Deepfake Experiment Study 1
- Data Analysis
Analysis of Study 1a-Political type of videos
- Anova results between Control and Treatment
- Calculating the effect of belivebility question in Eta Sqr

Deepfake Experiment Study 1

In this study, we intent to explore do people believe deepfakes and their sharing intentions of deepfake videos.

We pre-registerd our study in this -

Data Analysis

Data sets can be found in our Github Repo -

## New names:
## Rows: 130 Columns: 261
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (261): StartDate, EndDate, Status, Progress, Duration (in seconds), Fini...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## New names:
## Rows: 131 Columns: 261
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (261): StartDate, EndDate, Status, Progress, Duration (in seconds), Fini...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `1P_R_IMPT` -> `1P_R_IMPT...27`
## • `1P_R_INTEREST` -> `1P_R_INTEREST...28`
## • `1P_R_FAMILIAR` -> `1P_R_FAMILIAR...29`
## • `1P_R_NOVEL` -> `1P_R_NOVEL...30`
## • `1P_R_SHARE` -> `1P_R_SHARE...31`
## • `1P_R_SHARE_REASONS` -> `1P_R_SHARE_REASONS...32`
## • `1P_R_SHARE_OTHER` -> `1P_R_SHARE_OTHER...33`
## • `1P_R_DNSHARE_REASONS` -> `1P_R_DNSHARE_REASONS...34`
## • `1P_R_DNSHARE_OTHER` -> `1P_R_DNSHARE_OTHER...35`
## • `2P_DF_IMPT` -> `2P_DF_IMPT...37`
## • `2P_DF_INTEREST` -> `2P_DF_INTEREST...38`
## • `2P_DF_FAMILIAR` -> `2P_DF_FAMILIAR...39`
## • `2P_DF_NOVEL` -> `2P_DF_NOVEL...40`
## • `2P_DF_SHARE` -> `2P_DF_SHARE...41`
## • `2P_DF_SHARE_REASONS` -> `2P_DF_SHARE_REASONS...42`
## • `2P_DF_SHARE_OTHER` -> `2P_DF_SHARE_OTHER...43`
## • `2P_DF_DNSHARE_REASON` -> `2P_DF_DNSHARE_REASON...44`
## • `2P_DF_DNSHARE_OTHER` -> `2P_DF_DNSHARE_OTHER...45`
## • `4P_DF_IMPT` -> `4P_DF_IMPT...47`
## • `4P_DF_INTEREST` -> `4P_DF_INTEREST...48`
## • `4P_DF_FAMILIAR` -> `4P_DF_FAMILIAR...49`
## • `4P_DF_NOVEL` -> `4P_DF_NOVEL...50`
## • `4P_DF_SHARE` -> `4P_DF_SHARE...51`
## • `4P_DF_SHARE_REASONS` -> `4P_DF_SHARE_REASONS...52`
## • `4P_DF_SHARE_OTHER` -> `4P_DF_SHARE_OTHER...53`
## • `4P_DF_DNSHARE_REASON` -> `4P_DF_DNSHARE_REASON...54`
## • `4P_DF_DNSHARE_OTHER` -> `4P_DF_DNSHARE_OTHER...55`
## • `5P_R_IMPT` -> `5P_R_IMPT...57`
## • `5P_R_INTEREST` -> `5P_R_INTEREST...58`
## • `5P_R_FAMILIAR` -> `5P_R_FAMILIAR...59`
## • `5P_R_NOVEL` -> `5P_R_NOVEL...60`
## • `5P_R_SHARE` -> `5P_R_SHARE...61`
## • `5P_R_SHARE_REASONS` -> `5P_R_SHARE_REASONS...62`
## • `5P_R_SHARE_OTHER` -> `5P_R_SHARE_OTHER...63`
## • `5P_R_DNSHARE_REASONS` -> `5P_R_DNSHARE_REASONS...64`
## • `5P_R_DNSHARE_OTHER` -> `5P_R_DNSHARE_OTHER...65`
## • `6P_DF_IMPT` -> `6P_DF_IMPT...67`
## • `6P_DF_INTEREST` -> `6P_DF_INTEREST...68`
## • `6P_DF_FAMILIAR` -> `6P_DF_FAMILIAR...69`
## • `6P_DF_NOVEL` -> `6P_DF_NOVEL...70`
## • `6P_DF_SHARE` -> `6P_DF_SHARE...71`
## • `6P_DF_SHARE_REASONS` -> `6P_DF_SHARE_REASONS...72`
## • `6P_DF_SHARE_OTHER` -> `6P_DF_SHARE_OTHER...73`
## • `6P_DF_DNSHARE_REASON` -> `6P_DF_DNSHARE_REASON...74`
## • `6P_DF_DNSHARE_OTHER` -> `6P_DF_DNSHARE_OTHER...75`
## • `7P_R_IMPT` -> `7P_R_IMPT...77`
## • `7P_R_INTEREST` -> `7P_R_INTEREST...78`
## • `7P_R_FAMILIAR` -> `7P_R_FAMILIAR...79`
## • `7P_R_NOVEL` -> `7P_R_NOVEL...80`
## • `7P_R_SHARE` -> `7P_R_SHARE...81`
## • `7P_R_SHARE_REASONS` -> `7P_R_SHARE_REASONS...82`
## • `7P_R_SHARE_OTHER` -> `7P_R_SHARE_OTHER...83`
## • `7P_R_DNSHARE_REASONS` -> `7P_R_DNSHARE_REASONS...84`
## • `7P_R_DNSHARE_OTHER` -> `7P_R_DNSHARE_OTHER...85`
## • `13P_DF_IMPT` -> `13P_DF_IMPT...87`
## • `13P_DF_INTEREST` -> `13P_DF_INTEREST...88`
## • `13P_DF_FAMILIAR` -> `13P_DF_FAMILIAR...89`
## • `13P_DF_NOVEL` -> `13P_DF_NOVEL...90`
## • `13P_DF_SHARE` -> `13P_DF_SHARE...91`
## • `13P_DF_SHARE_REASONS` -> `13P_DF_SHARE_REASONS...92`
## • `13P_DF_SHARE_OTHER` -> `13P_DF_SHARE_OTHER...93`
## • `13P_DF_DNSHARE_REASO` -> `13P_DF_DNSHARE_REASO...94`
## • `13P_DF_DNSHARE_OTHER` -> `13P_DF_DNSHARE_OTHER...95`
## • `15P_DF_IMPT` -> `15P_DF_IMPT...97`
## • `15P_DF_INTEREST` -> `15P_DF_INTEREST...98`
## • `15P_DF_FAMILIAR` -> `15P_DF_FAMILIAR...99`
## • `15P_DF_NOVEL` -> `15P_DF_NOVEL...100`
## • `15P_DF_SHARE` -> `15P_DF_SHARE...101`
## • `15P_DF_SHARE_REASONS` -> `15P_DF_SHARE_REASONS...102`
## • `15P_DF_SHARE_OTHER` -> `15P_DF_SHARE_OTHER...103`
## • `15P_DF_DNSHARE_REASO` -> `15P_DF_DNSHARE_REASO...104`
## • `15P_DF_DNSHARE_OTHER` -> `15P_DF_DNSHARE_OTHER...105`
## • `17P_R_IMPT` -> `17P_R_IMPT...107`
## • `17P_R_INTEREST` -> `17P_R_INTEREST...108`
## • `17P_R_FAMILIAR` -> `17P_R_FAMILIAR...109`
## • `17P_R_NOVEL` -> `17P_R_NOVEL...110`
## • `17P_R_SHARE` -> `17P_R_SHARE...111`
## • `17P_R_SHARE_REASONS` -> `17P_R_SHARE_REASONS...112`
## • `17P_R_SHARE_OTHER` -> `17P_R_SHARE_OTHER...113`
## • `17P_R_DNSHARE_REASON` -> `17P_R_DNSHARE_REASON...114`
## • `17P_R_DNSHARE_OTHER` -> `17P_R_DNSHARE_OTHER...115`
## • `19P_DF_IMPT` -> `19P_DF_IMPT...117`
## • `19P_DF_INTEREST` -> `19P_DF_INTEREST...118`
## • `19P_DF_FAMILIAR` -> `19P_DF_FAMILIAR...119`
## • `19P_DF_NOVEL` -> `19P_DF_NOVEL...120`
## • `19P_DF_SHARE` -> `19P_DF_SHARE...121`
## • `19P_DF_SHARE_REASONS` -> `19P_DF_SHARE_REASONS...122`
## • `19P_DF_SHARE_OTHER` -> `19P_DF_SHARE_OTHER...123`
## • `19P_DF_DNSHARE_REASO` -> `19P_DF_DNSHARE_REASO...124`
## • `19P_DF_DNSHARE_OTHER` -> `19P_DF_DNSHARE_OTHER...125`
## • `21P_R_IMPT` -> `21P_R_IMPT...127`
## • `21P_R_INTEREST` -> `21P_R_INTEREST...128`
## • `21P_R_FAMILIAR` -> `21P_R_FAMILIAR...129`
## • `21P_R_NOVEL` -> `21P_R_NOVEL...130`
## • `21P_R_SHARE` -> `21P_R_SHARE...131`
## • `21P_R_SHARE_REASONS` -> `21P_R_SHARE_REASONS...132`
## • `21P_R_SHARE_OTHER` -> `21P_R_SHARE_OTHER...133`
## • `21P_R_DNSHARE_REASON` -> `21P_R_DNSHARE_REASON...134`
## • `21P_R_DNSHARE_OTHER` -> `21P_R_DNSHARE_OTHER...135`
## • `23P_R_IMPT` -> `23P_R_IMPT...137`
## • `23P_R_INTEREST` -> `23P_R_INTEREST...138`
## • `23P_R_FAMILIAR` -> `23P_R_FAMILIAR...139`
## • `23P_R_NOVEL` -> `23P_R_NOVEL...140`
## • `23P_R_SHARE` -> `23P_R_SHARE...141`
## • `23P_R_SHARE_REASONS` -> `23P_R_SHARE_REASONS...142`
## • `23P_R_SHARE_OTHER` -> `23P_R_SHARE_OTHER...143`
## • `23P_R_DNSHARE_REASON` -> `23P_R_DNSHARE_REASON...144`
## • `23P_R_DNSHARE_OTHER` -> `23P_R_DNSHARE_OTHER...145`
## • `CTRL_SHARING_PERSP` -> `CTRL_SHARING_PERSP...147`
## • `CTRL_SHARING_PERSP` -> `CTRL_SHARING_PERSP...148`
## • `1P_R_IMPT` -> `1P_R_IMPT...150`
## • `1P_R_INTEREST` -> `1P_R_INTEREST...151`
## • `1P_R_FAMILIAR` -> `1P_R_FAMILIAR...152`
## • `1P_R_NOVEL` -> `1P_R_NOVEL...153`
## • `1P_R_SHARE` -> `1P_R_SHARE...154`
## • `1P_R_SHARE_REASONS` -> `1P_R_SHARE_REASONS...155`
## • `1P_R_SHARE_OTHER` -> `1P_R_SHARE_OTHER...156`
## • `1P_R_DNSHARE_REASONS` -> `1P_R_DNSHARE_REASONS...157`
## • `1P_R_DNSHARE_OTHER` -> `1P_R_DNSHARE_OTHER...158`
## • `2P_DF_IMPT` -> `2P_DF_IMPT...159`
## • `2P_DF_INTEREST` -> `2P_DF_INTEREST...160`
## • `2P_DF_FAMILIAR` -> `2P_DF_FAMILIAR...161`
## • `2P_DF_NOVEL` -> `2P_DF_NOVEL...162`
## • `2P_DF_SHARE` -> `2P_DF_SHARE...163`
## • `2P_DF_SHARE_REASONS` -> `2P_DF_SHARE_REASONS...164`
## • `2P_DF_SHARE_OTHER` -> `2P_DF_SHARE_OTHER...165`
## • `2P_DF_DNSHARE_REASON` -> `2P_DF_DNSHARE_REASON...166`
## • `2P_DF_DNSHARE_OTHER` -> `2P_DF_DNSHARE_OTHER...167`
## • `4P_DF_IMPT` -> `4P_DF_IMPT...168`
## • `4P_DF_INTEREST` -> `4P_DF_INTEREST...169`
## • `4P_DF_FAMILIAR` -> `4P_DF_FAMILIAR...170`
## • `4P_DF_NOVEL` -> `4P_DF_NOVEL...171`
## • `4P_DF_SHARE` -> `4P_DF_SHARE...172`
## • `4P_DF_SHARE_REASONS` -> `4P_DF_SHARE_REASONS...173`
## • `4P_DF_SHARE_OTHER` -> `4P_DF_SHARE_OTHER...174`
## • `4P_DF_DNSHARE_REASON` -> `4P_DF_DNSHARE_REASON...175`
## • `4P_DF_DNSHARE_OTHER` -> `4P_DF_DNSHARE_OTHER...176`
## • `5P_R_IMPT` -> `5P_R_IMPT...177`
## • `5P_R_INTEREST` -> `5P_R_INTEREST...178`
## • `5P_R_FAMILIAR` -> `5P_R_FAMILIAR...179`
## • `5P_R_NOVEL` -> `5P_R_NOVEL...180`
## • `5P_R_SHARE` -> `5P_R_SHARE...181`
## • `5P_R_SHARE_REASONS` -> `5P_R_SHARE_REASONS...182`
## • `5P_R_SHARE_OTHER` -> `5P_R_SHARE_OTHER...183`
## • `5P_R_DNSHARE_REASONS` -> `5P_R_DNSHARE_REASONS...184`
## • `5P_R_DNSHARE_OTHER` -> `5P_R_DNSHARE_OTHER...185`
## • `6P_DF_IMPT` -> `6P_DF_IMPT...186`
## • `6P_DF_INTEREST` -> `6P_DF_INTEREST...187`
## • `6P_DF_FAMILIAR` -> `6P_DF_FAMILIAR...188`
## • `6P_DF_NOVEL` -> `6P_DF_NOVEL...189`
## • `6P_DF_SHARE` -> `6P_DF_SHARE...190`
## • `6P_DF_SHARE_REASONS` -> `6P_DF_SHARE_REASONS...191`
## • `6P_DF_SHARE_OTHER` -> `6P_DF_SHARE_OTHER...192`
## • `6P_DF_DNSHARE_REASON` -> `6P_DF_DNSHARE_REASON...193`
## • `6P_DF_DNSHARE_OTHER` -> `6P_DF_DNSHARE_OTHER...194`
## • `7P_R_IMPT` -> `7P_R_IMPT...195`
## • `7P_R_INTEREST` -> `7P_R_INTEREST...196`
## • `7P_R_FAMILIAR` -> `7P_R_FAMILIAR...197`
## • `7P_R_NOVEL` -> `7P_R_NOVEL...198`
## • `7P_R_SHARE` -> `7P_R_SHARE...199`
## • `7P_R_SHARE_REASONS` -> `7P_R_SHARE_REASONS...200`
## • `7P_R_SHARE_OTHER` -> `7P_R_SHARE_OTHER...201`
## • `7P_R_DNSHARE_REASONS` -> `7P_R_DNSHARE_REASONS...202`
## • `7P_R_DNSHARE_OTHER` -> `7P_R_DNSHARE_OTHER...203`
## • `13P_DF_IMPT` -> `13P_DF_IMPT...204`
## • `13P_DF_INTEREST` -> `13P_DF_INTEREST...205`
## • `13P_DF_FAMILIAR` -> `13P_DF_FAMILIAR...206`
## • `13P_DF_NOVEL` -> `13P_DF_NOVEL...207`
## • `13P_DF_SHARE` -> `13P_DF_SHARE...208`
## • `13P_DF_SHARE_REASONS` -> `13P_DF_SHARE_REASONS...209`
## • `13P_DF_SHARE_OTHER` -> `13P_DF_SHARE_OTHER...210`
## • `13P_DF_DNSHARE_REASO` -> `13P_DF_DNSHARE_REASO...211`
## • `13P_DF_DNSHARE_OTHER` -> `13P_DF_DNSHARE_OTHER...212`
## • `15P_DF_IMPT` -> `15P_DF_IMPT...213`
## • `15P_DF_INTEREST` -> `15P_DF_INTEREST...214`
## • `15P_DF_FAMILIAR` -> `15P_DF_FAMILIAR...215`
## • `15P_DF_NOVEL` -> `15P_DF_NOVEL...216`
## • `15P_DF_SHARE` -> `15P_DF_SHARE...217`
## • `15P_DF_SHARE_REASONS` -> `15P_DF_SHARE_REASONS...218`
## • `15P_DF_SHARE_OTHER` -> `15P_DF_SHARE_OTHER...219`
## • `15P_DF_DNSHARE_REASO` -> `15P_DF_DNSHARE_REASO...220`
## • `15P_DF_DNSHARE_OTHER` -> `15P_DF_DNSHARE_OTHER...221`
## • `17P_R_IMPT` -> `17P_R_IMPT...222`
## • `17P_R_INTEREST` -> `17P_R_INTEREST...223`
## • `17P_R_FAMILIAR` -> `17P_R_FAMILIAR...224`
## • `17P_R_NOVEL` -> `17P_R_NOVEL...225`
## • `17P_R_SHARE` -> `17P_R_SHARE...226`
## • `17P_R_SHARE_REASONS` -> `17P_R_SHARE_REASONS...227`
## • `17P_R_SHARE_OTHER` -> `17P_R_SHARE_OTHER...228`
## • `17P_R_DNSHARE_REASON` -> `17P_R_DNSHARE_REASON...229`
## • `17P_R_DNSHARE_OTHER` -> `17P_R_DNSHARE_OTHER...230`
## • `19P_DF_IMPT` -> `19P_DF_IMPT...231`
## • `19P_DF_INTEREST` -> `19P_DF_INTEREST...232`
## • `19P_DF_FAMILIAR` -> `19P_DF_FAMILIAR...233`
## • `19P_DF_NOVEL` -> `19P_DF_NOVEL...234`
## • `19P_DF_SHARE` -> `19P_DF_SHARE...235`
## • `19P_DF_SHARE_REASONS` -> `19P_DF_SHARE_REASONS...236`
## • `19P_DF_SHARE_OTHER` -> `19P_DF_SHARE_OTHER...237`
## • `19P_DF_DNSHARE_REASO` -> `19P_DF_DNSHARE_REASO...238`
## • `19P_DF_DNSHARE_OTHER` -> `19P_DF_DNSHARE_OTHER...239`
## • `21P_R_IMPT` -> `21P_R_IMPT...240`
## • `21P_R_INTEREST` -> `21P_R_INTEREST...241`
## • `21P_R_FAMILIAR` -> `21P_R_FAMILIAR...242`
## • `21P_R_NOVEL` -> `21P_R_NOVEL...243`
## • `21P_R_SHARE` -> `21P_R_SHARE...244`
## • `21P_R_SHARE_REASONS` -> `21P_R_SHARE_REASONS...245`
## • `21P_R_SHARE_OTHER` -> `21P_R_SHARE_OTHER...246`
## • `21P_R_DNSHARE_REASON` -> `21P_R_DNSHARE_REASON...247`
## • `21P_R_DNSHARE_OTHER` -> `21P_R_DNSHARE_OTHER...248`
## • `23P_R_IMPT` -> `23P_R_IMPT...249`
## • `23P_R_INTEREST` -> `23P_R_INTEREST...250`
## • `23P_R_FAMILIAR` -> `23P_R_FAMILIAR...251`
## • `23P_R_NOVEL` -> `23P_R_NOVEL...252`
## • `23P_R_SHARE` -> `23P_R_SHARE...253`
## • `23P_R_SHARE_REASONS` -> `23P_R_SHARE_REASONS...254`
## • `23P_R_SHARE_OTHER` -> `23P_R_SHARE_OTHER...255`
## • `23P_R_DNSHARE_REASON` -> `23P_R_DNSHARE_REASON...256`
## • `23P_R_DNSHARE_OTHER` -> `23P_R_DNSHARE_OTHER...257`

Analysis of Study 1a-Political type of videos

The influence of believing and sharing political videos :

#Likert scale has 
likely_values <-  c(
  "very unlikely",
  "moderately unlikely",
  "slightly unlikely",
  "slightly likely",
  "moderately likely",
  "very likely"
)
consume_values <- c(
  "Less than 1 hour per day",
  "1-2 hours per day",
  "2-3 hours per day",
  "3-4 hours per day",
  "5+ hours per day"
)
likley_shory_values <- c("Very unlikely",
                         "Unlikely",
                         "Neither likely nor unlikely",
                         "Likely",
                         "Very likely")
knowledgable_values <- c(
  "Very unknowledgeable",
  "Somewhat Unknowledgeable",
  "Neither",
  "Somewhat Knowledgeable",
  "Very knowledgeable"
)
easy_levels <- c("Very difficult",
                 "Difficult",
                 "Neither difficult nor easy",
                 "Easy",
                 "Very easy")

# Clean Political data frame separating video into deepfake/real, share likely values and, 
clean_data_pol <- data_raw_pol |> filter(Finished == "True")  |>
  select(ResponseId, matches("_DF_"), matches("_R_")) |>
  pivot_longer(-ResponseId, values_drop_na = TRUE) |>
  separate(name, c("video", "fake", "question", "question_detail")) |>
  group_by(ResponseId) |>
  mutate(condition = if_else(any(grepl("BELIEVE", question)), "Treatment", "Control")) |>
  ungroup() |>
  filter(question == "SHARE", question_detail != "REASONS", question_detail != "OTHER") |> 
  select(-question_detail) |>
  pivot_wider(names_from = question, values_from = value) |>
  unite("video", video, fake) |>
  mutate(
    SHARE = str_to_lower(SHARE),
    share = ordered(SHARE,levels = likely_values), 
    share_numerical = as.numeric(share)
  ) |>
  separate(video, c("video", "type")) |>
  mutate(type = if_else(type == "R", "Real", "Deepfake")) |>
  select(-SHARE)

## Warning: Expected 4 pieces. Additional pieces discarded in 1621 rows [6, 12, 18, 24, 30,
## 36, 42, 48, 54, 60, 66, 72, 79, 86, 93, 100, 107, 114, 121, 122, ...].

## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 756 rows [73, 80, 87, 94,
## 101, 108, 115, 123, 130, 137, 144, 152, 232, 239, 246, 253, 260, 267, 274, 281,
## ...].

head(clean_data_pol)

## # A tibble: 6 × 6
##   ResponseId        video type     condition share             share_numerical
##   <chr>             <chr> <chr>    <chr>     <ord>                       <dbl>
## 1 R_ezxZ4Fh2teO3yCZ 2P    Deepfake Control   very unlikely                   1
## 2 R_ezxZ4Fh2teO3yCZ 4P    Deepfake Control   slightly unlikely               3
## 3 R_ezxZ4Fh2teO3yCZ 6P    Deepfake Control   very unlikely                   1
## 4 R_ezxZ4Fh2teO3yCZ 13P   Deepfake Control   slightly unlikely               3
## 5 R_ezxZ4Fh2teO3yCZ 15P   Deepfake Control   very unlikely                   1
## 6 R_ezxZ4Fh2teO3yCZ 19P   Deepfake Control   very unlikely                   1

clean_data_ent <- data_raw_ent |> filter(Finished == "True")  |>
  select(ResponseId, matches("_DF_"), matches("_R_")) |>
  pivot_longer(-ResponseId, values_drop_na = TRUE) |>
  separate(name, c("video", "fake", "question", "question_detail")) |>
  group_by(ResponseId) |>
  mutate(condition = if_else(any(grepl("BELIEVE", question)), "Treatment", "Control")) |>
  ungroup() |>
  filter(question == "SHARE", question_detail != "REASONS", question_detail != "OTHER") |> 
  select(-question_detail) |>
  pivot_wider(names_from = question, values_from = value) |>
  unite("video", video, fake) |>
  mutate(
    SHARE = str_to_lower(SHARE),
    share = ordered(SHARE,levels = likely_values), 
    share_numerical = as.numeric(share)
  ) |>
  separate(video, c("video", "type")) |>
  mutate(type = if_else(type == "R", "Real", "Deepfake")) |>
  select(-SHARE)

## Warning: Expected 4 pieces. Additional pieces discarded in 1610 rows [7, 14, 21, 28, 35,
## 42, 49, 56, 63, 70, 77, 84, 90, 96, 102, 108, 114, 120, 126, 132, ...].

## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 780 rows [1, 8, 15, 22,
## 29, 36, 43, 50, 57, 64, 71, 78, 230, 237, 244, 251, 258, 265, 272, 279, ...].

head(clean_data_ent)

## # A tibble: 6 × 6
##   ResponseId        video type     condition share               share_numerical
##   <chr>             <chr> <chr>    <chr>     <ord>                         <dbl>
## 1 R_3Jh3kj1y3kaZbyl 8E    Deepfake Treatment very unlikely                     1
## 2 R_3Jh3kj1y3kaZbyl 10E   Deepfake Treatment moderately unlikely               2
## 3 R_3Jh3kj1y3kaZbyl 12E   Deepfake Treatment very unlikely                     1
## 4 R_3Jh3kj1y3kaZbyl 14E   Deepfake Treatment very unlikely                     1
## 5 R_3Jh3kj1y3kaZbyl 16E   Deepfake Treatment very unlikely                     1
## 6 R_3Jh3kj1y3kaZbyl 24E   Deepfake Treatment moderately unlikely               2

library(stringr)
All_combined_pol_ent <- rbind( clean_data_ent,clean_data_pol) 

All_videos_combined<- All_combined_pol_ent |> 
  mutate (Videotype = ifelse(str_detect (All_combined_pol_ent$video, "P"), "Political", "Entertainment"))

tail (All_videos_combined)

## # A tibble: 6 × 7
##   ResponseId        video type  condition share        share_numerical Videotype
##   <chr>             <chr> <chr> <chr>     <ord>                  <dbl> <chr>    
## 1 R_3lukBqCKhPAkNjn 1P    Real  Control   very likely                6 Political
## 2 R_3lukBqCKhPAkNjn 5P    Real  Control   slightly un…               3 Political
## 3 R_3lukBqCKhPAkNjn 7P    Real  Control   slightly un…               3 Political
## 4 R_3lukBqCKhPAkNjn 17P   Real  Control   very likely                6 Political
## 5 R_3lukBqCKhPAkNjn 21P   Real  Control   slightly li…               4 Political
## 6 R_3lukBqCKhPAkNjn 23P   Real  Control   very unlike…               1 Political

Analyzing the variance of sharing likely behavior when there is a deepfake believablity question

#---------Political videos---------
clean_data_pol |> 
  group_by(condition,type) |> 
  summarise(share = mean(share_numerical))

## `summarise()` has grouped output by 'condition'. You can override using the
## `.groups` argument.

## # A tibble: 4 × 3
## # Groups:   condition [2]
##   condition type     share
##   <chr>     <chr>    <dbl>
## 1 Control   Deepfake  1.54
## 2 Control   Real      1.81
## 3 Treatment Deepfake  1.70
## 4 Treatment Real      1.71

clean_data_pol |> ggplot(aes(condition,share_numerical)) + 
  # facet_wrap(vars(video)) +
  stat_summary(
    fun.data = mean_cl_boot,
    geom = "pointrange",
    shape = 21,
    fill = "white"
  )

#---------Entertainment videos-----------
clean_data_ent |> 
  group_by(condition,type) |> 
  summarise(share = mean(share_numerical))

## `summarise()` has grouped output by 'condition'. You can override using the
## `.groups` argument.

## # A tibble: 4 × 3
## # Groups:   condition [2]
##   condition type     share
##   <chr>     <chr>    <dbl>
## 1 Control   Deepfake  1.80
## 2 Control   Real      2.32
## 3 Treatment Deepfake  1.57
## 4 Treatment Real      1.90

clean_data_ent |> ggplot(aes(condition,share_numerical)) + 
  # facet_wrap(vars(video)) +
  stat_summary(
    fun.data = mean_cl_boot,
    geom = "pointrange",
    shape = 21,
    fill = "white"
     )

clean_data_ent |> 
  group_by(condition,type) |> 
  summarise(share = mean(share_numerical))

## `summarise()` has grouped output by 'condition'. You can override using the
## `.groups` argument.

## # A tibble: 4 × 3
## # Groups:   condition [2]
##   condition type     share
##   <chr>     <chr>    <dbl>
## 1 Control   Deepfake  1.80
## 2 Control   Real      2.32
## 3 Treatment Deepfake  1.57
## 4 Treatment Real      1.90

clean_data_ent |> ggplot(aes(condition,share_numerical)) + 
  # facet_wrap(vars(video)) +
  stat_summary(
    fun.data = mean_cl_boot,
    geom = "pointrange",
    shape = 21,
    fill = "white"
     )

# ----- All video types ------------------

All_videos_combined |> 
  group_by(condition,type, Videotype ) |> 
  summarise(share = mean(share_numerical))

## `summarise()` has grouped output by 'condition', 'type'. You can override using
## the `.groups` argument.

## # A tibble: 8 × 4
## # Groups:   condition, type [4]
##   condition type     Videotype     share
##   <chr>     <chr>    <chr>         <dbl>
## 1 Control   Deepfake Entertainment  1.80
## 2 Control   Deepfake Political      1.54
## 3 Control   Real     Entertainment  2.37
## 4 Control   Real     Political      1.84
## 5 Treatment Deepfake Entertainment  1.57
## 6 Treatment Deepfake Political      1.70
## 7 Treatment Real     Entertainment  1.93
## 8 Treatment Real     Political      1.72

All_videos_combined |> ggplot(aes(condition,share_numerical)) + 
  # facet_wrap(vars(video)) +
  stat_summary(
    fun.data = mean_cl_boot,
    geom = "pointrange",
    shape = 21,
    fill = "white"
     )

Anova results between Control and Treatment

#-------1-One way ANOVA -----------------

pol_oneway <- aov(share_numerical ~ condition, data = clean_data_pol)

summary(pol_oneway)

##               Df Sum Sq Mean Sq F value Pr(>F)
## condition      1    0.4   0.424   0.326  0.568
## Residuals   1534 1993.2   1.299

#----------2-Two way ANOVA without interactions----------

#ANOVA Two way without interactions 

pol_twoway_no_interactions <- aov (share_numerical ~ type + condition,
                                   data= clean_data_pol)

summary (pol_twoway_no_interactions)

##               Df Sum Sq Mean Sq F value Pr(>F)  
## type           1    8.0   8.021   6.194 0.0129 *
## condition      1    0.4   0.424   0.327 0.5673  
## Residuals   1533 1985.2   1.295                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#-----------3-Two way ANOVA with Interactions----------------
#ANOVA 2 way with interactions 

pol_twoway_with_interactions <- aov (share_numerical ~type * condition,
                                     data=clean_data_pol)

summary ( pol_twoway_with_interactions)

##                  Df Sum Sq Mean Sq F value Pr(>F)  
## type              1    8.0   8.021   6.209 0.0128 *
## condition         1    0.4   0.424   0.328 0.5668  
## type:condition    1    5.9   5.910   4.574 0.0326 *
## Residuals      1532 1979.3   1.292                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#-------1-One way ANOVA -----------------

ent_oneway <- aov(share_numerical ~ condition, data = clean_data_ent)

summary(ent_oneway)

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## condition      1   40.3   40.29    21.2 4.46e-06 ***
## Residuals   1546 2937.5    1.90                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#----------2-Two way ANOVA without interactions----------

#ANOVA Two way without interactions 

ent_twoway_no_interactions <- aov (share_numerical ~ type + condition,
                                   data= clean_data_ent)

summary (ent_twoway_no_interactions)

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## type           1   70.8   70.78   38.14 8.39e-10 ***
## condition      1   40.3   40.29   21.71 3.44e-06 ***
## Residuals   1545 2866.8    1.86                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#-----------3-Two way ANOVA with Interactions----------------
#ANOVA 2 way with interactions 

ent_twoway_with_interactions <- aov (share_numerical ~type * condition,
                                     data=clean_data_ent)

summary ( ent_twoway_with_interactions)

##                  Df Sum Sq Mean Sq F value   Pr(>F)    
## type              1   70.8   70.78  38.163 8.31e-10 ***
## condition         1   40.3   40.29  21.726 3.42e-06 ***
## type:condition    1    3.3    3.31   1.784    0.182    
## Residuals      1544 2863.5    1.85                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#-------1-One way ANOVA -----------------

combined_oneway <- aov(share_numerical ~ condition, data = All_videos_combined)

summary(combined_oneway)

##               Df Sum Sq Mean Sq F value  Pr(>F)   
## condition      1     16  15.765    9.74 0.00182 **
## Residuals   3082   4989   1.619                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#----------2-Two way ANOVA without interactions----------

#ANOVA Two way without interactions 

combined_twoway_no_interactions <- aov (share_numerical ~ type + condition,
                                   data= All_videos_combined)

summary (combined_twoway_no_interactions)

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## type           1     63   63.35  39.627 3.51e-10 ***
## condition      1     16   15.77   9.862   0.0017 ** 
## Residuals   3081   4925    1.60                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

#-----------3-Two way ANOVA with Interactions----------------
#ANOVA 2 way with interactions 

combined_twoway_with_interactions <- aov (share_numerical ~type * condition,
                                     data=All_videos_combined)

summary ( combined_twoway_with_interactions)

##                  Df Sum Sq Mean Sq F value   Pr(>F)    
## type              1     63   63.35  39.685 3.41e-10 ***
## condition         1     16   15.77   9.876  0.00169 ** 
## type:condition    1      9    8.75   5.482  0.01927 *  
## Residuals      3080   4916    1.60                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# Combined being political and entertainment effect 

combined_twoway_without_interactions_videotype <- 
  aov (share_numerical ~type + Videotype+ condition,
       data=All_videos_combined)

summary ( combined_twoway_without_interactions_videotype )

##               Df Sum Sq Mean Sq F value   Pr(>F)    
## type           1     63   63.35   39.90 3.06e-10 ***
## Videotype      1     34   34.45   21.70 3.33e-06 ***
## condition      1     16   16.27   10.25  0.00138 ** 
## Residuals   3080   4890    1.59                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Calculating the effect of belivebility question in Eta Sqr

# One way 
library (lsr)

# Eta sqr one way 
Eta_oneway <-etaSquared(pol_oneway)

print (Eta_oneway)

##                 eta.sq  eta.sq.part
## condition 0.0002126591 0.0002126591

#Eta sqr for twoway without interactions 

Eta_twoway_nointeraction<-etaSquared(pol_twoway_no_interactions)

print(Eta_twoway_nointeraction)

##                 eta.sq  eta.sq.part
## type      0.0040235686 0.0040244245
## condition 0.0002126591 0.0002135182

Eta_twoway_with_interaction<-etaSquared(pol_twoway_with_interactions)
print(Eta_twoway_with_interaction)

##                      eta.sq  eta.sq.part
## type           0.0040235686 0.0040363927
## condition      0.0002126591 0.0002141556
## type:condition 0.0029644487 0.0029770602

#Clean Political data frame to explore the effect of measured other variables
Exploratory_data<- read.csv("C:/Users/Dell/OneDrive/Documents/CREST Postdoc/Deepfakes Experiment/Analysis/Study_1aPolitical/Study1_Political_Control_Treament.csv")

individual_data_pol <- data_raw_pol |> filter(Finished == "True") |>
  mutate(Duration = as.numeric(`Duration (in seconds)`),
         AGE = as.numeric(AGE),
         BROWSE_INTERNET = ordered(BROWSE_INTERNET, levels = consume_values),
         USE_SNS = ordered(USE_SNS, levels = consume_values),
         WATCHING_BEHAVIOR = ordered(WATCHING_BEHAVIOR, levels =     likley_shory_values),
         SHARING_BEHAVIOR = ordered(SHARING_BEHAVIOR, levels = likley_shory_values),
         KNOW_DEEPFAKE = KNOW_DEEPFAKE == "Yes",
         EXP_CREATE_DF = EXP_CREATE_DF == "Yes",
         KNOW_CREATE_DF = ordered(KNOW_CREATE_DF, levels = knowledgable_values),
         EASE_CREATE_DF = ordered(EASE_CREATE_DF, levels = easy_levels)
         # KNOW_CREATE_DF = KNOW_CREATE_DF == "Yes"
  ) |>
  select(
    ResponseId,
    Duration,
    AGE,
    BROWSE_INTERNET,
    USE_SNS,
    SNS_PLATFORM_USE,
    WATCHING_BEHAVIOR,
    SHARING_BEHAVIOR,
    KNOW_DEEPFAKE,
    KNOW_CREATE_DF,
    EXP_CREATE_DF,
    EASE_CREATE_DF
  ) |> mutate(
    SNS_PLATFORM_USE = strsplit(SNS_PLATFORM_USE,split = ","),
    value = TRUE
  ) |> 
  unnest() |> 
  mutate(SNS_PLATFORM_USE = paste0("Plat_",SNS_PLATFORM_USE)) |> 
  pivot_wider(names_from = SNS_PLATFORM_USE, values_fill = FALSE)

## Warning: `cols` is now required when using `unnest()`.
## ℹ Please use `cols = c(SNS_PLATFORM_USE)`.

head(individual_data_pol)

## # A tibble: 6 × 29
##   ResponseId        Duration   AGE BROWSE_INTERNET   USE_SNS   WATCHING_BEHAVIOR
##   <chr>                <dbl> <dbl> <ord>             <ord>     <ord>            
## 1 R_ezxZ4Fh2teO3yCZ      546    25 3-4 hours per day 2-3 hour… Very likely      
## 2 R_oXNZs9xxIElDLOh      826    23 5+ hours per day  3-4 hour… Very likely      
## 3 R_sHFekJJgmMVPn2N      564    25 5+ hours per day  5+ hours… Very likely      
## 4 R_wRA5rgVjq1efbt7      979    30 2-3 hours per day Less tha… Likely           
## 5 R_1NkXnrpmhSPr3ZX      545    22 3-4 hours per day 1-2 hour… Very likely      
## 6 R_2CEbMiHBHh4LB4T      637    30 5+ hours per day  1-2 hour… Very likely      
## # ℹ 23 more variables: SHARING_BEHAVIOR <ord>, KNOW_DEEPFAKE <lgl>,
## #   KNOW_CREATE_DF <ord>, EXP_CREATE_DF <lgl>, EASE_CREATE_DF <ord>,
## #   Plat_YouTube <lgl>, Plat_WhatsApp <lgl>, Plat_TikTok <lgl>,
## #   Plat_Snapchat <lgl>, Plat_Reddit <lgl>, `Plat_Microsoft Teams` <lgl>,
## #   Plat_LinkedIn <lgl>, Plat_Facebook <lgl>, Plat_Instagram <lgl>,
## #   Plat_Messenger <lgl>, Plat_Twitter <lgl>, Plat_Pinterest <lgl>,
## #   Plat_Quora <lgl>, Plat_Telegram <lgl>, Plat_WeChat <lgl>, …

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

Deepfake Experiment

Sasahara Lab

2023-05-02

Deepfake Experiment Study 1

Data Analysis

Analysis of Study 1a-Political type of videos

Anova results between Control and Treatment

Calculating the effect of belivebility question in Eta Sqr