1. Cross-Platform Social Media Strategy
Developing Branded Communities
Network science has evolved greatly since The
Seven Bridges of Königsberg was ﬁrst published
in 1736 by Leonhard Euler, reducing the physi-
cal pathways of bridges into abstract graphable
nodes. The pathways are now digital and the in-
formation we can derive from their relationships
is immensely valuable. Network science has
been applied to numerous studies over the years
and it is now ﬁnally focused rightfully on social
media; this has huge implications for market-
ers of all kinds, enabling better understanding of
successful network characteristics.
Using social network analysis and graph theory
from Netlytic, we can begin to asses successful
social media strategy across platform and devel-
op a model for building a branded community.
Network Variation Challenges
Facebook, Twitter & Instagram index user data for research
but because the platforms differ in a variety of ways, it is not
always straight-forward how to assess them against eacho-
ther. Newer apps like Snapchat are home to many branded
stories, yet lack any ability to recover analytical data. This
will present challenges as marketers try to further monetize
Most studies of social networks have focused on individu-
al platforms rather than the environment as a whole. In this
study, we wanted to compare performance between plat-
forms to learn more about which platforms have the best
community engagement for brands. Gaining an understand-
ing of the differing network qualities across platforms is
valuable for companies determining their marketing and so-
cial media resource allocation in the building of their brand-
ed community. By observing behaviour of model compa-
nies, we can begin to quantify how they place and perform
Academic Research in
Inﬂuence and Community on Social Media
For this project, I worked with fellow classmember Cam
Munro to support my ﬁndings with research lead by
Dr. Gruzd and his team at the Ryerson Social Media Lab.
Two papers that signiﬁcantly contributed to and guided this
‘Enabling Community Through Social Media’ (Gruzd A, Hay-
Is Happiness Contagious Online? A Case of Twitter and the
2010 Winter Olympics. (Gruzd A, Doiron S, and Mai P.)
These articles suggest important ideas about what makes a
strong online community and we used this information to di-
rect analysis of the dataset into branded social media strat-
Following the behaviour of three successful brands
on social media over six weeks.
We gathered network data over a six week span from Her-
schel Supply, Jansport and Yeti Coolers’ Facebook, Twitter
and Instagram channels.
These brands all make somewhat similar, iconic and fash-
ionable consumer goods and generate a large audience on
For each brand, we tracked their company name as a
hashtag, their ‘branded’ hashtag(s) and their contest
hashtag, if they had one. Below is a breakdown of the net-
work statistics for each hashtag as well as pie-charts de-
tailing follower distribution and above is a breakdown of the
Herschel Supply Co.
HASHTAGS: #HerschelSupply, #WellTravelled, #CityLimitless
Facebook: 298.3k likes
Twitter: 33.1k followers
Instagram: 566k followers
HASHTAGS: #Jansport, #RightPack, #LifeUnzipped
Facebook: 1.58m likes
Twitter: 22.6k followers
Instagram: 59.6k followers
HASHTAGS: #YetiCoolers, #BuiltForTheWild
Facebook: 283.6k likes
Twitter: 56.5k followers
Instagram: 129k followers
Social Network & Content Analysis
We can use Netlytic to gather social network data track-
ing usernames and hashtags and conduct a cross-platform
content analysis. This allows us to extract valuable under-
standing from engaged branded communities in ways previ-
Using Netlytic we can explore the conversations happening
in branded communities through a variety of interactive vi-
sualizations. Users with a high degree of centrality are visibi-
ly more connected in a network compared to average users.
Identifying and recognizing these users on a branded chan-
nel is a strategic way to make new connections with com-
munity leaders and inﬂuencers. See Figure 5.
Content and Language Analysis
Using Netlytic we can organize the data in a way that lets
us analyze the linguistic content of the messages. This al-
lows us to understand the emotional content of the com-
munity and the conventions, which occur most frequently.
This also allows the practitioner the ability to form a lexicon
of key community hashtags and terms that are used most
frequently by the community. This could be helpful for fu-
ture post creation and hashtags to target and engage. See
Figure 4. for a look at content analysis around a branded
Regional Targeting and Geo-location
Using Netlytic we can build a heat-map that indicates areas
with higher user density. This is very valuable for brands to
gain a local understanding of their international following.
It is important to know both where the majority of the audi-
ence is engaged and also where the audience is lacking so
that steps can be taken to grow weaker regions.
Social Media Resource Allocation & Distribution
The data suggests that Facebook and Instagram build larg-
er branded communities especially compared to Twitter.
At this moment in time, Instagram appears to be the ide-
al platform for developing a branded community around
a consumer good or product. Further, 2014 study found
that roughly half of internet-using young adults ages 18-29
(53%) use Instagram; and half of all Instagram users (49%)
use the site daily (Duggan, et al., 2014). I would assert that
because these brands market fashionable consumer prod-
ucts, a platform that supports the use of strong artsistic vi-
suals is preferable, which is why Instagram currently is of
such strong focus. As new more-candid apps like Snapchat
begin to tell incredible branded stories, more companies will
engage with inﬂuencers on this medium as well.
Currently, these brands make strong use of Instagram le-
veraging the talents of the proliﬁc content creators on the
platform. Further, the data suggests that these brands in-
vest into Instagram content creation through inﬂuencer mar-
keting, which they then push to their Facebook and Twitter
Strategic Inﬂuencer Marketing
Hashtag Paid @ Ryerson Digital Media Zone
Hashtag Paid is a startup located in the Ryerson Digital Me-
dia Zone. They are leaders in an emerging ﬁeld of Instagram
marketing. An important trend shared by all three brands
in this study is the use of strategic inﬂuencer marketing.
Brands are ﬁnding great success using highly inﬂuential
photographers to tell branded stories to their communities.
Brands of this size can beneﬁt greatly from the investment
of resources into strategic inﬂuencer marketing to help build
stronger branded communities, leveraging the engaged
communities that follow these inﬂuencers.
A Model For Building A Successful
This research of three leading brands on social media pro-
vided some very valuable insights into marketing resource
allocation on Facebook, Twitter and Instagram. While there
are challenges comparing the content across platforms, we
are able to build a model for creating new branded commu-
nities from this information.
The use of social media data will continue to be the founda-
tion for well-informed social media strategy across platform
including inﬂuencer identiﬁcation, regional targeting and
geo-location and language and content analysis. Temporary
apps like Snapchat may make analyzing data more chal-
lenging. Finally, more research will need to be done in the
future to evaluate the implementation of this strategy as oth-
er brands attempt to recreate the successes of the leading
brands on social media.
Figure 3. Regional Targeting and Geo-location of
Herschel’s branded hashtag #CityLimitless
Figure 4. Content and Language Analysis of
Herschel’s branded hashtag #CityLimitless
showing the top 30 most-used terms.
Figure 2. Social Media Follower DistributionFigure 1. Comparing Network Metrics
In Figure 1. above, the various network statistics paint a pic-
ture of the strength of the various branded hashtag com-
munities. This tells us not only how large these commu-
nities are (nodes / names) , but how well connected they
are (modularity), and how well they share content between
In Figure 2. above, we can visualize a breakdown of follower distribution between Facebook,
Twitter and Instagram. This allows us to understand where the majority of followers currently
are, which is important when we evaluate growth rates of each account, post frequency of each
account and more. The one common trend here is that Twitter is the least populated channel
for all three brands. While Jansport has 95% of their following on Facebook, it is important to
note that their post-frequency on Instagram was the highest of all three brands on any platform,
suggesting that Instagram is where more of their resources are currently being directed.
Figure 5. Network Visualization and Inﬂuencer Identiﬁcation for Herschel’s #CityLimitless branded hashtag
Figure 5. illustrates how we can use the network visualization of a branded hashtag like #CityLimitless to identify inﬂuencers who
relate closely with the brand. Nodes with higher numbers of ties are represented visually as more central and large within the net-
work. When looking through the interactive visualization, the most-tied users are almost always highly inﬂuential photographers
who are great candidates to represent a brand with their photographic and social contributions.