Analysis of the Effect of Content on Engagement on a Brand’s Social Media Page
Mahita Ajmera
Setting Up & Summarizing
ds <- read.csv(paste("total.csv", sep=""))
summary(ds)## Date Month Daily.Page.Engaged.Users Daily.Total.Reach
## 01-01-17: 1 August : 62 Min. : 19.00 Min. : 30
## 01-02-17: 1 July : 62 1st Qu.: 86.75 1st Qu.: 1588
## 01-03-17: 1 October : 62 Median : 235.50 Median : 6880
## 01-04-17: 1 November : 60 Mean : 2355.33 Mean : 68000
## 01-05-17: 1 September: 60 3rd Qu.: 1439.50 3rd Qu.: 77062
## 01-06-17: 1 December : 31 Max. :72155.00 Max. :1009771
## (Other) :512 (Other) :181
## Daily.Total.Impressions Text Weekly.Page.Engaged.Users
## Min. : 68 Min. : 0.000 Min. : 208.0
## 1st Qu.: 2327 1st Qu.: 4.000 1st Qu.: 700.5
## Median : 10220 Median : 5.000 Median : 4669.0
## Mean : 79822 Mean : 6.079 Mean : 15196.1
## 3rd Qu.: 85687 3rd Qu.: 7.000 3rd Qu.: 13011.8
## Max. :1461268 Max. :24.000 Max. :206929.0
##
## Weekly.Total.Reach Daily.Viral.Reach Weekly.Total.Impressions
## Min. : 587 Min. : 16.0 Min. : 1301
## 1st Qu.: 11359 1st Qu.: 436.5 1st Qu.: 24142
## Median : 158736 Median : 940.5 Median : 225195
## Mean : 354852 Mean : 9129.6 Mean : 556392
## 3rd Qu.: 415671 3rd Qu.: 2592.2 3rd Qu.: 625314
## Max. :3280272 Max. :650119.0 Max. :5502372
##
## Daily.Total.Consumers
## Min. : 5.00
## 1st Qu.: 38.25
## Median : 173.00
## Mean : 1620.33
## 3rd Qu.: 966.00
## Max. :70066.00
## library(psych)
library(lattice)
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitdescribe(ds)## vars n mean sd median trimmed
## Date* 1 518 259.50 149.68 259.5 259.50
## Month* 2 518 7.00 3.61 7.0 7.07
## Daily.Page.Engaged.Users 3 518 2355.33 6248.49 235.5 886.18
## Daily.Total.Reach 4 518 67999.80 137988.18 6879.5 34541.49
## Daily.Total.Impressions 5 518 79822.30 171034.16 10219.5 38911.51
## Text 6 518 6.08 3.60 5.0 5.71
## Weekly.Page.Engaged.Users 7 518 15196.08 29316.28 4669.0 7883.27
## Weekly.Total.Reach 8 518 354851.77 565312.24 158735.5 228666.50
## Daily.Viral.Reach 9 518 9129.63 54120.63 940.5 1576.32
## Weekly.Total.Impressions 10 518 556392.22 919462.97 225195.0 339006.92
## Daily.Total.Consumers 11 518 1620.33 5424.27 173.0 489.83
## mad min max range skew kurtosis
## Date* 192.00 1 518 517 0.00 -1.21
## Month* 4.45 1 12 11 -0.16 -1.33
## Daily.Page.Engaged.Users 297.26 19 72155 72136 5.62 42.53
## Daily.Total.Reach 9981.60 30 1009771 1009741 3.64 15.67
## Daily.Total.Impressions 14742.97 68 1461268 1461200 4.08 19.86
## Text 2.97 0 24 24 1.60 4.44
## Weekly.Page.Engaged.Users 6071.25 208 206929 206721 3.43 13.96
## Weekly.Total.Reach 223627.23 587 3280272 3279685 2.94 10.01
## Daily.Viral.Reach 977.77 16 650119 650103 9.39 93.67
## Weekly.Total.Impressions 309974.59 1301 5502372 5501071 2.91 9.31
## Daily.Total.Consumers 238.70 5 70066 70061 6.92 62.61
## se
## Date* 6.58
## Month* 0.16
## Daily.Page.Engaged.Users 274.54
## Daily.Total.Reach 6062.85
## Daily.Total.Impressions 7514.81
## Text 0.16
## Weekly.Page.Engaged.Users 1288.08
## Weekly.Total.Reach 24838.39
## Daily.Viral.Reach 2377.92
## Weekly.Total.Impressions 40398.88
## Daily.Total.Consumers 238.33Contingency Tables
table(ds$Text)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 23 2 22 50 62 126 34 73 38 28 22 5 6 5 3 3 3 2
## 18 19 20 22 24
## 2 1 5 2 1table(ds$Daily.Page.Engaged.Users, ds$Month)##
## April August December February January July June March May
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##
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## 3006 0 0 0
## 3045 0 0 0
## 3063 1 0 0
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## 3154 0 1 0
## 3173 1 0 0
## 3255 0 0 0
## 3284 0 0 0
## 3386 0 0 0
## 3397 1 0 0
## 3402 0 0 0
## 3428 0 0 0
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## 5696 0 1 0
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## 34181 0 0 0
## 35250 0 0 0
## 42359 0 0 0
## 47492 0 0 1
## 72155 0 0 0table(ds$Text, ds$Month)##
## April August December February January July June March May November
## 0 0 2 0 0 0 0 0 0 0 11
## 1 1 0 0 0 0 0 0 0 1 0
## 2 2 3 0 2 1 1 1 3 2 0
## 3 0 1 2 2 2 0 0 2 1 20
## 4 4 2 2 1 0 3 0 21 0 22
## 5 9 26 3 2 0 35 28 2 3 1
## 6 0 1 2 17 0 1 0 1 4 2
## 7 6 3 1 0 25 18 0 1 10 3
## 8 5 1 2 0 0 0 0 0 1 0
## 9 0 16 2 2 0 0 0 1 2 0
## 10 0 1 11 0 2 0 1 0 3 0
## 11 0 0 0 2 0 0 0 0 0 0
## 12 0 0 0 0 0 1 0 0 1 0
## 13 0 1 0 0 1 0 0 0 1 0
## 14 0 2 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 3 0 0 0 0
## 16 0 1 1 0 0 0 0 0 1 0
## 17 1 1 0 0 0 0 0 0 0 0
## 18 1 1 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 1 0
## 20 0 0 4 0 0 0 0 0 0 0
## 22 0 0 1 0 0 0 0 0 0 1
## 24 1 0 0 0 0 0 0 0 0 0
##
## October September
## 0 3 7
## 1 0 0
## 2 4 3
## 3 4 16
## 4 2 5
## 5 15 2
## 6 2 4
## 7 2 4
## 8 22 7
## 9 2 3
## 10 2 2
## 11 1 2
## 12 1 3
## 13 1 1
## 14 0 1
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 1 0
## 22 0 0
## 24 0 0Plots & Tests
boxplot(Daily.Page.Engaged.Users~Month, data=ds,ylab="Month",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Text, data=ds,ylab="Text",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Daily.Total.Reach, data=ds,ylab="Daily Total Reach",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Daily.Viral.Reach, data=ds,ylab="Daily Viral Reach",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Daily.Total.Consumers, data=ds,ylab="Daily Total Consumers",xlab="Daily Page Engaged Users", horizontal=TRUE )
histogram(~Daily.Page.Engaged.Users,data=ds,col="grey",breaks=6)
histogram(~Daily.Total.Reach,data=ds,col="grey",breaks=6)
histogram(~Text,data=ds,col="grey",breaks=6)
To check the correlation between Daily Page Engaged Users and text:
cor.test(ds$Daily.Page.Engaged.Users,ds$Text)##
## Pearson's product-moment correlation
##
## data: ds$Daily.Page.Engaged.Users and ds$Text
## t = 1.482, df = 516, p-value = 0.139
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02116911 0.15041048
## sample estimates:
## cor
## 0.06510185p-value = 0.139
cor = 0.065
Hence, we fail to reject the null hypothesis that the daily page engaged users and the amount of text in the published content are not correlated.
To check the correlation between Daily Page Engaged Users and Daily Total Reach:
cor.test(ds$Daily.Page.Engaged.Users,ds$Daily.Total.Reach)##
## Pearson's product-moment correlation
##
## data: ds$Daily.Page.Engaged.Users and ds$Daily.Total.Reach
## t = 43.752, df = 516, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8677056 0.9045049
## sample estimates:
## cor
## 0.8875121p-value < 2.2e-16
cor = 0.88
Hence, we fail to reject the null hypothesis that the daily page engaged users and the daily total reach of the conten are not correlated.
library(corrgram)
corrgram(ds, order=FALSE, lower.panel=panel.cor,
upper.panel=panel.pie, text.panel=panel.txt,
main="Engaged Users")
scatterplot.matrix(~Daily.Page.Engaged.Users+Text+Daily.Total.Reach+Daily.Total.Impressions+Daily.Viral.Reach+Month+Daily.Total.Consumers, data=ds,
main="Daily Page Engaged Users vs Other Variables")## Warning: 'scatterplot.matrix' is deprecated.
## Use 'scatterplotMatrix' instead.
## See help("Deprecated") and help("car-deprecated").
T-test
Hypothesis: Amount of text in published content affects the people who engage with the page
t.test(ds$Text, ds$Daily.Page.Engaged.Users,data=ds)##
## Welch Two Sample t-test
##
## data: ds$Text and ds$Daily.Page.Engaged.Users
## t = -8.557, df = 517, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2888.611 -1809.898
## sample estimates:
## mean of x mean of y
## 6.079151 2355.333977p-value < 2.2e-16 which is less than 0.05
Hence, the number of people who engage with the page depends on the amount of text in the content published.
Hypothesis: The reach of content directly affects the people who engage with the page.
t.test(ds$Daily.Total.Reach, ds$Daily.Page.Engaged.Users,data=ds)##
## Welch Two Sample t-test
##
## data: ds$Daily.Total.Reach and ds$Daily.Page.Engaged.Users
## t = 10.816, df = 519.12, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 53721.51 77567.41
## sample estimates:
## mean of x mean of y
## 67999.795 2355.334p-value < 2.2e-16 which is less than 0.05
Hence, the number of people who engage with the page depends on the amount of text in the content published.
Regression Analysis
In order to understand how text affects the number of daily page engaged users:
fit1 <- lm(Daily.Page.Engaged.Users ~ Text,data=ds)
summary(fit1)##
## Call:
## lm(formula = Daily.Page.Engaged.Users ~ Text, data = ds)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4162 -2196 -1877 -777 69470
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1669.20 538.10 3.102 0.00203 **
## Text 112.87 76.16 1.482 0.13896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6241 on 516 degrees of freedom
## Multiple R-squared: 0.004238, Adjusted R-squared: 0.002308
## F-statistic: 2.196 on 1 and 516 DF, p-value: 0.139Model 1:
Linear Model for Daily Page Engaged Users, Text, Daily Total Reach
fit2 <- lm(Daily.Page.Engaged.Users ~ Text+Daily.Total.Reach,data=ds)
summary(fit2)##
## Call:
## lm(formula = Daily.Page.Engaged.Users ~ Text + Daily.Total.Reach,
## data = ds)
##
## Residuals:
## Min 1Q Median 3Q Max
## -12869 -446 362 445 40428
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.365e+02 2.537e+02 -2.115 0.0349 *
## Text 2.659e+01 3.524e+01 0.755 0.4508
## Daily.Total.Reach 4.015e-02 9.204e-04 43.623 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2883 on 515 degrees of freedom
## Multiple R-squared: 0.7879, Adjusted R-squared: 0.7871
## F-statistic: 956.6 on 2 and 515 DF, p-value: < 2.2e-16Model 2:
Linear Model for Daily Page Engaged Users, Daily Viral Reach, Daily Total Impressions and Daily Total Consumers
fit3 <- lm(Daily.Page.Engaged.Users ~ Daily.Viral.Reach+Daily.Total.Impressions+Daily.Total.Consumers,data=ds)
summary(fit3)##
## Call:
## lm(formula = Daily.Page.Engaged.Users ~ Daily.Viral.Reach + Daily.Total.Impressions +
## Daily.Total.Consumers, data = ds)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5062.5 -392.6 -148.2 -127.5 14230.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.680e+02 7.863e+01 2.136 0.0331 *
## Daily.Viral.Reach -1.495e-03 1.547e-03 -0.966 0.3344
## Daily.Total.Impressions 1.139e-02 8.648e-04 13.169 <2e-16 ***
## Daily.Total.Consumers 7.974e-01 2.542e-02 31.368 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1585 on 514 degrees of freedom
## Multiple R-squared: 0.936, Adjusted R-squared: 0.9356
## F-statistic: 2506 on 3 and 514 DF, p-value: < 2.2e-16From this we can see that the second model gives a better result than the first one with
p-value < 2.2e-16;
F-statistic: 2506 with 3 data points and 514 degrees of freedom;
Multiple R-squared: 0.936 accounts for 93.6% of variances with Adjusted R-squared: 0.9356
We use the regression equation:
Daily Page Engaged Users = 1.680e+02 -1.495e-03Daily.Viral.Reach + 1.139e-02Daily.Total.Impressions + 7.974e-01Daily.Total.Consumers