README.md

In hrbrmstr/misinfo: Tools to Perform 'Misinformation' Analysis on a Text Corpus

misinfo

Tools to Perform ‘Misinformation’ Analysis on a Text Corpus

Description

Tools to Perform ‘Misinformation’ Analysis on a Text Corpus

What’s Inside The Tin

The following functions are implemented:

• mi_analyze_document: Run a one or more documents through a misinformation/bias sentiment analysis
• mi_plot_document_summary: Plot raw frequency count summary from a processed corpus
• mi_plot_individual_frequency: Plot raw frequency count summary from a processed corpus
• mi_use_builtin: Use a built-in ‘sentiment’ word/phrase list
• word_doc_to_txt: Convert a Word document to a plaintext document

Installation

devtools::install_github("hrbrmstr/misinfo")

Usage

library(misinfo)
library(hrbrthemes)
library(tidyverse)

# current verison
packageVersion("misinfo")

## [1] '0.1.0'

Try it out on the built-in corpus

mi_analyze_document(
  path = system.file("extdat", package="misinfo"),
  pattern = ".*txt$",
  bias_type = "explanatory",
  sentiment_list = mi_use_builtin("explanatory")
) -> corpus

glimpse(corpus)

## List of 4
##  $ bias_type           : chr "explanatory"
##  $ corpus              :Classes 'tbl_df', 'tbl' and 'data.frame':    7 obs. of  3 variables:
##   ..$ doc   : chr [1:7] "CNET - How US cybersleuths decided Russia hacked the DNC" "CNN - DNC hack" "IVN - CNN Covering For DNC Corruption" "Red Nation- FBI Didn't Analyze DNC servers" ...
##   ..$ text  : chr [1:7] "how us cybersleuths decided russia hacked the dnc  digital clues led security pros to agencies in putins govern"| __truncated__ "dnc hack what you need to know  by tal kopan cnn  updated 130 pm et tue june 21 2016  cyber experts russia hack"| __truncated__ "cnn covering for dnc corruption  by jeff powers in opinion jun 27 2017  we should have known this hysteria woul"| __truncated__ "fbi didnt analyze dnc servers before issuing hacking report  posted at 839 pm on january 4 2017 by jennifer van"| __truncated__ ...
##   ..$ doc_id: chr [1:7] "CNET - How US cybersleuths dec" "CNN - DNC hack" "IVN - CNN Covering For DNC Cor" "Red Nation- FBI Didn't Analyze" ...
##  $ frequency_summary   :Classes 'tbl_df', 'tbl' and 'data.frame':    7 obs. of  5 variables:
##   ..$ doc_id     : chr [1:7] "CNET - How US cybersleuths dec" "CNN - DNC hack" "IVN - CNN Covering For DNC Cor" "Red Nation- FBI Didn't Analyze" ...
##   ..$ n          : int [1:7] 12 3 3 2 33 6 13
##   ..$ doc_num    : chr [1:7] "1" "2" "3" "4" ...
##   ..$ total_words: int [1:7] 1327 1024 880 377 5119 1430 1694
##   ..$ pct        : num [1:7] 0.00904 0.00293 0.00341 0.00531 0.00645 ...
##  $ individual_frequency:Classes 'tbl_df', 'tbl' and 'data.frame':    119 obs. of  6 variables:
##   ..$ doc_id     : chr [1:119] "CNET - How US cybersleuths dec" "CNN - DNC hack" "IVN - CNN Covering For DNC Cor" "Red Nation- FBI Didn't Analyze" ...
##   ..$ keyword    : chr [1:119] "because" "because" "because" "because" ...
##   ..$ ct         : int [1:119] 1 0 2 0 2 2 2 0 0 0 ...
##   ..$ doc_num    : chr [1:119] "1" "2" "3" "4" ...
##   ..$ total_words: int [1:119] 1327 1024 880 377 5119 1430 1694 1327 1024 880 ...
##   ..$ pct        : num [1:119] 0.000754 0 0.002273 0 0.000391 ...
##  - attr(*, "class")= chr "misinfo_corpus"

corpus$frequency_summary

## # A tibble: 7 x 5
##   doc_id                             n doc_num total_words     pct
##   <chr>                          <int> <chr>         <int>   <dbl>
## 1 CNET - How US cybersleuths dec    12 1              1327 0.00904
## 2 CNN - DNC hack                     3 2              1024 0.00293
## 3 IVN - CNN Covering For DNC Cor     3 3               880 0.00341
## 4 Red Nation- FBI Didn't Analyze     2 4               377 0.00531
## 5 The Nation - A New Report Rais    33 5              5119 0.00645
## 6 VOA- Think Tank- Cyber Firm at     6 6              1430 0.00420
## 7 WaPo - DNC hack                   13 7              1694 0.00767

corpus$individual_frequency

## # A tibble: 119 x 6
##    doc_id                         keyword      ct doc_num total_words      pct
##    <chr>                          <chr>     <int> <chr>         <int>    <dbl>
##  1 CNET - How US cybersleuths dec because       1 1              1327 0.000754
##  2 CNN - DNC hack                 because       0 2              1024 0       
##  3 IVN - CNN Covering For DNC Cor because       2 3               880 0.00227 
##  4 Red Nation- FBI Didn't Analyze because       0 4               377 0       
##  5 The Nation - A New Report Rais because       2 5              5119 0.000391
##  6 VOA- Think Tank- Cyber Firm at because       2 6              1430 0.00140 
##  7 WaPo - DNC hack                because       2 7              1694 0.00118 
##  8 CNET - How US cybersleuths dec therefore     0 1              1327 0       
##  9 CNN - DNC hack                 therefore     0 2              1024 0       
## 10 IVN - CNN Covering For DNC Cor therefore     0 3               880 0       
## # ... with 109 more rows

mi_plot_individual_frequency(corpus)

mi_plot_individual_frequency(corpus, FALSE)

mi_plot_document_summary(corpus)

mi_plot_document_summary(corpus, FALSE)

Compare a corpus across a bunch of biases

map_df(c("uncertainty", "sourcing", "retractors", "explanatory"), ~{

  # compute individual bias scores
  mi_analyze_document(
    path = system.file("extdat", package="misinfo"),
    pattern = ".*txt$",
    bias_type = .x,
    sentiment_list = mi_use_builtin(.x)
  ) -> corpus

 mutate(corpus$frequency_summary, bias = corpus$bias_type)

}) -> biases

mutate(biases, pct = ifelse(pct == 0, NA, pct)) %>% 
  ggplot(aes(sprintf("Doc #%s", doc_num), bias, fill=pct)) +
  geom_tile(color="#2b2b2b", size=0.125) +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_discrete(expand=c(0,0)) +
  viridis::scale_fill_viridis(direction=-1, na.value="white") +
  labs(x=NULL, y=NULL, title="Word List Usage Heatmap (normalized)") +
  theme_ipsum_rc(grid="")

distinct(biases, doc_num, doc_id, total_words) %>% 
  knitr::kable(format="markdown")

| doc_id | doc_num | total_words | | :----------------------------- | :------- | -----------: | | CNET - How US cybersleuths dec | 1 | 1327 | | CNN - DNC hack | 2 | 1024 | | IVN - CNN Covering For DNC Cor | 3 | 880 | | Red Nation- FBI Didn’t Analyze | 4 | 377 | | The Nation - A New Report Rais | 5 | 5119 | | VOA- Think Tank- Cyber Firm at | 6 | 1430 | | WaPo - DNC hack | 7 | 1694 |

hrbrmstr/misinfo documentation built on May 7, 2019, 9:36 a.m.