WHO creates trends in a mobile sharing app? accidentals or influentials? Answer: influentials DO exist, yet they are not few but many! http://profzero.org/publications/trend13sha.pdf

1. Trend Makers and Trend Spotters
in a Mobile Application
Xiaolan Sha◦
Daniele Quercia•
Pietro Michiardi◦
Matteo Dell’Amico◦
◦EURECOM •Yahoo! Research Barcelona

2. Who create trends?

3. Two-Step Flow
Mass Media
Influentials
Normal

4. 444 Accidental Influentials JOURNAL OF CON
FIGURE 2 of these assumptions is demonstrabl
clearly correct either—the empirical ev
SCHEMATIC OF NETWORK MODEL OF INFLUENCE
inconclusive. Thus we will also presen
variations of the basic model that relax
and the randomness assumptions.
Another advantage of formally defi
work, even with such a simple mod
define more precisely what we mea
Previous empirical work has address
should be considered influential, b
mains elusive (Weimann 1991). Clas
of Coleman et al. (1957) and Merton
individuals who directly influence m
of their peers should be considered
cent market research studies have co
ber may be as high as 14 (Burson-M
studies, by contrast, define influent
terms: Keller and Berry (2003), fo
fluentials as scoring in the top 10%
ership test, while Coulter et al. (2002
treat the top 32% as influentials.
Here we follow the latter approac
ential as an individual in the top q%
tribution p (n). From a theoretical pers
value of q that we specify is necessa
we have already argued that dichoto
tween opinion leaders and followers a
1 influence can only flow from opinion leaders to fol- derived nor empirically supported. O
lowers, in figure 2, it can flow in either direction. Second, ever, is not to defend any particular d
in figure 2 influence can propagate for many steps, [D. Watts,but to examine the claim that influen
P. Dodds JSTOR 2007]
whereas in figure 1 it can propagate only two. We note, reasonable, self-consistent manner—
however, that, in both cases, figure 2 is consistent with of diffusion processes. From this pers
available empirical evidence—arguably more so than fig- definition has the advantage (over d
ure 1. Numerous studies, including that of Katz and La- absolute numbers) that it can be app

5. Context

6. Dataset
Users
technology-savvy, design-conscious
Pictures
technology, lifestyle, music, design and fashion
9,316 users uploaded 6,395 pictures and submitted 13,893 votes.

7. Identification
Trends
A simple burst detection method
Spotters/Makers
Spotter Score: how many, early, popular of the trends
Maker Score: how often
Typical Users
All active users (>=2 votes/uploads) who is not spotter or a maker.
140 Makers; 671 Spotters; 1,705 Typical Users

8. Characterizations
Features
Activity
Content
Network
Geographical
Hypotheses
[Kolmogorov-Smirnov tests]
Spotters/Makers vs. Typical Users
Spotters vs. Makers

9. Results
Spotters/Makers vs. Typical users
More active, more popular
Spotters vs. Makers
More votes, less uploads,wider spectrum of interests

10. Prediction
Features
｛
Activity
Content Trend Spotters
Every User
Social Network Trend Makers
User Space Geography User Space

11. Predictors
Follower Geo Span
Upload Diversity
Daily Uploads
Vote Diversity
Daily Votes
Wandering
#Followers
#Followees
Life Time
Age
Life Time 0.21
Activity Daily Uploads 0.02 -0.12
Daily Votes 0.05 -0.09 0.47 ⇤
Upload Diversity 0.02 0.09 0.40 ⇤ 0.08
Content Vote Diversity 0.04 0.08 0.22 0.08 0.42 ⇤
Wandering 0.004 0.13 0.16 0.11 0.06 0.05
Geographical Follower Geo Span 0.05 0.12 0.16 0.10 0.12 0.11 0.23
#Followers 0.03 0.23 0.37 ⇤ 0.14 0.22 0.16 0.44 0.16
#Followees 0.05 0.17 0.52 ⇤ 0.31 ⇤ 0.29 ⇤ 0.22 0.56 ⇤ 0.21 0.64 ⇤
Network Network Clustering 0.03 0.13 0.22 0.04 0.24 0.23 -0.001 0.27 ⇤ 0.08 0.22
Spotter Score 0.07 0.18 0.03 0.01 0.05 0.10 0.04 0.07 0.13 0.11 0.15
Maker Score 0.07 0.10 0.06 0.01 0.07 0.06 0.02 0.12 0.12 0.09 0.10
Table 5. Pearson Correlation coefficients between each pair of predictors. Coefficients greater than ±0.25 with statistical significant level < 0.05 are
marked with a ⇤.
Practical Implications CONCLUSION
The ability of identifying trend spotters and trend makers has A community is an emergent system. It forms from the ac-
implications in designing recommender systems, marketing tions of its members who are reacting to each other’s behav-
campaigns, new products, privacy tools, and user interfaces. ior. Here we have studied a specific community of individuals
who are passionate about sharing pictures of items (mainly
Recommender Systems. Every user has different interests fashion and design items) using a mobile phone application.
and tastes and, as such, might well benefit from personalized This community has a specific culture in which a set of habits,
suggestions of content. These suggestions are automatically attitudes and beliefs guide how its members behave. In it, we
produced by so-called “recommender systems”. Typically,

12. Prediction
True positive rate
0.8
S-logistic
0.4 S-svm
M-logistic
M-svm
0.0
0.0 0.2 0.4 0.6 0.8 1.0
False positive rate

13. y trend spot-
Age 2e-04 0.001
preliminary Life Time 0.006 * 0.001 *
ers opens up Successful Spotters/Makers
Daily Votes (Daily Uploads) 0.007 * 0.16 *
ferences be- Vote Diversity (Upload Diversity) 0.38 * 0.14 *
Wandering -6e-15 -7e-15
#Followers 2e-05 0.009 *
Network Clustering 0.08 0.28 *
no previous
spotters and
l hypotheses (b) Linear Regression
er that trend Features log(Score)
tend to vote Spotters Makers
compared to Age 0.36 * 0.01
(H3.1), vote Life Time 0.19 * 0.0001
Daily Votes (Daily Uploads) 0.16 -
-1.03 *
ote more di- Vote Diversity (Upload Diversity) 7.28 * -
-1.09 *
). After run- Wandering -2.1e-13 -1.4e-15
nd that trend #Followers -0.06 0.01 *
ers who, by Network Clustering 2.75 -
-0.64 *
oth H3.1 and R2 0.15 0.65
ote, we find Adjusted R2 0.14 0.64
oad and vote
s vote items
kers act in a Table 3. Coefficients of the linear regression. A correlation coefficient
d in [20, 18] within 2 standard errors is considered statistically significant. We high-
light and mark them with *.
ality content.
items in the
le they vote of followees, daily uploads, daily votes, and content diver-
de spectrum

14. Summary
Successful Spotters
Early adopters who vote items from various categories.
Successful Makers
Users who upload items belonging to specific categories, tend to be followed by
users from different social clusters.

15. Conclusions
Who Create Trends?
Regular individual with specific interests connected with early adopters with
diverse interests.

16. Questions?