

Originality score of a board lies in interval $[0,1]$. Boards having
originality score close to 1 constitute of mainly original pins, which means
that they are content generators. Boards having originality score close to 0
constitute of mainly duplicate pins, which means that they are content
copiers/consumers.

In figure 2(a), we observe that the originality scores are highly correlated
with follower count. We then group the boards in less followed, highly
followed and expert ranked boards and measure the originality scores in these
popularity buckets. We observe that originality scores of highly followed and
expert ranked boards are high, whereas that of less followed boards are on the
lower side. Thus, originality of a board is an important indicator of its
popularity.

Figure 2:  (a) Relationship between originality scores and follower counts of
boards. (b) Distribution of originality scores across the less followed,
highly followed and expert ranked boards. The K-S test for significance among
the relevant distributions are measured. ****,***,**,*, ns denote $p$-values
of significance to be $<$ 0.0001, $<$ 0.001, $<$ 0.01, $<$ 0.05 and non-
significant respectively. We have used the same notations for the subsequent
figures wherever applicable.

#### Originality of the top fashion brands

We further study the originality scores of the boards corresponding to the top
fashion brands. Toward this objective, we consider the top fashion clothing
brands777http://www.businessinsider.in/The-top-15-clothing-brands-millennials-
love/14-Under-Armour/slideshow/51080592.cms and attempt to compute their
originality scores. We separately collect the board information and the pins
of these top fashion clothing brands. We compute the originality score of
these boards and observe that they have highly original content (see table 1).

We then attempt to find the extent of correlation between the originality
scores of these boards with their popularity (in terms of the number of
followers). The Spearman’s rank correlation comes out to be $0.41$. This
establishes that there is a strong positive correlation between originality
and popularity of the top fashion brands.

Table 1:  Originality scores of top fashion brands

Rank | Brand Name | Originality Score
---|---|---
1 | Nike | 0.893333494064
2 | Target | 0.99560813134
3 | Adidas | 1.0
4 | Macy’s | 0.98539077817
5 | JCPenney | 0.992822203076
6 | Converse | 0.877229331742
7 | Van’s | 0.990441463251
8 | Ralph Lauren | 0.996835443038
9 | Forever 21 | 0.953847264232
10 | Victoria’s Secret | 0.988732432462
11 | Levi’s | 0.944628465324
12 | Chanel | 0.876407556394
13 | Under Armour | 0.897653427232
14 | Aeropostale | 0.916352963232

### Sharing/borrowing behavior

Sharing/borrowing of pins are very common on the Pinterest platform. We
introduce board retention coefficients and board production coefficients based
on the sharing/borrowing behavior dynamics of the pins on a board. On
Pinterest, the ‘social behavior’ of a pin can be measured based on three
factors: the number of likes, the number of repins and the number of comments
generated by the pin. We however observe that commenting is not practiced
extensively in this platform. Hence, we only take number of $likes$ and
$repins$ generated by a pin. The two coefficients we define next roughly
correspond to the direction and magnitude of flow of information from one
board to another. Each of them is independently able to portray meaningful
information about sharing.

#### Retention coefficients

Board retention coefficients are a novel set of measures concerning the
$like$/$repin$ ‘retention’ capabilities of a board. It addresses the question
that - how many likes/repins shall a board be able to retain if other boards
copy content from it. We calculate the likes retention coefficient using the
algorithm 1. Similarly, we also compute the repins retention coefficient.

Algorithm 1 Calculation of likes retention coefficient.

temp $\leftarrow$ [ ]

for each original pin $p$ on board $b$ do

$temp.append(\dfrac{1+likes\>of\>p\>on\>b}{1+avg.\>likes\>of\>p\>on\>other\>boards})$

end for

likes retention coefficient of board $b$ = average(temp)

Both the retention coefficient values lie in the interval $(1,\infty)$. If a
board $b$ has a higher likes (repins) retention coefficient, then the
subsequent boards that copy pins from this board shall be able to garner less
likes (repins) than the board $b$.

Figure 3:  Distribution of a) likes retention coefficient b) repins retention
coefficient (c) likes production coefficient b) repins production coefficient
for less followed, highly followed and expert ranked boards.

A significant fraction of highly followed and expert ranked boards have higher
retention coefficients compared to the less followed boards (see figure 3(a)
and (b)). Thus, the original pins on highly followed and expert ranked boards
are significantly more liked/repinned among their respective duplicate
(shared) pins, whereas the original pins on less followed boards are not
popular among their duplicate pins. Hence, the likes/repins of the content on
highly followed and expert ranked boards do not decline even after they are
duplicated through copying.

#### Production coefficients

Board production coefficients are a novel set of measures that capture the
like/repin production capacities of a board. It addresses the question that -
how many likes/repins shall other boards gain if they copy content from a
board. We compute the likes production coefficient using the algorithm 2.
Similarly, we also compute repins production coefficient.

Algorithm 2 Calculation of likes production coefficient.

temp $\leftarrow$ []

for each duplicate pin $p$ on board $b$ do

$temp.append(\dfrac{1+likes\>of\>p\>on\>its\>original\>board}{1+likes\>of\>p\>on\>b})$

end for

likes production coefficient of board $b$ = average(temp)

Both the production coefficients lie in the interval $(1,\infty)$. If a board
$b$ has a lower likes (repins) production coefficient, then the duplicate pins
on $b$ shall garner more likes (repins) compared to that on their board of
origin.

A vast majority of highly followed and expert ranked boards have lower
production coefficients than the less followed boards (see figure 3 (c) and
(d)). Thus, the duplicate pins on highly followed and expert ranked boards
generate more number of likes/repins compared to that generated on their
corresponding boards of origin. On the other hand, duplicate pins on less
followed boards generate less likes/repins compared to that in their
corresponding boards of origin. Hence, highly followed boards and expert
ranked boards are able to make an existing pin more liked/repinned by copying
it.

### Temporal dynamics of sharing/borrowing

In this section, we introduce two measures based on the temporal aspects of
sharing: inter-copying time and duration of sharing. In addition, we also
define speed coefficients that indicate the speed at which likes/repins are
gained.

Figure 4:  Distribution of a) inter-copying time for pins b) inter-copying
time for boards c) duration of sharing for pins d) duration of sharing for
boards e) likes speed coefficient f) repins speed coefficient for pins in less
followed, highly followed and expert ranked boards.

Inter-copying time is a measure defined for the original pins. This is
expressed as the average time-gaps between instances of sharing of an original
pin on the subsequent boards. For an original pin $p$, we compute ICT as
explained in algorithm 3. We now average the value of ICTs for all the
original pins on board $b$, and call this as inter-copying time of board $b$.

A significant number of pins belonging to highly followed and expert ranked
boards have a higher value of inter-copying time than pins on less followed
boards (see figure 4(a) and (b)). This shows that pins on less followed boards
have smaller time-gaps between consecutive shares as compared to pins on
highly followed and expert ranked boards.

Algorithm 3 Calculation of inter-copying time for an original pin $p$.

$temp$ $\leftarrow$ []

for each duplicate pin $p^{{}^{\prime}}$ generated from pin $p$ do

$temp$.append(time-stamp of $p^{{}^{\prime}}$)

end for

sort $temp$ in non-decreasing order

for each $i$ in range(0, len($temp$)) do

if $i$ == 0 then

$temp[i]$ $\leftarrow$ 0

else

$temp[i]$ $\leftarrow$ $temp[i]$ \- $temp[i-1]$

end if

end for

$ICT$ for pin $p$ $\leftarrow$ $average(temp)$

#### Duration of sharing (DoS)

Similar to inter-copying time, duration of sharing is also defined for
original pins. It can be interpreted as the life-cycle of sharing of a pin.
For an original pin $p$, we compute DoS as explained in algorithm 4. We now
average the value of DoSs for all original pins on board $b$, and call this as
duration of sharing of board $b$.

Algorithm 4 Calculation of duration of sharing for an original pin $p$.

$temp$ $\leftarrow$ []

for each duplicate pin $p^{{}^{\prime}}$ generated from pin $p$ do

$temp$.append(time-stamp of $p^{{}^{\prime}}$)

end for

sort $temp$ in non-decreasing order

$DoS$ for pin $p$ $\leftarrow$ $temp[len($temp$)-1]-temp[0]$

A large fraction of pins on highly followed boards and expert ranked boards
have high duration of sharing compared to the less followed boards (see figure
4(c) and (d)). Hence, a pin on highly followed and expert ranked boards is
likely going to have a longer life span than a pin on the less followed
boards.

#### Speed coefficients

In this section, we attempt to combine the likes/repins on a board with its
temporal characteristics. Toward this objective, we define likes and repins
speed coefficients as follows.

we compute likes speed coefficient as explained in algorithm 5. We then
average the value of likes speed coefficient for all the original pins on a
board $b$, and call this as the likes speed coefficient of board $b$. We
similarly calculate repins speed coefficient.

Algorithm 5 Calculation of likes speed coefficient for an original pin $p$.

$likes$ $\leftarrow$ []

for each duplicate pin $p^{{}^{\prime}}$ generated from pin $p$ do

$likes$.append(number of likes on $p^{{}^{\prime}}$)

end for

likes speed coefficient of $p$ $\leftarrow$ $\dfrac{sum(likes)}{DoS(p)}$

Speed coefficients are greater for highly followed and expert ranked boards
compared to the less followed boards (see figure 4(e) and (f))). Thus,
original pins on highly followed and expert ranked boards gain popularity much
more quickly than the original pins on less followed boards.

### Image-based content analysis

Pinterest being an image sharing social media, the characteristics of image
(pins) also should have an impact on their popularity. In this section, we
analyze the content characteristics of images (pins) on the various boards.
Toward this objective, we use densecap (?), an image captioning tool that
extracts salient regions from an image and describes them in natural language
(English). We perform tokenization, stemming, lemmatization and stop-words
removal of these generated ‘dense’ captions to obtain key tokens/phrases
demonstrating the image. We further group these tokens into three key types:
gender terms, fashion terms and color terms. Gender terms which we analyze are
male and female. We obtain an exhaustive listing of fashion terms from
Myvocabulary888https://myvocabulary.com/word-list/fashion-and-clothing-
vocabulary/. A universal set of all color terms are available in
Wikipedia999https://en.wikipedia.org/wiki/Lists_of_colors. The fashion and
color terms which we find both in the above respective listings and our data-
set are shown in table 2 and 3.

Table 2: Fashion terms in the dataset.

clothes | tshirt | skin | shirts | jacket
---|---|---|---|---
feathers | pillows | sunglasses | buttons | shoe
suit | curtains | skirt | leather | pants
trouser | striped | shorts | strap | dress
jeans | pillow | necklace | umbrella | bag

Table 3: Color terms in the dataset.

black | blue | white | brown | green | purple | red
---|---|---|---|---|---|---
yellow | grey | metal | wooden | pink | silver |

#### Gender term analysis

We analyze the occurrences of both the genders in the pins across all the
three categories of boards. In table 4, we report the occurrences of each
gender. The cell corresponding to Male and Less Followed Boards has a value of
0.45. This means that 0.45 fraction of pins belonging to less followed boards
have male faces on them. We observe that the number of female faces on highly
followed boards and expert ranked boards is high, whereas they are
significantly lower ($\sim$ 20% lower) in less followed boards. This shows
that having more female faces on a board could increase the popularity of a
board. Further, if a board has more female faces, it has more chances of being
listed in expert ranked boards.

Table 4: Fraction of pins on boards with various gender combinations.

Gender | LFB | HFB | ERB
---|---|---|---
Male only | 0.45 | 0.48 | 0.49
Female only | 0.49 | 0.57 | 0.61
Male-Female | 0.26 | 0.37 | 0.35

Another very interesting observation is that the more popular boards have a
higher fraction of pins containing both male and female faces together on a
single pin compared to the less followed boards.

In summary, highly followed boards and expert ranked boards have more female
faces than the less followed boards. Moreover, the boards in the former
category have a richer collection of pins that together feature faces of the
genders.

#### Fashion term analysis

We analyze the occurrences of various fashion terms in table 2 in the imagery
of the pins across all the three categories of boards. We compute number of
occurrences of the fashion terms from table 2 appearing in the pins across the
boards. We use the torso of this frequency distribution to extract the most
discerning fashion terms. We choose the top 10 fashion terms from the torso to
compute results in table 5. Each cell $(x,y)$ in the table represents the
fraction of pins belonging to a particular popularity class ‘y’ having the
fashion term ‘x’. Therefore, the cell value of 0.318 corresponding to shirt
and Less Followed Boards means that 0.318 fraction of pins belonging to less
followed boards have the fashion term shirt in them. We observe that 20% of
fashion terms have equal distributions in highly followed boards and expert
ranked boards whereas their distributions in the less followed boards are
quite different.

Table 5: Fraction of pins having top 10 fashion terms which are obtained from
the torso of the frequency distribution of all the fashion terms in table 2.
The boldface values indicate similar distribution among $ERB$ and $HFB$ but a
different distribution in LFB. We adopted this convention in the subsequent
tables also.

Fashion Terms |  | LFB
---
| HFB
---
| ERB
---
shirt | 0.318 | 0.389 | 0.330
bag | 0.221 | 0.291 | 0.312
dress | 0.221 | 0.179 | 0.225
pants | 0.210 | 0.154 | 0.216
shoe | 0.187 | 0.231 | 0.201
jacket | 0.159 | 0.148 | 0.139
umbrella | 0.158 | 0.139 | 0.146
necklace | 0.147 | 0.134 | 0.156
pillow | 0.143 | 0.131 | 0.153
jeans | 0.092 | 0.127 | 0.152

We further study the co-occurrences of fashion terms in more detail. In table
6, we report the number of occurrences of two co-occurring fashion terms in a
pin. Each cell $(x\textrm{-}z,y)$ in the table represents the fraction of pins
belonging to a particular popularity class ‘y’ having the co-occurring fashion
term ‘x-z’. Thus, the value of 0.0964 corresponding to jacket-trouser and Less
Followed Boards means that 0.0964 fraction of pins belonging to Less Followed
Boards have both jacket and trouser together in them. We observe that 70% co-
occurring bi-terms have almost similar distributions in highly followed boards
and expert ranked boards, whereas their distributions in the less followed
boards are very different from the other two.

Table 6: Fractions of pins having top 10 co-occurring bi-terms, which are
obtained from the torso of the distribution of the fashion terms in table 2.

Bi-terms |  | LFB
---
| HFB
---
| ERB
---
jacket-trouser | 0.0964 | 0.1276 | 0.1198
bag-umbrella | 0.0753 | 0.1243 | 0.1134
bag-striped | 0.0683 | 0.1223 | 0.1143
necklace-strap | 0.0879 | 0.1124 | 0.0953
jeans-shoe | 0.0762 | 0.1057 | 0.1049
bag-shorts | 0.1032 | 0.1242 | 0.0643
bag-trouser | 0.0923 | 0.1243 | 0.1142
leather-strap | 0.0923 | 0.1214 | 0.1203
dress-umbrella | 0.0812 | 0.1143 | 0.1043
dress-skirt | 0.1023 | 0.0854 | 0.1053

We also consider the co-occurring tri-terms (three fashion terms together). We
observe similar discriminating results for 60% tri-terms (see table 7). The
discrimination becomes more prominent when we use the co-occurring bi-terms
and tri-terms. We thus conclude that the collection of fashion terms together
used in pins affect the popularity of their boards. Hence, a popularity
seeking board can host images having a particular collection of fashion terms
from the above analysis.

Table 7: Fraction of pins having top 10 co-occurring tri-terms, which are obtained from the torso of the distribution of the fashion terms in table 2.  Tri-terms |  | LFB
---
| HFB
---
| ERB
---
leather-pillow-shirt | 0.1032 | 0.1343 | 0.1763
pants-strap-trouser | 0.0913 | 0.1132 | 0.1298
pants-shoe-skirt | 0.0613 | 0.1232 | 0.1265
jeans-leather-pants | 0.1265 | 0.0942 | 0.1135
jeans-shirt-shorts | 0.1175 | 0.0823 | 0.0732
bag-necklace-skirt | 0.0786 | 0.0974 | 0.1296
dress-shirt-sunglasses | 0.0874 | 0.0925 | 0.1145
bag-pants-trouser | 0.0874 | 0.1134 | 0.1341
bag-shirt-umbrella | 0.0112 | 0.1324 | 0.1142
dress-pants-shorts | 0.0931 | 0.1121 | 0.1321

#### Color term analysis

Colors are a very important factor in fashion (?). In this section, we analyze
the occurrence of color terms from table 3 appearing in the pins across all
three categories of boards. We compute the number of occurrences of each color
term, co-occurring bi-terms generated from the colors in table 3. We choose
the top 10 color terms (once again, from the torso of the frequency
distribution) in table 8. We observe that 30% color terms have similar
distributions in the highly followed boards and expert ranked boards, whereas
their distributions in the less followed boards are different from the other
two. White, black and blue are found to be the three mostly used color terms
whereas purple is the least favored one.

In table 9, we show the occurrence distribution of the top 10 most co-
occurring color bi-terms from the torso of the frequency distribution. Once
again, we obtain a better discrimination while using bi-terms over single term
occurrences (40% over 30%). Black-yellow and blue-yellow are the most co-
occurring color terms for the less followed boards whereas black-pink and
pink-red are the most dominating color terms occurring together for the highly
followed boards in the torso region. For the expert ranked boards, blue-pink
and pink-red are the most used color terms whereas blue-white, black-white,
blue-black are the most dominant color combinations in all the three
categories of boards when we consider the whole distribution. Therefore, we
observe that the color composition of images (pins) affect the popularity of
their boards. Hence, a popularity seeking board can host images having a
particular color composition from the above analysis.

Table 8: Fractions of pins having top 10 color terms which are obtained from the torso of the frequency distribution of all colors terms mentioned in table 3. Color |  | LFB
---
| HFB
---
| ERB
---
white | 0.638 | 0.664 | 0.696
black | 0.555 | 0.581 | 0.538
blue | 0.543 | 0.566 | 0.492
brown | 0.497 | 0.379 | 0.389
red | 0.346 | 0.244 | 0.262
wooden | 0.338 | 0.218 | 0.236
green | 0.224 | 0.221 | 0.233
metal | 0.256 | 0.205 | 0.198
pink | 0.122 | 0.098 | 0.086
purple | 0.012 | 0.008 | 0.012
Table 9: Fractions of pins having top 10 co-occurring bi-terms, which are
obtained from the torso of the distribution of all possible color bi-terms
from table 3.

Color bi-terms |  | LFB
---
| HFB
---
| ERB
---
black-yellow | 0.1043 | 0.0745 | 0.0943
blue-pink | 0.0744 | 0.0935 | 0.1064
black-pink | 0.0824 | 0.1053 | 0.1034
metal-red | 0.0743 | 0.0723 | 0.1053
blue-yellow | 0.1024 | 0.0923 | 0.0814
blue-silver | 0.0908 | 0.0956 | 0.0932
pink-red | 0.0824 | 0.1025 | 0.1057
metal-silver | 0.0823 | 0.0675 | 0.0723
red-yellow | 0.0923 | 0.0814 | 0.0774
grey-white | 0.0452 | 0.0423 | 0.0424

#### Gender infused fashion analysis

In this section, we shall study gender based usage of fashion and color terms
across the three board categories. In table 10, we show the gender based usage
of the fashion terms (bi-terms). Each cell $(x,y)$ in the table represents the
fraction of pins belonging to a particular popularity class ‘y’ having the
gender (‘g’) based fashion bi-term ‘a-b’. Here ‘x’ corresponds to ‘g-a-b’. For
example, the cell value corresponding to man-bag-jeans and Less Followed
Boards means that 0.1375 fraction of pins belonging to less followed boards
have male, bag and jeans in them. We observe that 50% of combinations have
almost equal distributions in the highly followed boards and expert ranked
boards, whereas their distributions in the less followed boards are very
different from the other two. Such differences in the combination of gender
and fashion terms affect the popularity of boards. It is seen that some
combinations increase the popularity of boards, whereas rest decrease it.
Another interesting observation we obtain from this analysis is that all the
40% combinations which have woman in them correspond to more popular boards.

Table 10: Fractions of pins having top 10 gender-based fashion bi-terms.

Gender and Fashion Bigrams | LFB | HFB | ERB
---|---|---|---
man-bag-jeans | 0.1375 | 0.1323 | 0.1250
man-dress-shorts | 0.1175 | 0.1442 | 0.1525
woman-bag-jeans | 0.1275 | 0.1567 | 0.1489
woman-bag-strap | 0.1325 | 0.1578 | 0.1652
woman-shirt-striped | 0.1450 | 0.1682 | 0.1575
man-bag-shoe | 0.1675 | 0.1324 | 0.1434
woman-bag-shoe | 0.1424 | 0.1550 | 0.1523
woman-shirt-skirt | 0.1375 | 0.1576 | 0.1503
woman-necklace-pants | 0.1324 | 0.1425 | 0.1232
man-shirts-shorts | 0.1453 | 0.1486 | 0.1502

#### Gender infused color analysis

In table 11, we show the distribution of top five gender-based color bi-terms
among pins across the three board categories. We observe that $\sim$60%
combinations have equal distributions in highly followed boards and expert
ranked boards, whereas their distributions in less followed boards are
different from the other two. Though black-metal is the dominant color
combination for female in both less and highly followed boards, white-metal is
the prominent color combination in expert ranked boards. In general, metal
colors seem to go very well with women.

Table 11: Fractions of pins having top five gender-based co-occurring color
bi-terms.

Gender-Color Trigrams |  | LFB
---
| HFB
---
| ERB
---
woman-metal-white | 0.2853 | 0.2753 | 0.2895
woman-pink-white | 0.2514 | 0.2657 | 0.2644
man-pink-white | 0.2425 | 0.2400 | 0.2350
woman-black-metal | 0.2850 | 0.2675 | 0.2643
woman-brown-green | 0.2325 | 0.2184 | 0.2135

## Prediction model

The previous section demonstrates how several factors serve as indicators of
popularity of the fashion boards on Pinterest. In this section, we shall
leverage these factors to predict the future popularity of fashion boards. The
popularity of a board is governed by the number of followers it has. To
prevent any from of data leakage we separately re-crawl the new follower
counts of all the fashion boards in our dataset in the month of April, 2017.
This follower count statistics therefore is at a distance of 12 months from
the training data. 101010Note that we do not use temporal statistics between
March 2016 and April 2017 for enhanced robustness of the model; the idea is to
make efficient predictions using minimal set of features that can be easily
obtainable at any static time point.

For the prediction task, we shall use the following features each of which is
motivated by the analysis in the previous section.

  * •

Originality score;

  * •

Likes retention coefficient;

  * •

Repins retention coefficient;

  * •

Likes production coefficient;

  * •

Repins production coefficient;

  * •

Total number of pins;

  * •

Avg. no. of likes on pins;

  * •

Avg. no. of repins of pins;

  * •

Avg. no. of comments on pins;

  * •

Inter-copying time;

  * •

Duration of sharing;

  * •

Likes speed coefficient;

  * •

Repins speed coefficient;

  * •

Gender counts (2 bins); Gender bi-term count;

  * •

Fashion term count (10 bins); Fashion bi-term count (10 bins); Fashion tri-
term count (10 bins);

  * •

Color term count (10 bins); Color bi-term count (10 bins);

  * •

Gender infused fashion bi-term count (10 bins); Gender infused fashion tri-
term count (10 bins); Gender infused color count (5 bins).

### Predicting the popularity class of the boards

We have seen that the factors we have discussed earlier highly discriminate
the unpopular class (LFB) from the two popular classes (HFB and ERB). The
factors, however, can only moderately discriminate one of the popular class
(HFB) from the other (ERB). We consider equal number of data points for each
of the classes and then perform a 10-fold cross validation for generating
results. In table 12, we present the classification results for i) HFB vs LFB
ii) ERB vs LFB and iii) HFB vs ERB. As evident from the table, we can
discriminate both popular (HFB or ERB) from the unpopular class (LFB) very
well with a very high accuracy (95.96% for HFB vs LFB and 93.95% for ERB vs
LFB) and very high precision, recall and area under ROC curve. Note that we
are only able to obtain a moderate accuracy (65.1%) in classifying the two
popular classes since the boards belonging to these two classes have very
similar characteristics.

For the classification task, we have used Support Vector Machines (SVM),
Logistic Regression (LR) and Random Forest (RF) classifiers implemented in the
Weka Toolkit (?). We choose the three classifiers for their diversity since
they are known to be able to solve a vast range of different types of
classification problems. Each of these classifiers represent different schools
of thoughts and have their own set of strengths and
advantages111111https://bit.ly/2LkuSf0. All the classifiers yield similar
performance results with Random Forest classifier performing the best.

Table 12: Performance of various classifiers for classification of i) HFB vs LFB ii) ERB vs LFB ii) ERB vs HFB. Categor-ies |  Classif-iers |  Accu-racy |  Preci-sion |  Recall |  F-Score |  ROC Area
---|---|---|---|---|---|---
HFB vs LFB |  SVM |  92.51% |  0.935 |  0.925 |  0.925 |  0.928
LR |  94.91% |  0.95 |  0.949 |  0.949 |  0.981
RF |  95.96% |  0.96 |  0.96 |  0.96 |  0.995
ERB vs LFB |  SVM |  92.06% |  0.921 |  0.921 |  0.921 |  0.92
LR |  93.27% |  0.934 |  0.933 |  0.933 |  0.977
RF |  93.95% |  0.94 |  0.94 |  0.94 |  0.99
ERB vs HFB |  SVM |  61.08% |  0.62 |  0.611 |  0.603 |  0.611
LR |  65.1% |  0.655 |  0.651 |  0.649 |  0.684
RF |  64.81% |  0.672 |  0.648 |  0.636 |  0.625

### Predicting the followership counts of the boards

To study the robustness of our prediction model, we further try to predict the
actual popularity, i.e., the logarithmic values of the followership counts of
the boards. Toward this objective, we use Support Vector Regression (SVR) due
to non-linearity of the problem. We use sequential minimal optimization (SMO)
algorithm for training the SVR. We perform both separate training and testing
as well as 10-fold cross validation method. We consider Pearson VII function-
based universal kernel (PUK) due to its flexibility and adaptability through
adjusting kernel parameter. We set the cost parameter (C) as 1. For evaluating
how good the prediction is, we use Pearson correlation coefficient ($\rho$),
normalized root mean square error (RMSE). We achieve high correlation
coefficient ($0.8738$) and low normalized root mean square error ($0.1427$)
which establishes the fact that the features obtained are robust and
discriminating in nature (see table 13121212We have also tried linear
regression model which gives correlation coefficient of 0.7363 and 0.2 as
normalized the RMSE value for 10-fold cross validation setting.). Both cross-
validation and separate training/testing produces very similar results.

Table 13: Regression results.

Method | $\rho$ | Normalized RMSE
---|---|---
10-fold cross-validation | 0.8659 | 0.146
Separate training/testing (4:1 ratio) | 0.8738 | 0.1427

Discriminative features: In order to determine the discriminative power of
each feature, we use the $RELIEFF$ feature selection algorithm (?) available
in the Weka Toolkit. Table 14 shows the rank of the features in terms of their
discriminating power for prediction. The rank order clearly indicates that for
popularity prediction the sharing/borrowing features, the color terms and some
of the fashion terms are important. Likes retention coefficient, repins
retention coefficient are the top discriminative features followed by various
color term based features. Therefore, color (sometimes in conjunction with
fashion and gender term) seems to be one of the most important discriminator
for popularity of fashion boards.

Table 14: Top predictive features and their ranks. Rank | Features
---|---
1 | LRC
2 | RRC
3 | bag-striped (fashion)
4 |  white (color)
5 | black (color)
6 |  blue (color)
7 | female
8 | male-female
9 |  brown (color)
10 |  blue-pink (color)
11 |  pink (color)
12 | black-pink (color)
13 | woman-pink-white (gender-color)
14 |  red (color)
15 | man-shirts-shorts (gender-fashion)

## Discussions and conclusions

In this section we outline various insights and implications of the current
work. We also discuss the generalizability of the current work and finally
draw the conclusions.

### Insights and implications

Insights: The current study puts forward a lot of insights especially for new
and upcoming fashion brands.

  * •

Certain social sharing behavior of users can make boards popular. The most
crucial among these are the retention coefficients. Popular boards are able to
retain their proportion of ‘likes’ and ‘repins’ despite a lot of sharing and
re-sharing of pins. Specially, engineered campaigns by the fashion houses can
be made to ensure/promote such retentions.

  * •

More female faces or both male and female faces together may be promoted by
the fashion houses since that, as we have seen, could lead to enhanced
popularity.

  * •

Certain choices of colors (e.g., white, black, blue, pink etc.) and color
combinations (e.g., blue-pink, black-pink etc.) may be more advertised to
enhance the chances of being more popular.

  * •

Certain fashion items like ‘striped bags’ seem to be very common in popular
brands and could be more promoted by the newbies.

  * •

For male fashion, ‘shirts’ and ‘shorts’ are the items that seem to propel
popularity and can therefore be more vigorously advertised by the new outlets.
Many articles131313https://www.cosmopolitan.com/sex-love/a63297/things-hot-
guys-wear/, in fact, have noted that shorts like boxers and bathing suits that
end above the knee enhance the sex appeal of men.

  * •

For female fashion, colors like pink and white seem to be good indicators of
popularity. In fact, pink has been the most favorite color of garments for
women for a very long time141414https://www.racked.com/2015/3/20/8260341/pink-
color-history. These therefore can be items of more focused publicity by the
upcoming fashion agencies.

Implications: Our findings make several contributions to existing research. We
believe, this research opens new pathway to understand new factors like
colors, faces, fashion terms which are influential for understanding
popularity. Our work also echoes some of the previous findings on impact of
color on diffusion. We also suggest color combinations that makes a board
popular. For newbie fashion houses and fashion trend-setters, our findings
shed light on how images can be constructed so that they become popular. Pins
of certain colors, more female faces or male-female joint faces could be some
of the prime suggestions. One could also launch campaigns to promote their
boards in such a way that the originating boards are able to retain the
‘likes’ and ‘repins’ of their pins in the face of constant sharing of these
pins. In fact, Pinterest can make such ‘tips-n-tricks’ application available
in exchange of a small amount of subscription from every newbie. This could
potentially be a premium/paid support and could be a business model for the
company for possibility of enhanced revenues.

There are several mobile apps which provide users with photo-editing tools.
One of the widely used techniques in photo-editing is applying filters to
them. These filters can change saturation, brightness, and color distribution
of the image. Our findings can be used to design new filters for photo
editing. Filters that increase saturation or enhance the warmness of the image
will likely increase engagement with the photo.

### Generalizability

Though the entire study has been performed on Pinterest, the findings can be
generalized in other similar websites focused on images, for example,
professional photography site like Flickr, or people-focused website like
Instagram. Instagram is also a quite popular website for fashion trend. We
believe these findings in the form of importance of color combinations and
fashion terms influencing popularity can be generalized to Instagram, Flickr
and Tumblr as well, though the popularity figures might vary which is mostly
dependent on the website’s underlying usage among communities, ranking
algorithms etc.

### Conclusions

In summary, we study various aspects of fashion boards on Pinterest. Our
proposed measures – retention coefficients, production coefficients, inter-
copying time and duration of sharing portray the sharing dynamics evident in
less followed, highly followed and expert ranked fashion boards.

We observe that generally highly followed and expert ranked fashion boards are
able to make an existing non-popular pin popular, whereas less popular fashion
boards do not exhibit this characteristic. Further, if a pin has originated
from highly followed or expert ranked fashion boards, it would achieve high
popularity on this board than the subsequent boards on which it would be
shared in future. We also observe that the pins on the highly followed and
expert ranked fashion boards keep getting shared for a long time, whereas this
happens for a short time for the pins on less followed fashion boards.

Gender, fashion and color terms embedded in images also yield interesting and
conclusive results. We observe that both highly followed and expert ranked
fashion boards exhibit similar trend in the usage of fashion bi- and tri-
terms. We also observe that a large number of pins having female faces are
present in highly followed and expert ranked fashion boards; the number of
female faces is 20% lower for the less followed boards. Similar trend is
observed for pins having both male and female faces. We also study occurrences
of gender-based fashion and color terms. We identify combinations which give
good discriminatory results across the three board categories. We try to
leverage various sharing/borrowing characteristics, image-based content
characteristics of fashion boards to predict their future popularity
(logarithm of follower count). We achieve a high correlation coefficient of
$0.874$ and low RMSE.

Limitations: We acknowledge that there is some limitation of the current
study. We specifically note the fact that some of the features and outcomes
might be influenced by the particulars of the Pinterest ranking algorithms
(e.g., what gets featured on the homepage, how personalization affects the
probability a pin will be surfaced, etc.). There is no way we can control the
internal algorithm promoting pins and boards. However, we believe, the factors
we come up with are indeed influential as they strongly correlate with
popularity studied on a large-scale data.

## References

  * [Bakhshi and Gilbert 2015] Bakhshi, S., and Gilbert, E.  2015\.  Red, purple and pink: The colors of diffusion on pinterest.  PloS one 10(2):e0117148.
  * [Bakhshi, Shamma, and Gilbert 2014] Bakhshi, S.; Shamma, D. A.; and Gilbert, E.  2014\.  Faces engage us: Photos with faces attract more likes and comments on instagram.  In CHI ’14, 965–974.  ACM.
  * [Bellizzi, Crowley, and Hasty 1983] Bellizzi, J. A.; Crowley, A. E.; and Hasty, R. W.  1983\.  The effects of color in store design.  Journal of retailing.
  * [Berger and Milkman 2012] Berger, J., and Milkman, K. L.  2012\.  What makes online content viral?  Journal of marketing research 49(2):192–205.
  * [Burke, Marlow, and Lento 2009] Burke, M.; Marlow, C.; and Lento, T.  2009\.  Feed me: Motivating newcomer contribution in social network sites.  In CHI ’09, 945–954.
  * [Chang et al. 2014] Chang, S.; Kumar, V.; Gilbert, E.; and Terveen, L. G.  2014\.  Specialization, homophily, and gender in a social curation site: Findings from pinterest.  CSCW ’14.
  * [Crowley 1993] Crowley, A. E.  1993\.  The two-dimensional impact of color on shopping.  Marketing letters 4(1):59–69.
  * [Deeb-Swihart et al. 2017] Deeb-Swihart, J.; Polack, C.; Gilbert, E.; and Essa, I.  2017\.  Selfie-presentation in everyday life: A large-scale characterization of selfie contexts on instagram.  In ICWSM, 42–51.
  * [Dubois and Duquesne 1993] Dubois, B., and Duquesne, P.  1993\.  The market for luxury goods: Income versus culture.  European Journal of Marketing 27(1):35–44.
  * [Forte, Hall, and Zarro 2013] Forte, M. Z. C. H. A.; Hall, C.; and Zarro, M.  2013\.  Wedding dresses and wanted criminals: Pinterest. com as an infrastructure for repository building.  ICWSM ’13, 650–658.
  * [Gilbert et al. 2013] Gilbert, E.; Bakhshi, S.; Chang, S.; and Terveen, L.  2013\.  “i need to try this”?: A statistical overview of pinterest.  CHI ’13, 2427–2436.
  * [Gray et al. 2014] Gray, K.; Schmitt, P.; Strohminger, N.; and Kassam, K. S.  2014\.  The science of style: In fashion, colors should match only moderately.  PloS one 9(7):e102772.
  * [Hall et al. 2009] Hall, M.; Frank, E.; Holmes, G.; Pfahringer, B.; Reutemann, P.; and Witten, I. H.  2009\.  The weka data mining software: An update.  SIGKDD Explor. Newsl. 11(1):10–18.
  * [Han et al. 2017] Han, J.; Choi, D.; Joo, J.; and Chuah, C.-N.  2017\.  Predicting popular and viral image cascades in pinterest.  In ICWSM, 82–91.
  * [Hessel, Lee, and Mimno 2017] Hessel, J.; Lee, L.; and Mimno, D.  2017\.  Cats and captions vs. creators and the clock: Comparing multimodal content to context in predicting relative popularity.  In WWW ’17, 927–936.
  * [Hochman and Schwartz 2012] Hochman, N., and Schwartz, R.  2012\.  Visualizing instagram: Tracing cultural visual rhythms.  In SocMedVis in conjunction with ICWSM ’12, 6–9.
  * [Jamali and Rangwala 2009] Jamali, S., and Rangwala, H.  2009\.  Digging digg: Comment mining, popularity prediction, and social network analysis.  In WISM ’09, 32–38.  IEEE.
  * [Johnson, Karpathy, and Fei-Fei 2016] Johnson, J.; Karpathy, A.; and Fei-Fei, L.  2016\.  Densecap: Fully convolutional localization networks for dense captioning.  CVPR ’16.
  * [Kim and Ko 2010] Kim, A. J., and Ko, E.  2010\.  Impacts of luxury fashion brand’s social media marketing on customer relationship and purchase intention.  Journal of Global Fashion Marketing 1(3):164–171.
  * [Kim and Ko 2012] Kim, A. J., and Ko, E.  2012\.  Do social media marketing activities enhance customer equity? an empirical study of luxury fashion brand.  Journal of Business Research 65(10):1480–1486.
  * [Kononenko, Simec, and Robnik-Sikonja 1997] Kononenko, I.; Simec, E.; and Robnik-Sikonja, M.  1997\.  Overcoming the myopia of inductive learning algorithms with relieff.  Applied Intelligence 7:39–55.
  * [Lee and Barnes 1989] Lee, S., and Barnes, J. H.  1989\.  Using color preferences in magazine advertising.  Journal of Advertising Research.
  * [Lee and Ma 2012] Lee, C. S., and Ma, L.  2012\.  News sharing in social media: The effect of gratifications and prior experience.  Computers in Human Behavior 28(2):331 – 339.
  * [Lee et al. 2017] Lee, D. J.-L.; Han, J.; Chambourova, D.; and Kumar, R.  2017\.  Identifying fashion accounts in social networks.
  * [Linder, Snodgrass, and Kerne 2014] Linder, R.; Snodgrass, C.; and Kerne, A.  2014\.  Everyday ideation: All of my ideas are on pinterest.  CHI ’14, 2411–2420.
  * [Lo, Cheng, and Leskovec 2017] Lo, C.; Cheng, J.; and Leskovec, J.  2017\.  Understanding online collection growth over time: A case study of pinterest.  WWW ’17 Companion, 545–554.
  * [Lo, Frankowski, and Leskovec 2016] Lo, C.; Frankowski, D.; and Leskovec, J.  2016\.  Understanding behaviors that lead to purchasing: A case study of pinterest.  KDD ’16, 531–540.
  * [Manikonda et al. 2015] Manikonda, L.; Venkatesan, R.; Kambhampati, S.; and Li, B.  2015\.  Trending chic: Analyzing the influence of social media on fashion brands.  arXiv preprint arXiv:1512.01174.
  * [Meyers-Levy and Peracchio 1995] Meyers-Levy, J., and Peracchio, L. A.  1995\.  How the use of color in advertising affects attitudes: The influence of processing motivation and cognitive demands.  Journal of Consumer Research 22(2):121–38.
  * [Middlestadt 1990] Middlestadt, S. E.  1990\.  The effect of background and ambient color on product attitudes and beliefs.  ACR North American Advances.
  * [Miller, Chang, and Terveen 2015] Miller, H. J.; Chang, S.; and Terveen, L. G.  2015\.  “i love this site!” vs. “it’s a little girly”: Perceptions of and initial user experience with pinterest.  CSCW ’15, 1728–1740.
  * [Ottoni et al. 2013] Ottoni, R.; Pesce, J. P.; Las Casas, D. B.; Franciscani Jr, G.; Meira Jr, W.; Kumaraguru, P.; and Almeida, V.  2013\.  Ladies first: Analyzing gender roles and behaviors in pinterest.  ICWSM ’13.
  * [Sanchis-Ojeda, Sibley, and Massimi 2016] Sanchis-Ojeda, R.; Sibley, D.; and Massimi, P.  2016\.  Detection of fashion trends and seasonal cycles through the analysis of implicit and explicit client feedback.  In KDD Fashion Workshop.
  * [Schindler 1986] Schindler, P. S.  1986\.  Color and contrast in magazine advertising.  Psychology & Marketing 3(2):69–78.
  * [Sparkman Jr and Austin 1980] Sparkman Jr, R., and Austin, L. M.  1980\.  The effect on sales of color in newspaper advertisements.  Journal of Advertising 9(4):39–42.
  * [Wojnicki and Godes 2008] Wojnicki, A., and Godes, D.  2008\.  Word-of-mouth as self-enhancement.  HBS Marketing Research Paper.
  * [Wu et al. 2017] Wu, B.; Cheng, W.-H.; Zhang, Y.; Huang, Q.; Li, J.; and Mei, T.  2017\.  Sequential prediction of social media popularity with deep temporal context networks.  In IJCAI ’17.
  * [Yamaguchi, Berg, and Ortiz 2014] Yamaguchi, K.; Berg, T. L.; and Ortiz, L. E.  2014\.  Chic or social: Visual popularity analysis in online fashion networks.  MM ’14, 773–776.
  * [Yang, Li, and Luo 2015] Yang, X.; Li, Y.; and Luo, J.  2015\.  Pinterest board recommendation for twitter users.  MM ’15, 963–966.
  * [You et al. 2017] You, Q.; García-García, D.; Paluri, M.; Luo, J.; and Joo, J.  2017\.  Cultural diffusion and trends in facebook photographs.  ICWSM ’17.
  * [Zhong et al. 2014] Zhong, C.; Salehi, M.; Shah, S.; Cobzarenco, M.; Sastry, N.; and Cha, M.  2014\.  Social bootstrapping: How pinterest and last.fm social communities benefit by borrowing links from facebook.  WWW ’14, 305–314.
  * [Zhong, Kourtellis, and Sastry 2016] Zhong, C.; Kourtellis, N.; and Sastry, N. R.  2016\.  Pinning alone? a study of the role of social ties on pinterest.  In ICWSM, 751–755.