Social Media Analytics

Wendy Moe, University of Maryland

with Kunpeng Zhang (UMD) and David Schweidel (Emory)

Social Media and Marketing

• Social media for messaging vs. customer service vs. marketing research

• The promise of social media monitoring? • Sometimes there is no distinction between those who

implement social media campaigns and those who analyze social media data

• Too much data, so the data scientists are driving • Social media is being managed in a silo and not integrated with

the overall marketing strategy. • No alignment with tried and true offline marketing research

Challenges when using social media opinion for insights

• Potential for bias from: – Audience effects – Scale usage heterogeneity

• Dynamics over time – Product life cycle effects (Li and Hitt 2008) – Ordinality effects (Godes and Silva 2012) – Social dynamics (Moe and Schweidel 2012)

• Implications for social media analytics – Theory of Social Media Posting Behavior – Study 1: Brand Tracking – Study 2: Brand Benchmarking and Ranking

Theory: Social Media Posting Behavior

Pre-Purchase

Evaluation

Purchase Decision and

Product Experience

Post-Purchase

Evaluation

Incidence

Decision

Evaluation

Decision

Posted Product Opinion

OP

INIO

N F

OR

MA

TIO

N

OP

INIO

N E

XP

RES

SIO

N

SELECTION

EFFECT

ADJUSTMENT

EFFECT

What influences posting behavior? Opinion formation vs. opinion

expression

Opinion formation, in theory, is a function of satisfaction

Opinion expression is subject to a variety of biases and dynamics Scale usage Expert effects General audience effects Multiple audience effects Bandwagon vs. differentiation

Variance

Average

Activists

Post frequently Attracted by lack of consensus More negative Variance and volume make them more negative

Low Involvements

Post infrequently Deterred by lack of consensus More positive Variance and volume make them more positive

Shifting behavior and user base over time

Study 1: Brand Tracking Social media listening vs. surveys

0%

20%

40%

60%

80%

100%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Pro

po

rtio

n o

f P

osi

tive

Co

mm

en

ts

Observation Month

Blog

Forum

Microblog

Aggregate

Venue Correlation

Blogs .197

Forums -.231

Microblogs -.394

Average .008

Correlation with offline brand tracking survey

Factors influencing social media brand tracking

• Audience effects

– Venue effects

– Social dynamics within venues

• Context effects

– Products in the brand portfolio

– Attributes

Predictive model to account for audience and context effects

Correlation with survey (t) Adj SM Brand Metric = .376

Avg sentiment =.008

Blogs = .197

Forums = -.231

Microblogs = .394

Correlation with survey (t+1) Adj SM Brand Metric = .881

Avg sentiment = .169

Blogs = .529

Forums = .213

Microblogs = .722

8.75

8.8

8.85

8.9

8.95

9

9.05

9.1

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8 9 10

Ave

rage

Su

rve

y R

esp

on

se

Ad

j SM

Bra

nd

Me

tric

Month of Overlap Period (t)

GBI in month t-1 Survey in month tSM in month t-1

Study 2: Brand Benchmarking and Ranking Role of Positivity/Negativity Bias

from Zhang and Moe (2016)

Data Description • Data pertaining to Facebook fan pages (English speaking only)

includes likes, comments, posts, followers, etc.

• All historical data up to January 1, 2016 (first brand post in our data was in January 2009)

• Data cleansed for fraudulent activity and bots

Number of brands 3,355

Number of brand posts 11,253,623

Number of unique users 169,574,532

Number of user comments 947,550,458

Number of likes 6,681,320,439

Validation of Method: Comparison with BrandZ rank

Estimate Std.

Error p-value

Intercept 8.253 0.646 <.0001

Favorability score 2.336 0.833 0.006

DV = ln(BrandZ Value)

Estimate Std.

Error p-value

Intercept 130.940 22.454 <.0001

Favorability score -114.879 28.954 0.0002

Estimate Std.

Error p-value

Intercept 10.833 0.493 <.0001

Average Sentiment -1.046 0.651 0.112

DV = ln(BrandZ Value)

Estimate Std.

Error p-value

Intercept 15.544 17.884 0.387

Average Sentiment 36.025 23.639 0.131

DV = ln (BrandZ Value)

Also considered models with average sentiment, number of likes, number of comments, and number of followers. Adj R2 is a little better but none are significant predictors.

DV = BrandZ Rank

Effects on Bias (BrandZ Top 100) MDL1 MDL2 MDL3 MDL4 MDL5

Intercept 0.1347 0.0885 0.0956 0.0876 0.0742

Brand Community

Variance in Sentiment -0.7431 -0.6335** -0.6243** -0.6320** -0.6965**

# of Followers (000,000s) -0.0029*

# of Engaged User (000,000s) -0.0119m

# of Users Commenting (000,000s) -0.1165m

# of Users Liking (000,000s) 0.0128m

# of Comment (000,000s) -0.1717

# of Like (000,000s) 0.0074m

Brand Traits

Sector[Basic Materials] 0.0545 0.0526 0.0540 0.0527 0.0542

Sector[Consumer Goods] -0.0036 -0.0062 -0.0123 -0.0057 0.0021

Sector[Financial] 0.0409 0.0406 0.0420 0.0406 0.0426

Sector[Industrial Goods] -0.0741 -0.0704 -0.0671 -0.0705 -0.0803

Sector[Services] -0.0320 -0.0304 -0.0302 -0.0309 -0.0300

# Employee (000,000s) 0.0388 0.0325 0.0358 0.0319 0.0262

GoogleTrends -0.0270 -0.0007 -0.0192 0.0007 0.0224

Brand Activity

# of Brand Post (000s) 0.0691m 0.0752m 0.0864* 0.0737m 0.1093*

# of News Mentions (0,000s) 0.0462 0.0315 0.0405 0.0296 0.0203

R-Squared 0.2578 0.2336 0.2383 0.2324 0.2508

Adjusted R-Squared 0.1395 0.1114 0.1168 0.1100 0.1185

** p<0.01; * p<0.05; m p<0.1

Examples

Brand Avg Sent Favorability Bias # Followers

PayPal (#88) 0.8009 0.6401 0.1608 395,751

Oracle (#44) 0.9189 0.7977 0.1212 475,935

Nike (#28) 0.7372 0.8524 -0.1152 20,604,708

Disney (#19) 0.8752 0.9941 -0.1189 34,726,558

Conclusions

• Bias exists on social media. Therefore, social media analytics designed to extract insights need to account for these biases.

• Bias can lead to: – Flawed metrics – Social dynamics – Echo chamber effects

• Social media analytics can be valuable if appropriate

methods are employed.