Stock Taking: How big is the queer Wikidata

My reference for how much queer1 history is in linked data is Wikidata. So the first step is to take stock.

I do that in two ways:

  1. Take LGBTIQ* properties and fetch all items that have this relation
  2. Take main LGBTIQ* items and look up which items have a relation

Setup

Of course, I could run many different SPARQL queries. A more efficient way for my workflows is using the package {tidywikidatar} that offers, like the name suggests, tools to retrieve data in a tidy (read: recangular) way. But I’ll also use SPARQL for a bit more complex queries with {WikidataR}.

library(tidywikidatar) # tidy queries
library(WikidataR) # SPARQL queries
library(tidyverse)

# use caching from tidywikidatar
tw_enable_cache()
tw_set_cache_folder(path = fs::path(fs::path_home_r(), "R", "tw_data"))
tw_set_language(language = "en")
tw_create_cache_folder(ask = FALSE)
tw_get_cache_folder()

LGBTIQ* main items

The Wikidata:WikiProject LGBT lists main items and suggested properties for these items. I transferred these to a table in order to use them to get data from Wikidata in bulk.

lgbt_items <- read_tsv("../../../static/wikidata-queer-items.tsv") %>% 
  mutate(q = str_extract(item, "Q[0-9]+"),
         p = str_extract_all(related_property, "P[0-9]+")) %>% 
  unnest(p)

DT::datatable(lgbt_items)

I take every item-property-combination from the input file and get all sub-items.

subitems <- lgbt_items %>%
  pmap_dfr(function(...) {
    current <- tibble(...) %>% 
      select(item, topic, q, p)

    if (current$topic %in% c("Places & organizations", "Arts & culture")) {
      
      current %>%
        tw_query() %>%
        bind_cols(current) %>% 
        mutate(country_id = tw_get_p(id, "P17")) %>% 
        unnest(country_id) %>% 
        mutate(country = tw_get_label(id = country_id))
    } else {
      current %>%
        tw_query() %>%
        bind_cols(current)
    }
  }) %>% 
  select(topic, item, p, p, subitem = id, label, description, country_id, country)

DT::datatable(subitems)

So there are 9438 I can find with this approach. But be aware, that there are duplicates, e.g. because I ask partly for two properties or one subitem can be an LGBT bar as well as a lesbian bar.

When I take this into account, there are 9324 distinct subitems available.

Counted by topic:

topic_share <- subitems %>%
  count(topic) %>%
  mutate(pct = n / sum(n)) %>% 
  arrange(desc(pct))

DT::datatable(topic_share)

As a chart:

topic_share %>% 
  ggplot(aes(reorder(topic, -n), n)) +
  geom_col() +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90))

Counted by topic and item:

topic_item_share <- subitems %>%
  count(topic, item) %>%
  mutate(pct = n / sum(n)) %>% 
  arrange(desc(pct))

DT::datatable(topic_item_share)

That’s quite a long longtail:

topic_item_share %>% 
  ggplot(aes(reorder(item, -n), n)) +
  geom_col() +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 90))

Subitems by country

Since my project revolves around German LGBTIQ* history, country information is important. For items having the topic Places & organizations is queried the country information if available.

Just 16 location that are LGBTIQ* related are based in Germany. Or are labeled as some kind of queer location. For example the famous hotel, bar, sauna “Deutsche Eiche” in Munich (German Wikipedia) has no indication of its role for the gay scene on Wikidata.

subitems %>% 
  filter(topic == "Places & organizations") %>% 
  count(country, sort = T) %>% 
  DT::datatable()

Also, no LGBT archives from Germany can be found in the data, even if they exist as Wikipedia item, for instance the archive I am with: Forum Queeres Archiv. There are really some low hanging fruits out there…

subitems %>% 
  filter(country == "Germany") %>% 
  DT::datatable()

LGBTIQ* properties

Now the same with properties.

lgbt_properties <- read_tsv("../../../static/wikidata-queer-properties.tsv") %>% 
  rename(p = id)

DT::datatable(lgbt_properties)
items_with_properties <- lgbt_properties %>%
  filter(!data_type %in% c("Item", "Lexeme")) %>% 
  pmap_dfr(function(...) {
    current <- tibble(...) %>% 
      select(property, p)
    current %>% pull(p) %>% 
      tw_get_all_with_p() %>% 
      bind_cols(current)
  }) %>% 
  select(property, p, everything())

DT::datatable(items_with_properties)

As counts by propery:

properties_share <- items_with_properties %>%
  count(property) %>%
  mutate(pct = n / sum(n)) %>% 
  arrange(desc(pct))

DT::datatable(properties_share)

Persons, that are not heterosexual

This time, I use plain SPARQL to get the data, based on that query.

non_heterosexuals <- query_wikidata(
  '
  SELECT DISTINCT ?person ?personLabel ?sexualorientationLabel ?sexorgenderLabel
  WHERE {
    ?person wdt:P31 wd:Q5 . #?person is a human
  
    { 
      ?person wdt:P21 ?sexorgender. #?person has ?sexorgender
      #?sexorgender is not male, female, cisgender male, cigender female, or cisgender person
      FILTER(?sexorgender NOT IN (wd:Q6581097, wd:Q6581072, wd:Q15145778, wd:Q15145779, wd:Q1093205)). 
    } UNION {
      ?person wdt:P91 ?sexualorientation . #?person has ?sexualorientation
      FILTER(?sexualorientation != wd:Q1035954). #?sexualorientation is not heterosexual
    }
    SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}'
) %>% as_tibble()

DT::datatable(non_heterosexuals)
non_heterosexuals %>% 
  count(sexualorientationLabel, sort = T) %>% 
  DT::datatable()

Woah, what are those 3718 that have an empty label? Are these subversive attempts to undermine labels that deal with sexual orientation?

A sample shows: Not impossible, but probably not. For instance, quite a lot are painting with humans. Or “anonymous masters”, artists whose sex or gender is unknown, might be queer persons, might be not. So this must be taken with a grain of salt.

(For quite some time now, I want to write a blog post about the book “Queer Data” by Kevin Guyan. This would be an excellent link.)

Non-heterosexual persons linked to Munich

Since the focus of Remove NA is the city of Munich I want to see: What are non-heterosexual or non-cis persons that are somehow linked to Munich?

munich <- query_wikidata('
# LGBT that are linked to Munich
SELECT DISTINCT ?person ?personLabel ?sexualorientationLabel ?sexorgenderLabel
  WHERE {
    ?person wdt:P31 wd:Q5 . #?person is a human
    BIND(wd:Q1726 as ?place). #Q1726 is Munich
    
    { 
      ?person wdt:P21 ?sexorgender. #?person has ?sexorgender
      #?sexorgender is not male, female, cisgender male, cigender female, or cisgender person
      FILTER(?sexorgender NOT IN (wd:Q6581097, wd:Q6581072, wd:Q15145778, wd:Q15145779, wd:Q1093205)). 
    } UNION {
      ?person wdt:P91 ?sexualorientation . #?person has ?sexualorientation
      FILTER(?sexualorientation != wd:Q1035954). #?sexualorientation is not heterosexual
    }
    
    {
      ?person wdt:P19/wdt:P131* ?place. #?person was born in ?place
    }
    UNION {
      ?person wdt:P551/wdt:P131* ?place. #?person resides in ?place
    }
    UNION {
      ?person wdt:P937/wdt:P131* ?place. #?person works in ?place
    }

    SERVICE wikibase:label { bd:serviceParam wikibase:language "[AUTO_LANGUAGE],en". }
}
') %>% as_tibble()

DT::datatable(munich)

What’s next?

These queries are just superficial, relatively obvious queries. But they are a benchmark for future developements.

  1. I use queer, LGBT and LGBTIQ* as synonyms.↩︎