Analysis of the Effect of Content on Engagement on a Brand’s Social Media Page
Mahita Ajmera
1. Introduction
Social media marketing is the use of social media platforms and websites to promote a product or service. Most social media platforms have built-in data analytics tools, which enable brands to track the progress, success, and engagement of ad campaigns. Brands address a range of stakeholders through social media marketing, including current and potential customers, current and potential employees, journalists, bloggers, and the general public. The performance of a campaign depends on various factors, one of them being the content published on the social media platform. In this paper, we investigate how text present in content affects the people who engage with it.
2. Overview of the Study
Our field study consists of data from July 2016 to November 2017. The data includes the reach, engaged users and other such factors of an FMCG brand’s social media page.
3. Empirical Study
3.1 Overview
The specific objective of this study was to investigate how the amount of text present in published content affects the number of people who engage with a brand’s social media page. However, there are many other factors present that can contribute to an increase in engagement. Hence, we consider the following hypothesis:
H1: Engagement depends on total reach and text H2: Engagement depends on viral reach, impressions and consumers
3.2 Data
We collected data from the social media platform itself.
. Daily Page Engaged Users: Number of people who engaged with your Page. Engagement includes any click or story created. (unique users)
. Daily Reach: Number of people who have seen any content associated with your Page (unique users)
. Daily Impressions: Number of impressions seen of any content associated with your Page (total count)
. Weekly Page Engaged Users: Number of people who engaged with your Page. Engagement includes any click or story created. (unique users)
. Weekly Total Reach: Number of people who have seen any content associated with your Page (unique users)
. Daily Viral Reach: Number of people who saw your Page or one of its posts from a story shared by a friend. These stories include liking your Page, posting to your Page’s Timeline, liking, commenting on or sharing one of your Page posts, answering a question that you posted, responding to one of your events, mentioning your Page, tagging your Page in a photo or checking in at your location. (unique users)
. Weekly Total Impressions: Number of impressions seen of any content associated with your Page (total count)
. Daily Total Consumers: Number of people who clicked on any of your content. Stories that are created without clicking on Page content (e.g. liking the Page from Timeline) are not included. (unique users)
. Text: the number of words in a given content piece
3.3 Model
In order to test the 2 Hypothesis, we conduct a regression analysis.
Regression Analysis
In order to understand how text affects the number of daily page engaged users:
total <- read.csv("D:/Data Analytics Internship - Sameer Mathur/week 4/capstone project/total.csv")
View(total)
ds <- read.csv(paste("total.csv", sep=""))
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
3.4 Result
Thus we can reject the first model and conclude that the daily engaged users depends on the various attributes shown above.
4 Conclusion
This paper was motivated by the need for research that could improve our understanding of how text in content influences the engagement on a brand’s social media page.
Appendix 1
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)Contingency 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
## 19 0 0 0 0 0 1 0 0 0
## 21 0 1 0 0 0 0 0 0 0
## 25 0 2 0 0 0 0 0 0 0
## 27 0 0 0 0 0 1 0 0 0
## 29 0 0 0 0 0 2 0 0 0
## 30 0 0 0 0 0 0 0 0 0
## 31 0 1 0 0 0 1 0 0 0
## 32 0 0 0 0 0 3 0 0 0
## 33 0 1 0 0 0 1 0 0 0
## 34 0 3 0 0 0 2 0 0 0
## 35 0 1 0 0 0 2 0 0 0
## 36 0 0 0 0 0 2 0 0 0
## 37 0 1 0 0 0 3 1 0 0
## 38 0 1 0 0 0 2 0 0 0
## 39 0 0 0 0 0 2 0 0 0
## 40 0 0 0 0 0 0 0 0 1
## 41 0 2 0 0 0 2 0 0 0
## 44 0 0 0 0 0 1 0 0 0
## 47 1 2 0 0 0 2 1 0 0
## 48 0 0 0 0 0 0 0 0 0
## 49 0 2 0 0 0 0 0 0 1
## 50 0 1 0 0 0 3 0 0 0
## 51 0 1 0 0 0 0 1 0 1
## 52 0 2 0 0 0 1 1 1 0
## 53 0 0 0 0 0 1 1 0 0
## 54 0 0 0 0 0 0 1 0 0
## 55 0 0 0 0 0 0 0 0 0
## 56 0 0 0 0 0 1 0 0 0
## 57 0 2 0 0 0 0 3 0 1
## 58 0 1 0 0 0 0 1 0 0
## 60 0 1 0 0 0 1 0 0 0
## 62 0 1 0 0 0 0 0 0 0
## 64 0 0 0 0 0 1 0 0 0
## 65 2 0 0 0 0 0 0 0 0
## 66 0 0 0 0 0 0 1 0 1
## 67 1 0 0 0 0 0 0 0 0
## 68 0 0 0 0 0 1 0 1 1
## 69 0 0 0 0 0 1 1 0 0
## 70 0 1 0 0 0 1 0 1 0
## 71 0 0 0 0 0 1 0 0 0
## 72 0 1 0 0 0 0 0 0 0
## 73 0 0 0 0 0 1 0 0 0
## 74 0 0 0 0 0 3 1 0 0
## 75 0 0 0 0 0 1 0 0 0
## 78 0 0 0 0 0 0 1 0 0
## 81 0 0 0 0 0 0 1 0 0
## 82 0 0 0 0 0 0 0 0 1
## 83 0 0 0 0 0 0 0 0 0
## 84 0 0 0 0 2 1 0 1 0
## 85 0 1 0 0 0 1 0 0 0
## 86 0 0 0 0 0 0 0 0 1
## 89 0 1 0 0 0 0 0 0 0
## 90 0 0 0 0 0 0 0 0 0
## 91 0 0 0 0 0 1 0 0 0
## 92 0 0 0 0 0 0 1 0 0
## 93 0 0 0 0 0 1 0 0 0
## 94 0 0 0 0 0 1 0 0 0
## 95 0 0 0 0 0 0 1 0 0
## 96 0 0 0 0 0 0 0 0 1
## 97 0 1 0 0 0 0 1 0 0
## 98 0 0 0 0 0 0 1 0 0
## 99 0 1 0 0 0 0 0 0 1
## 101 0 0 0 0 0 2 0 0 0
## 103 0 0 0 0 0 0 0 0 1
## 104 0 0 0 0 0 1 0 0 0
## 105 1 0 0 0 1 0 0 0 0
## 107 0 0 0 0 0 1 1 0 2
## 108 0 0 0 0 0 0 0 0 0
## 109 0 1 1 0 0 1 0 0 0
## 110 0 0 0 0 0 0 0 0 0
## 111 0 0 0 0 0 1 0 0 1
## 112 0 0 0 0 0 0 0 0 1
## 114 0 1 0 0 0 0 1 0 0
## 115 0 0 0 0 0 0 0 1 0
## 116 0 0 0 0 0 1 0 0 1
## 119 0 0 0 0 0 0 1 0 1
## 120 0 0 0 0 0 0 0 0 1
## 122 1 0 1 0 1 0 0 0 0
## 124 1 0 0 0 0 0 0 0 1
## 125 0 0 0 0 0 0 0 0 0
## 126 0 0 0 0 0 0 1 0 0
## 128 0 0 0 0 0 0 0 0 0
## 130 0 0 0 0 0 0 0 0 1
## 131 0 0 1 0 0 0 0 0 0
## 133 0 0 0 0 0 0 0 0 0
## 135 0 1 1 0 0 0 1 0 0
## 136 0 0 0 0 0 0 0 0 1
## 138 0 1 0 0 0 1 0 0 1
## 143 0 1 0 0 0 0 0 1 0
## 144 0 0 0 0 0 0 0 1 0
## 145 0 0 0 0 0 0 0 0 0
## 146 0 0 0 0 0 0 0 0 0
## 147 0 0 0 0 0 0 0 0 0
## 148 1 0 0 0 0 0 0 0 0
## 149 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0
## 154 0 1 0 0 0 0 0 0 1
## 155 0 0 0 0 0 0 0 0 0
## 156 0 0 1 0 0 0 0 0 0
## 157 0 0 0 0 0 0 0 0 0
## 159 0 0 0 0 0 1 0 0 0
## 160 0 1 0 0 0 0 0 0 0
## 161 0 1 0 0 0 0 0 0 0
## 163 0 1 0 0 0 0 0 0 1
## 168 0 0 0 0 0 0 0 0 0
## 174 0 1 1 0 0 0 0 0 0
## 175 0 0 0 0 0 0 0 0 0
## 176 0 0 0 0 0 0 0 0 1
## 179 0 0 1 0 0 0 0 0 0
## 180 0 0 0 0 0 0 0 0 1
## 181 0 1 0 0 0 0 0 0 0
## 182 0 0 0 0 0 0 0 0 0
## 185 0 0 0 0 0 0 1 0 1
## 187 0 0 0 0 0 0 0 1 0
## 190 0 0 1 0 0 0 0 0 0
## 194 0 0 0 0 1 0 0 1 0
## 199 0 0 0 0 0 0 0 0 0
## 202 0 0 0 0 0 0 1 0 0
## 205 0 1 0 0 0 0 1 0 0
## 206 0 1 0 0 0 0 0 0 0
## 209 0 0 0 0 0 0 0 1 0
## 216 1 0 0 0 0 0 0 0 0
## 223 0 0 0 0 0 0 1 0 0
## 227 0 0 0 0 0 0 0 0 0
## 230 0 0 0 0 0 1 0 0 0
## 234 0 0 0 0 0 0 1 1 0
## 235 1 0 0 0 0 0 0 0 0
## 236 0 1 0 0 0 0 0 0 0
## 237 0 0 1 0 0 0 0 0 0
## 239 0 0 0 0 0 0 0 0 0
## 252 0 0 0 0 0 0 0 0 0
## 263 0 0 0 0 0 0 0 0 0
## 277 0 0 0 0 0 0 1 0 0
## 278 0 0 0 0 0 0 0 0 1
## 283 0 0 0 0 0 0 0 0 1
## 284 0 0 0 0 0 0 0 0 0
## 293 0 1 0 0 0 0 0 0 0
## 309 0 0 0 0 0 1 0 0 0
## 316 0 1 0 0 0 0 0 0 0
## 322 0 0 0 0 0 0 0 0 0
## 335 0 0 0 0 0 0 0 0 0
## 336 0 0 0 0 0 0 0 1 0
## 337 0 1 0 0 0 0 0 0 0
## 348 0 0 0 0 0 0 0 1 0
## 354 0 0 0 0 0 0 0 1 0
## 357 0 0 0 0 0 0 0 0 0
## 393 0 0 0 0 0 0 0 0 0
## 413 0 0 0 0 0 0 0 0 0
## 417 0 0 0 0 0 0 0 1 0
## 434 0 0 0 0 0 0 0 1 0
## 448 0 0 0 0 0 0 0 0 0
## 456 0 0 0 0 0 0 0 1 0
## 473 0 0 0 0 1 0 0 0 0
## 483 0 0 0 0 0 0 0 1 0
## 484 0 0 0 0 0 0 0 0 0
## 490 0 0 0 0 0 0 0 1 0
## 493 0 0 0 0 0 0 0 0 0
## 495 0 0 0 0 1 0 0 0 0
## 539 0 0 0 0 0 0 0 0 0
## 540 0 0 0 0 0 0 0 0 1
## 553 0 0 0 0 0 0 0 0 0
## 567 0 0 0 0 0 0 0 0 0
## 568 0 0 0 0 1 0 0 0 0
## 575 0 1 0 0 0 0 0 0 0
## 577 0 0 0 0 0 0 0 0 0
## 595 0 0 0 0 0 0 0 0 0
## 597 0 0 0 0 0 0 0 0 0
## 611 0 0 0 0 0 0 0 0 0
## 619 0 1 0 0 0 0 0 0 0
## 635 0 0 0 0 0 0 0 1 0
## 650 0 0 0 0 0 0 0 0 0
## 654 0 0 0 0 0 0 0 1 0
## 689 0 0 1 0 0 0 0 0 0
## 690 0 0 0 0 0 0 0 0 0
## 695 0 0 0 0 0 0 0 0 0
## 702 1 0 0 0 0 0 0 0 0
## 721 0 0 0 0 0 0 0 0 0
## 723 0 0 0 0 0 0 0 0 0
## 741 0 0 0 0 0 0 0 0 0
## 746 0 0 0 0 1 0 0 0 0
## 759 0 0 0 0 0 0 0 0 0
## 767 0 0 0 0 0 0 0 0 0
## 770 0 0 0 0 0 0 0 1 0
## 776 0 0 0 0 0 0 0 0 0
## 785 0 0 0 0 0 0 0 0 0
## 789 1 0 0 0 0 0 0 0 0
## 813 1 0 0 0 0 0 0 0 0
## 816 1 0 0 0 0 0 0 0 0
## 825 1 0 0 0 0 0 0 0 0
## 844 1 0 0 0 0 0 0 0 0
## 847 0 0 0 1 0 0 0 0 0
## 861 0 0 0 0 0 0 0 0 0
## 870 1 0 0 0 0 0 0 0 0
## 878 1 0 0 0 0 0 0 0 0
## 889 0 0 0 1 0 0 0 0 0
## 915 0 0 0 0 0 0 0 0 0
## 917 0 0 0 0 0 0 0 1 0
## 918 0 0 0 1 0 0 0 0 0
## 923 0 0 0 0 0 0 0 0 0
## 928 0 0 0 1 0 0 0 1 0
## 932 0 0 1 0 0 0 0 0 0
## 937 0 0 0 0 1 0 0 0 0
## 939 0 0 1 0 0 0 0 0 0
## 942 0 0 0 0 0 0 0 0 0
## 943 0 0 0 1 0 0 0 0 0
## 961 0 0 0 0 0 0 0 1 0
## 967 0 0 1 0 0 0 0 0 0
## 969 0 0 0 0 0 0 0 0 0
## 977 0 0 0 0 0 0 0 0 0
## 992 0 0 0 0 0 0 0 0 0
## 993 1 0 0 0 0 0 0 0 0
## 998 0 0 0 0 0 0 0 0 0
## 1016 0 0 0 1 0 0 0 0 0
## 1034 0 0 0 0 1 0 0 0 0
## 1046 1 0 0 0 0 0 0 0 0
## 1053 0 0 0 1 0 0 0 0 0
## 1061 0 1 0 0 0 0 0 0 0
## 1064 0 0 0 0 0 0 0 0 0
## 1066 0 0 1 0 0 0 0 0 0
## 1071 0 0 0 0 0 0 0 0 0
## 1083 0 0 0 0 0 0 0 0 0
## 1085 0 0 0 1 0 0 0 0 0
## 1112 0 0 1 0 0 0 0 0 0
## 1119 0 0 1 0 0 0 0 0 0
## 1121 0 0 0 1 0 0 0 0 0
## 1145 0 0 0 0 0 0 0 0 0
## 1150 0 0 0 1 0 0 0 0 0
## 1155 0 0 0 0 0 0 0 0 0
## 1164 1 0 0 0 0 0 0 0 0
## 1169 1 0 0 0 0 0 0 0 0
## 1173 0 0 1 1 0 0 0 0 0
## 1180 0 0 0 0 1 0 0 0 0
## 1193 1 0 0 0 0 0 0 0 0
## 1200 0 0 0 1 0 0 0 0 0
## 1205 0 0 0 0 0 0 0 0 0
## 1264 0 0 1 0 0 0 0 0 0
## 1269 0 0 0 0 0 0 0 1 0
## 1292 0 0 0 0 0 0 0 0 0
## 1305 0 0 0 0 0 0 0 0 0
## 1314 0 0 0 0 0 0 0 0 0
## 1326 0 0 0 0 0 0 0 0 0
## 1339 0 0 0 0 0 0 0 0 0
## 1351 0 0 1 0 0 0 0 0 0
## 1374 0 0 0 0 1 0 0 0 0
## 1376 0 0 0 0 0 0 0 0 0
## 1410 1 0 0 0 0 0 0 0 0
## 1414 0 0 0 1 0 0 0 0 0
## 1426 0 0 0 0 0 0 0 0 0
## 1444 0 0 0 1 0 0 0 0 0
## 1466 0 0 1 0 0 0 0 0 0
## 1470 1 0 0 0 0 0 0 0 0
## 1524 0 0 0 0 0 0 0 0 0
## 1537 0 0 0 0 0 0 0 0 0
## 1554 0 0 0 0 0 0 0 0 0
## 1597 0 0 0 0 1 0 0 0 0
## 1661 0 1 0 0 0 0 0 0 0
## 1675 1 0 0 0 0 0 0 0 0
## 1700 0 0 0 0 0 0 0 0 0
## 1707 1 0 0 0 0 0 0 0 0
## 1723 1 0 0 0 0 0 0 0 0
## 1732 0 0 0 0 0 0 0 1 0
## 1758 0 0 0 0 0 0 0 0 0
## 1789 0 0 0 0 0 0 0 0 0
## 1872 0 0 0 0 0 0 0 0 0
## 1930 1 0 0 0 0 0 0 0 0
## 1941 0 0 0 0 0 0 0 0 0
## 1972 0 0 0 0 0 0 0 0 0
## 1985 0 0 0 0 1 0 0 0 0
## 2038 0 0 0 0 1 0 0 0 0
## 2071 0 0 1 0 0 0 0 0 0
## 2079 0 0 0 0 1 0 0 0 0
## 2118 0 0 0 0 0 0 0 0 0
## 2273 0 0 1 0 0 0 0 0 0
## 2325 0 0 0 0 0 0 0 0 0
## 2382 0 0 0 0 0 0 0 0 0
## 2424 0 0 0 0 0 0 0 0 0
## 2467 0 0 0 0 0 0 0 0 0
## 2497 0 0 0 0 0 0 0 1 0
## 2619 0 0 0 0 0 0 0 1 0
## 2723 0 0 0 0 1 0 0 0 0
## 2730 0 0 0 0 0 1 0 0 0
## 2780 0 0 0 0 0 0 0 0 0
## 2800 0 0 0 0 0 0 0 1 0
## 2811 0 0 0 0 0 0 0 1 0
## 2815 0 0 0 0 0 0 0 0 0
## 2839 0 0 0 0 1 0 0 0 0
## 2880 0 0 0 0 1 0 0 0 0
## 2921 0 0 0 0 1 0 0 0 0
## 2942 1 0 0 0 0 0 0 0 0
## 2980 0 0 0 0 0 0 0 0 0
## 3006 0 0 0 0 1 0 0 0 0
## 3045 0 0 1 0 0 0 0 0 0
## 3063 0 0 0 0 0 0 0 0 0
## 3081 0 0 0 0 1 0 0 0 0
## 3154 0 0 0 0 0 0 0 0 0
## 3173 0 0 0 0 0 0 0 0 0
## 3255 0 0 0 0 1 0 0 0 0
## 3284 0 1 0 0 0 0 0 0 0
## 3386 0 1 0 0 0 0 0 0 0
## 3397 0 0 0 0 0 0 0 0 0
## 3402 0 0 0 1 0 0 0 0 0
## 3428 0 0 0 0 1 0 0 0 0
## 3660 0 0 1 0 0 0 0 0 0
## 3690 0 0 0 0 0 0 0 0 0
## 3840 0 0 0 1 0 0 0 0 0
## 3907 0 0 1 0 0 0 0 0 0
## 3961 0 0 0 0 1 0 0 0 0
## 3975 0 0 0 0 1 0 0 0 0
## 4082 0 0 0 0 0 0 0 0 0
## 4142 0 0 0 0 0 0 0 0 1
## 4150 0 0 0 1 0 0 0 0 0
## 4157 0 0 0 0 0 0 0 0 0
## 4332 0 0 0 0 1 0 0 0 0
## 4523 0 0 0 1 0 0 0 0 0
## 4635 0 0 0 0 0 0 0 0 0
## 4684 0 0 0 1 0 0 0 0 0
## 4859 0 0 0 0 0 1 0 0 0
## 4891 0 0 0 1 0 0 0 0 0
## 4957 0 0 1 0 0 0 0 0 0
## 5236 0 0 0 1 0 0 0 0 0
## 5590 0 0 1 0 0 0 0 0 0
## 5696 0 0 0 0 0 0 0 0 0
## 5733 0 0 0 1 0 0 0 0 0
## 5830 0 0 1 0 0 0 0 0 0
## 5913 0 0 0 1 0 0 0 0 0
## 5972 0 0 0 1 0 0 0 0 0
## 6023 0 0 0 1 0 0 0 0 0
## 6104 0 0 1 0 0 0 0 0 0
## 6326 0 0 1 0 0 0 0 0 0
## 6911 0 0 0 0 0 0 0 0 0
## 6929 0 0 0 0 0 0 0 0 0
## 6988 0 0 0 0 0 0 0 0 0
## 7079 0 0 0 1 0 0 0 0 0
## 7607 0 0 0 0 0 0 0 0 0
## 7776 0 0 0 0 0 0 0 0 0
## 7778 0 1 0 0 0 0 0 0 0
## 7869 0 0 0 0 0 0 0 0 0
## 8422 0 0 0 0 0 0 0 0 0
## 8441 0 0 0 0 0 0 0 0 0
## 9159 0 0 0 0 1 0 0 0 0
## 9559 0 0 0 0 0 0 0 0 0
## 9680 0 0 0 0 0 0 0 0 0
## 9809 0 0 0 0 0 0 0 0 0
## 9951 0 0 0 0 0 0 0 0 0
## 9960 0 0 0 0 0 0 0 0 0
## 10026 0 0 0 0 0 0 0 0 0
## 10338 0 0 0 0 0 0 0 0 0
## 10482 0 0 0 1 0 0 0 0 0
## 10918 0 0 1 0 0 0 0 0 0
## 11218 0 0 0 0 0 0 0 0 0
## 12391 0 0 0 0 0 0 0 0 0
## 12530 0 1 0 0 0 0 0 0 0
## 12662 0 0 0 0 0 0 0 0 0
## 13014 0 0 0 1 0 0 0 0 0
## 13610 0 0 0 0 0 0 0 0 0
## 13812 0 0 0 0 0 0 0 0 0
## 15240 0 0 0 0 0 0 0 0 0
## 16272 0 0 0 0 0 0 0 0 0
## 17476 0 0 0 0 0 0 0 0 0
## 17571 0 0 0 0 0 0 0 0 0
## 18121 0 1 0 0 0 0 0 0 0
## 18402 0 0 0 0 0 0 0 0 0
## 20559 0 0 0 0 1 0 0 0 0
## 22547 0 0 0 0 0 0 0 0 0
## 22596 0 0 0 0 0 0 0 0 0
## 22838 0 0 0 0 1 0 0 0 0
## 24355 0 0 0 0 0 0 0 0 0
## 24439 0 0 0 0 0 0 0 0 0
## 24772 0 0 0 0 0 0 0 0 0
## 30440 0 0 0 0 0 0 0 0 0
## 33170 0 0 0 0 0 0 0 0 0
## 34181 0 1 0 0 0 0 0 0 0
## 35250 0 1 0 0 0 0 0 0 0
## 42359 0 1 0 0 0 0 0 0 0
## 47492 0 0 0 0 0 0 0 0 0
## 72155 0 1 0 0 0 0 0 0 0
##
## November October September
## 19 0 0 0
## 21 0 0 0
## 25 0 0 0
## 27 0 0 0
## 29 0 0 0
## 30 0 1 0
## 31 0 0 0
## 32 0 0 0
## 33 0 0 0
## 34 0 1 0
## 35 0 0 0
## 36 0 0 0
## 37 0 0 0
## 38 0 0 2
## 39 0 1 0
## 40 0 0 0
## 41 0 0 0
## 44 0 0 0
## 47 0 0 0
## 48 0 0 1
## 49 0 0 0
## 50 0 0 0
## 51 0 2 0
## 52 1 0 0
## 53 0 0 0
## 54 0 0 0
## 55 0 1 0
## 56 0 0 0
## 57 0 0 0
## 58 0 1 0
## 60 0 0 0
## 62 0 0 0
## 64 0 0 0
## 65 0 0 0
## 66 0 0 0
## 67 0 0 0
## 68 0 0 2
## 69 0 0 0
## 70 0 0 0
## 71 0 0 0
## 72 1 0 0
## 73 1 1 0
## 74 0 0 0
## 75 0 0 0
## 78 1 0 0
## 81 0 1 0
## 82 0 0 0
## 83 0 1 1
## 84 0 0 0
## 85 1 0 0
## 86 0 0 1
## 89 0 0 0
## 90 0 0 1
## 91 0 0 0
## 92 0 1 0
## 93 0 0 0
## 94 1 0 0
## 95 0 0 0
## 96 0 0 0
## 97 0 0 0
## 98 0 0 0
## 99 0 0 0
## 101 0 0 0
## 103 0 0 0
## 104 0 1 0
## 105 0 1 0
## 107 0 0 0
## 108 0 0 1
## 109 0 0 0
## 110 1 1 0
## 111 0 0 0
## 112 0 0 1
## 114 0 0 0
## 115 0 1 2
## 116 0 0 0
## 119 0 0 0
## 120 0 0 0
## 122 0 0 0
## 124 0 0 2
## 125 1 0 0
## 126 0 1 0
## 128 1 0 0
## 130 1 0 1
## 131 0 0 1
## 133 0 0 1
## 135 0 0 0
## 136 0 0 0
## 138 0 0 1
## 143 0 0 1
## 144 0 0 0
## 145 0 0 1
## 146 0 0 1
## 147 2 0 0
## 148 0 1 1
## 149 0 1 1
## 151 1 0 0
## 154 1 0 0
## 155 0 0 1
## 156 0 0 0
## 157 0 1 0
## 159 0 0 0
## 160 0 0 0
## 161 0 0 0
## 163 0 0 0
## 168 0 1 0
## 174 0 0 0
## 175 0 0 1
## 176 0 0 0
## 179 0 0 0
## 180 0 0 0
## 181 0 0 0
## 182 0 1 0
## 185 0 0 0
## 187 0 0 0
## 190 0 0 0
## 194 0 0 0
## 199 0 0 1
## 202 0 0 0
## 205 0 1 0
## 206 0 0 1
## 209 0 1 0
## 216 0 0 0
## 223 0 0 0
## 227 1 0 0
## 230 0 0 0
## 234 0 0 0
## 235 0 0 0
## 236 0 0 0
## 237 0 0 0
## 239 1 0 0
## 252 0 0 1
## 263 0 0 1
## 277 0 0 0
## 278 0 0 0
## 283 0 0 0
## 284 1 0 0
## 293 0 0 0
## 309 0 0 0
## 316 0 0 0
## 322 0 0 1
## 335 0 0 1
## 336 0 0 0
## 337 0 0 0
## 348 0 0 0
## 354 0 0 0
## 357 0 0 1
## 393 0 0 1
## 413 0 1 0
## 417 0 0 0
## 434 0 0 0
## 448 1 0 0
## 456 0 0 0
## 473 0 1 0
## 483 0 0 0
## 484 1 0 0
## 490 0 0 1
## 493 0 0 1
## 495 0 0 0
## 539 0 1 0
## 540 0 0 0
## 553 0 0 1
## 567 0 0 1
## 568 0 0 0
## 575 0 0 0
## 577 0 0 1
## 595 1 0 0
## 597 0 0 1
## 611 0 0 1
## 619 0 0 0
## 635 0 0 0
## 650 0 1 0
## 654 0 0 0
## 689 0 0 0
## 690 0 0 1
## 695 1 0 0
## 702 0 0 0
## 721 0 0 1
## 723 1 0 0
## 741 0 1 0
## 746 0 0 0
## 759 0 0 1
## 767 0 1 0
## 770 0 0 0
## 776 0 1 0
## 785 0 0 1
## 789 0 0 0
## 813 0 0 0
## 816 0 0 0
## 825 0 0 0
## 844 0 0 0
## 847 0 0 0
## 861 1 0 0
## 870 0 0 0
## 878 0 0 0
## 889 0 0 0
## 915 0 1 0
## 917 0 0 0
## 918 0 0 0
## 923 1 0 0
## 928 0 0 0
## 932 0 0 0
## 937 0 0 0
## 939 0 0 0
## 942 1 0 0
## 943 0 0 0
## 961 0 0 0
## 967 0 0 0
## 969 0 0 1
## 977 0 1 0
## 992 0 0 1
## 993 0 0 0
## 998 0 1 0
## 1016 0 0 0
## 1034 0 0 0
## 1046 0 0 0
## 1053 0 0 0
## 1061 0 1 0
## 1064 1 0 0
## 1066 0 0 0
## 1071 0 1 0
## 1083 1 0 0
## 1085 0 0 0
## 1112 0 0 0
## 1119 0 0 0
## 1121 0 0 0
## 1145 2 0 0
## 1150 0 0 0
## 1155 0 0 1
## 1164 0 0 0
## 1169 0 0 0
## 1173 0 0 0
## 1180 0 0 0
## 1193 1 0 0
## 1200 0 0 0
## 1205 1 0 0
## 1264 0 0 0
## 1269 0 0 0
## 1292 0 0 1
## 1305 1 0 0
## 1314 0 0 1
## 1326 0 1 0
## 1339 0 1 0
## 1351 0 0 0
## 1374 0 0 0
## 1376 1 0 0
## 1410 0 0 0
## 1414 0 0 1
## 1426 1 0 0
## 1444 0 0 0
## 1466 0 0 0
## 1470 0 0 0
## 1524 1 0 0
## 1537 1 0 0
## 1554 1 0 0
## 1597 0 0 0
## 1661 0 0 0
## 1675 0 0 0
## 1700 1 0 0
## 1707 0 0 0
## 1723 0 0 0
## 1732 0 0 0
## 1758 0 0 1
## 1789 1 0 0
## 1872 1 0 0
## 1930 0 0 0
## 1941 1 1 0
## 1972 0 1 0
## 1985 0 0 0
## 2038 0 0 0
## 2071 0 0 0
## 2079 0 0 0
## 2118 0 0 1
## 2273 0 0 0
## 2325 1 0 0
## 2382 1 0 0
## 2424 0 1 0
## 2467 1 0 0
## 2497 0 0 0
## 2619 0 0 0
## 2723 0 0 0
## 2730 0 0 0
## 2780 1 0 0
## 2800 0 0 0
## 2811 0 0 0
## 2815 1 0 0
## 2839 0 0 0
## 2880 0 0 0
## 2921 0 0 0
## 2942 0 0 0
## 2980 1 0 0
## 3006 0 0 0
## 3045 0 0 0
## 3063 1 0 0
## 3081 1 0 0
## 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
## 3660 0 0 0
## 3690 0 0 1
## 3840 0 0 0
## 3907 0 0 0
## 3961 0 0 0
## 3975 0 0 0
## 4082 0 1 0
## 4142 0 0 0
## 4150 0 0 0
## 4157 1 0 0
## 4332 0 0 0
## 4523 0 0 0
## 4635 1 0 0
## 4684 0 0 0
## 4859 0 0 0
## 4891 0 0 0
## 4957 0 0 0
## 5236 0 0 0
## 5590 0 0 0
## 5696 0 1 0
## 5733 0 0 0
## 5830 0 0 0
## 5913 0 0 0
## 5972 0 0 0
## 6023 0 0 0
## 6104 0 0 0
## 6326 0 0 0
## 6911 0 0 1
## 6929 0 0 1
## 6988 1 0 0
## 7079 0 0 0
## 7607 1 0 0
## 7776 1 0 0
## 7778 0 0 0
## 7869 0 1 0
## 8422 1 0 0
## 8441 0 1 0
## 9159 0 0 0
## 9559 1 0 0
## 9680 1 0 0
## 9809 1 0 0
## 9951 0 1 0
## 9960 0 1 0
## 10026 0 1 0
## 10338 0 1 0
## 10482 0 0 0
## 10918 0 0 0
## 11218 0 1 0
## 12391 0 1 0
## 12530 0 0 0
## 12662 0 1 0
## 13014 0 0 0
## 13610 0 1 0
## 13812 0 1 0
## 15240 0 1 0
## 16272 0 0 1
## 17476 0 1 0
## 17571 0 1 0
## 18121 0 0 0
## 18402 0 1 0
## 20559 0 0 0
## 22547 0 1 0
## 22596 0 0 1
## 22838 0 0 0
## 24355 0 1 0
## 24439 0 1 0
## 24772 0 0 1
## 30440 0 1 0
## 33170 0 0 1
## 34181 0 0 0
## 35250 0 0 0
## 42359 0 0 0
## 47492 0 0 1
## 72155 0 0 0table(ds$Text, ds$Month)##
## April August December February January July June March May November
## 0 0 2 0 0 0 0 0 0 0 11
## 1 1 0 0 0 0 0 0 0 1 0
## 2 2 3 0 2 1 1 1 3 2 0
## 3 0 1 2 2 2 0 0 2 1 20
## 4 4 2 2 1 0 3 0 21 0 22
## 5 9 26 3 2 0 35 28 2 3 1
## 6 0 1 2 17 0 1 0 1 4 2
## 7 6 3 1 0 25 18 0 1 10 3
## 8 5 1 2 0 0 0 0 0 1 0
## 9 0 16 2 2 0 0 0 1 2 0
## 10 0 1 11 0 2 0 1 0 3 0
## 11 0 0 0 2 0 0 0 0 0 0
## 12 0 0 0 0 0 1 0 0 1 0
## 13 0 1 0 0 1 0 0 0 1 0
## 14 0 2 0 0 0 0 0 0 0 0
## 15 0 0 0 0 0 3 0 0 0 0
## 16 0 1 1 0 0 0 0 0 1 0
## 17 1 1 0 0 0 0 0 0 0 0
## 18 1 1 0 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0 0 1 0
## 20 0 0 4 0 0 0 0 0 0 0
## 22 0 0 1 0 0 0 0 0 0 1
## 24 1 0 0 0 0 0 0 0 0 0
##
## October September
## 0 3 7
## 1 0 0
## 2 4 3
## 3 4 16
## 4 2 5
## 5 15 2
## 6 2 4
## 7 2 4
## 8 22 7
## 9 2 3
## 10 2 2
## 11 1 2
## 12 1 3
## 13 1 1
## 14 0 1
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 20 1 0
## 22 0 0
## 24 0 0Appendix 2
Plots & Tests
boxplot(Daily.Page.Engaged.Users~Month, data=ds,ylab="Month",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Text, data=ds,ylab="Text",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Daily.Total.Reach, data=ds,ylab="Daily Total Reach",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Daily.Viral.Reach, data=ds,ylab="Daily Viral Reach",xlab="Daily Page Engaged Users", horizontal=TRUE )
boxplot(Daily.Page.Engaged.Users~Daily.Total.Consumers, data=ds,ylab="Daily Total Consumers",xlab="Daily Page Engaged Users", horizontal=TRUE )
histogram(~Daily.Page.Engaged.Users,data=ds,col="grey",breaks=6)
histogram(~Daily.Total.Reach,data=ds,col="grey",breaks=6)
histogram(~Text,data=ds,col="grey",breaks=6)
To check the correlation between Daily Page Engaged Users and text:
cor.test(ds$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:
total <- read.csv("D:/Data Analytics Internship - Sameer Mathur/week 4/capstone project/total.csv")
View(total)
ds <- read.csv(paste("total.csv", sep=""))
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