Fact Check

This is fact-check analysis of Turkish politicians. The data has two sources: The first source is www.dogrulukpayi.com. The second source is compiled by the author during a LAP project of Atilim University.

Data prep

rm(list=ls(all=TRUE)) 


setwd("D:/emre/SkyDrive/makale/FactCheck")
library(lessR)

## 
## lessR 3.5.0      feedback: gerbing@pdx.edu      web: lessRstats.com
## -------------------------------------------------------------------
## 1. Read a text, Excel, SPSS, SAS or R data file from your computer
##    into the mydata data table, enter:  mydata <- Read()  
## 2. For a list of help topics and functions, enter:  Help()
## 3. Use theme function for global settings, Ex: theme(colors="gray")

library(foreign)
library(nnet)
library(effects)
library(car)

## 
## Attaching package: 'car'

## The following object is masked from 'package:effects':
## 
##     Prestige

## The following objects are masked from 'package:lessR':
## 
##     bc, Recode, sp

library(reshape)
library(sjPlot)

## Visit http://strengejacke.de/sjPlot for package-vignettes.

library(plyr)

## 
## Attaching package: 'plyr'

## The following objects are masked from 'package:reshape':
## 
##     rename, round_any

library(pander)


mydata <- read.csv("combinedR.csv")
mydata$Tone <- NULL

Binary DV

mydata$b.fact <- mydata$Scale3
#0 False, 1 True
mydata$b.fact[mydata$b.fact==2] <- NA
mydata$b.fact[mydata$b.fact==3] <- 0
mydata$b.fact <- as.factor(mydata$b.fact)
levels(mydata$b.fact)[levels(mydata$b.fact)=="0"] <- "False"
levels(mydata$b.fact)[levels(mydata$b.fact)=="1"] <- "True"

Sorting the IVs

mydata$Party[mydata$Party == 1] <- "AKP"
mydata$Party[mydata$Party == 2] <- "CHP"
mydata$Party[mydata$Party == 3] <- "MHP"
mydata$Party[mydata$Party == 4] <- "HDP"
mydata$Party[mydata$Party == 5] <- "Others"
mydata$f.party <- as.factor(mydata$Party)
mydata$f.party <- relevel(mydata$f.party, ref = "AKP")


mydata$f.topic <- NA
mydata$f.topic[mydata$Topic=="Ekonomi"] <- 1
mydata$f.topic[mydata$Topic=="Mali Politikalar"] <- 1
mydata$f.topic[mydata$Topic=="Sanayi, Ticaret ve Finans"] <- 1
mydata$f.topic<-recode(mydata$f.topic,"NA=0")
mydata$f.topic[mydata$f.topic==1] <- "Economy"
mydata$f.topic[mydata$f.topic==0] <- "Other Issues"
mydata$f.topic <- as.factor(mydata$f.topic)

Basic Plots

sjp.frq(mydata$b.fact)

sjp.xtab(mydata$f.topic, mydata$b.fact,  margin = "row", bar.pos = "stack", coord.flip = TRUE, 
         show.n = FALSE, show.prc = TRUE,  axis.titles = "Message Medium", geom.colors = c("red", "green"))

sjp.xtab(mydata$f.party, mydata$b.fact, margin = "row", bar.pos = "stack", coord.flip = TRUE, 
         show.n = FALSE, show.prc = TRUE,  axis.titles = "Message Sender Party", geom.colors = c("red", "green"))

The Model

mod <- glm(b.fact ~ f.topic + f.party, data=mydata, family="binomial")
modt <- sjt.glm(mod)

## Waiting for profiling to be done...

pander(modt)

## Warning in pander.default(modt): No pander.method for "sjTable", reverting
## to default.No pander.method for "sjtglm", reverting to default.

• page.style:
• page.content:

  		b.fact
  		OR	CI	p
  (Intercept)		1.28	0.82 – 2.01	.284
  f.topic (Other Issues)		1.88	1.20 – 2.92	.005
  f.party
  CHP		1.69	1.10 – 2.61	.018
  HDP		3.50	1.32 – 12.07	.023
  MHP		1.96	1.15 – 3.47	.017
  Others		0.81	0.45 – 1.49	.493
  Observations		667

• output.complete:

  		b.fact
  		OR	CI	p
  (Intercept)		1.28	0.82 – 2.01	.284
  f.topic (Other Issues)		1.88	1.20 – 2.92	.005
  f.party
  CHP		1.69	1.10 – 2.61	.018
  HDP		3.50	1.32 – 12.07	.023
  MHP		1.96	1.15 – 3.47	.017
  Others		0.81	0.45 – 1.49	.493
  Observations		667

• knitr:

  		b.fact
  		OR	CI	p
  (Intercept)		1.28	0.82 – 2.01	.284
  f.topic (Other Issues)		1.88	1.20 – 2.92	.005
  f.party
  CHP		1.69	1.10 – 2.61	.018
  HDP		3.50	1.32 – 12.07	.023
  MHP		1.96	1.15 – 3.47	.017
  Others		0.81	0.45 – 1.49	.493
  Observations		667

• data:

  coef.name	estimate1	ci.lo1	ci.hi1	p-value1	se1
  (Intercept)	1.28	0.82	2.01	.284	0.23
  f.topicOther Issues	1.88	1.20	2.92	.005	0.23
  f.partyCHP	1.69	1.10	2.61	.018	0.22
  f.partyHDP	3.50	1.32	12.07	.023	0.55
  f.partyMHP	1.96	1.15	3.47	.017	0.28
  f.partyOthers	0.81	0.45	1.49	.493	0.31

sjp.glm(mod)

## Waiting for profiling to be done...