Pydata influencer validation

Influencer Validation
5th September 2017
Dr Ed Cannon

2Copyright © Capgemini 2015. All Rights Reserved
Insights & Data: Data Science | Version 1.0
Whose the one?

Objective
• Create a plugin to validate social media influencers for marketing
• Key Advantages:
• Can be used by analysts
• Can be extended by developers & data scientists
• Can be part of a workflow – simple plug & play
• Works ontop of hadoop using pyspark can scale to millions
• Can get raw metrics & analyse further
• Option to output to ppt

Influencer Validation
A way to measure the effectiveness of an influential social media entity prior to using them as a
service
• To quickly identify
potential influencers
for a marketing
campaign
• Target correct
audience
• Understand potential
value of an influencer/
ROI
Who needs
influencers to be
validated?
1 43
• Brand managers
• PR Agencies
• Breakdown of
metrics across
several social media
channels
• What age group
they target
• What the audience
is interested in
• Gender of the
audience
• Type of account
• Written in python
code & plugin was
created
• On demand
2
Why do we
need it?
How is it ran
and how often?
What does the
service provide?

What data sources are used?
Primary Data Sources

Metrics that Matter
 Engagement e.g. youtube comments/likes; twitter retweets
 Reach Youtube views, twitter followers etc
 Target demographic & Interest e.g. males aged 25-34
 Channel(s) e.g. Youtube for a video campaign
 Indices: H-index, M-index, G-index

H-index
Definition:
“A scholar with an index of h has published h papers
each of which has been cited in other papers
at least h times. Thus, the h-index reflects
both the number of publications and the
number of citations per publication.”
H-index = 116
H-index = 116

The H-Index is a means of measuring influence
Twitter Author-level metric – measures: productivity (tweets) & engagement
(retweets)
Tweet Retweeted Retweeted
1 10 25
2 8 8
3 5 5
4 4 3
5 3 3
200
Last 200 tweets
H-index = #of tweets which have been
retweeted H or more times
H-index = 4 H-index = 3

Twitter H-index
Twitter H-index = 10 Twitter H-index = 5

M-Index
 The m-index is defined as h/n, where n is the time delta in years between the
last tweet and the 200th tweet & h is the h-index
 M-indices tend to be higher than H or G-indices as the time taken to tweet
200 times could be days in some circumstances

G-Index
The g-index can be seen as the h-index for an averaged retweet count,
largest number n of highly engaged tweets for which the average number of retweets is at least n
The index is calculated based on the distribution of retweets
received by a given authors tweets, such that given a set of
tweets ranked in decreasing order of the number of
of retweets that they received, the g-index is the unique largest
number such that the top g tweets received together at least g2
retweets.

Objective
Validate a set of influencers that can be used
to market a food brand on Youtube, or
Twitter, targeting mixed audience, primarily
females in age group 25-34, intrested in food
& parenting?

Followers 6527902
Location London & Essex
Created @ Tue Jan 06 14:21:45 2009
Average Reach 728.26
+ Sentiment(%) 44.60
- Sentiment(%) 4.50
Average Impact 38.24
H-Index 61
55% 45%
Women Men
Audience Information
77% 23%
Individual Organisation
0 50 100 150
0-9
10-17
18-24
25-34
35-44
45-54
55-64
65+
Audience Age
0% 5% 10% 15% 20%
food & drinks
family & parenting
sports
beauty/health & fitness
music
Audience Interest
Followers 5765451
Posts 5090
Videos(last 6m) 250
Average Likes 6414
Average Dislikes 186
Average Views 476999
Average
Comments
421
jamieoliver
jamieoliver
JamieOliver
jamieoliver
London & Essex

Followers 79178
Location United Kingdom
Created @ Tue Feb 22 22:19:13 2011
Average Reach 943.12
+ Sentiment(%) 54.17
- Sentiment(%) 0.00
Average Impact 46.96
H-Index 15
38% 63%
Women Men
Audience Information
54% 46%
Individual Organisation
0 1 2 3 4
0-9
10-17
18-24
25-34
35-44
45-54
55-64
65+
Audience Age
0% 10% 20% 30% 40%
animals & pets
tv
automotive
food & drinks
photo & video
Audience Interest
Followers 139316
Posts 332
Videos(last 6m) 0
Average Likes Not available
Average Dislikes Not available
Average Views Not available
Average
Comments
Not available
CandiceBrown
candicebrown
candicebrown
United Kingdom

H-index Foodie Distribution (sample 1K)

Foodie H-index By Gender
0.0%
2.5%
5.0%
7.5%
10.0%
0 50 100 150 200
Twitter h−index
UserPercentage
female
male
Set threshold acceptance criteria
Female foodie influencers
have more engagement

0%
10%
20%
0 20000 40000
Twitter m−index
UserPercentage
female
male
Foodie M-index By Gender
Highly influential female influencers
who have tweeted a lot over
a short period of time
@DeniseCop1
74K tweets
Joined: July 2013
H-index: 159
M-index: 58K

Foodie G-Index By Gender
0%
5%
10%
15%
0 20 40 60
Twitter g−index
UserPercentage
female
male
Skewed to female foodies having more
collections of tweets with higher retweets

Comparing Foodies To Beauticians

Beauty H-index By Gender
0%
20%
40%
0 50 100 150 200
Twitter h−index
UserPercentage
Female
Male
0.0%
2.5%
5.0%
7.5%
10.0%
0 50 100 150 200
Twitter h−index
UserPercentage
female
male
FoodieBeauty
H-indices lower for beauticians & follows exponential distribution

Beauty M-Index
0%
20%
40%
60%
0 20000 40000
Twitter m−index
UserPercentage
Female
Male
0%
10%
20%
0 20000 40000
Twitter m−index
UserPercentage
female
male
Both follow exponential distributions
FoodieBeauty

Beauty G-Index
0%
10%
20%
30%
40%
0 25 50 75
Twitter g−index
UserPercentage
Female
Male
0%
5%
10%
15%
0 20 40 60
Twitter g−index
UserPercentage
female
male
FoodieBeauty
Foodies & females across these 2 categories have higher G-indices

Conclusions
 Developed software that can validate influencers for social media marketing
campaigns across different channels
 Introduced 3-novel indices to measure an influencers engagement (H, M &
G)
 Indices are quick to calculate, can be incorporated into workflows, are easily
scalable in a distributed fashion and used by a variety of audiences &
categories
 Analysis is automated to provide both metrics & ppt

APIs Leveraged
 Twitter API
 Instagram API
 Youtube API
 Faceplusplus
 Pandas
 Requests
 Rpy2
 Python-pptx

Questions

Pydata influencer validation

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