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2020-monitoring-instagram-analysis's Introduction

Here you’ll find a codebook explaining the database structure for the “Undressing Instagram” project.

Story by: Judith Duportail, Nicolas Kayser-Bril

Data collection: Édouard Richard

Data analysis: Kira Schacht

Setup

Follow the steps below in a terminal to load the database from a dump file and save under the name igdb.

pg_restore 02-19_backup > backup.sql
sudo -i -u postgres
dropdb igdb
createdb igdb
igdb < backup.sql

Load packages needed

library("RPostgreSQL")
## Loading required package: DBI

Read database

# create a connection to the postgres database
con = dbConnect(dbDriver("PostgreSQL"), dbname= "igdb", host="127.0.0.1",
                user="postgres", password = params$pwd)

#list tables in the database
dbListTables(con)
##  [1] "auth_user_user_permissions"    "data_donors_datadonationerror"
##  [3] "django_migrations"             "django_session"               
##  [5] "auth_permission"               "auth_group"                   
##  [7] "django_content_type"           "auth_user_groups"             
##  [9] "auth_user"                     "data_donors_donor"            
## [11] "data_donors_datadonation"      "data_donors_donor_following"  
## [13] "data_donors_donorfollowing"    "data_donors_encounter"        
## [15] "django_admin_log"              "ig_observer_igpost"           
## [17] "ig_observer_igimage"           "auth_group_permissions"       
## [19] "ig_observer_iguser"            "ig_observer_gvisionanalyse"

Codebook

data_donors_donor_following

Observed accounts each donor follows

id Pair ID

donor_id Donor ID

iguser_id Observed account ID

data_donors_datadonation

Log of donation runs

id Encounter ID

created Encounter time

ig_posts_seen Number of IG posts seen (12 normally)

donor_id Donor ID

data_donors_encounter

Log of encounters with posts from observed accounts

id Encounter ID

position_in_list Position of encounter in the user feed. Starts counting at 0

data_donation_id ID of donation run

ig_post_id

data_donors_donor

List of donors

id

ig_donor_id

created

last_status

last_status_changed

version

updated

display_name

data_donors_donorfollowing

List of accounts the donor follows, encountered in the feed during donation runs

id

created

following_ig_username

donor_id

ig_observer_igimage

List of images in observed posts

id Image ID

image_url Image URL

ig_post_id Post ID

ig_observer_iguser

List of details for observed IG accounts.

id Account ID

created Timestamp of observation creation

ig_username Username

created_by_id Observation created by

ig_biography IG Bio

ig_business_category_name Business category

ig_full_name Full name

ig_id IG User ID

ig_is_business_account Is business account?

ig_profile_pic URL of profile pic

sex Gender of user m or f

ig_observer_gvisionanalyse

Result of G Vision analysis

id

analyse

ig_image_id

created

ig_observer_igpost

List of posts by observed accounts

id Post ID

created Encountered timestamp

ig_id IG ID

ig_shortcode IG Shortcode

ig_taken_at_timestamp IG picture timestamp

ig_type Type: Image, Video, Image gallery

ig_user_id

created_by_donor

2020-monitoring-instagram-analysis's People

Contributors

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Forkers

cappert

2020-monitoring-instagram-analysis's Issues

Controlling for the number of previous likes at the moment of encounter

Did you get a chance to control for the number of likes a post had when your donors encountered it? If I understood correctly, you successfully proved that racy/nude posts are displayed more often. However, Facebook and Instagram are known to feature posts with higher interaction rates. Thus this might be a case of a "chicken or egg" problem: If posts showing bare skin are more successful in generating likes/interaction among the users who first encounter the post, said posts will consequently most likely be more heavily featured in other users' newsfeed. Note that the reason for this would then not be that they show bare skin but that they have proven to be more popular/"relevant" to other users so far.

Issue with statistical test

Here https://github.com/algorithmwatch/monitoringinstagram/tree/master/analysis#question-2-label-analysis a very significant effect is found. I think this is due to assuming that the created and encountered posts are independent and identically distributed (IID). As shown later, the encounters differ per donor, thus the IID assumption is violated. Violating the IID assumption makes the statistical test unreliable. I think it would be more meaningful to perform the test on data aggregated at the donor level.

PS: Thank you for making this analysis transparant!

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