Download, clean and arrange the SWoW

A great investigation of this data set has already been done by Julia Silge and the pipeline and preliminary analysis here uses that work. The end of this pipeline creates a classic edge list useful for treating the SWoW data set as a graph.

library(here)
library(tidyverse)
library(knitr)
library(broom)
library(ggrepel)
library(scales)
library(tidygraph)
library(ggraph)
library(igraph)
library(magrittr)

swow_forward <- readRDS(here("static", "swow_forward.rds"))

Explore what it is that people assocaite with ‘pleasure’

The first thing to do is limit our set to the words that followed ‘pleasure’. Then we can look at the most frequently occurring.

swow_forward %>%
  filter(from == "pleasure") %>% 
  count(to, sort = TRUE) %>% 
  top_n(20) %>%
  ggplot(aes(fct_reorder(to, n), n)) +
    geom_col() +
    coord_flip() +
    labs(x = "Words used to respond to the prompt 'pleasure'", y  = "Count of times participants responded with each word")

And we can compare the constructs men and women associate with pleasure using Julia Silge’s code too.

swow_forward %>%
    filter(from == "pleasure",
           gender %in% c("Men", "Women")) %>%
    group_by(to) %>%
    filter(n() > 10) %>%
    ungroup %>%
    count(gender, to, sort = TRUE) %>%
    spread(gender, n, fill = 0) %>%
    mutate_if(is.numeric, funs((. + 1) / (sum(.) + 1))) %>%
    mutate(logratio = log2(Women / Men)) %>%
    top_n(15, abs(logratio)) %>%
    ggplot(aes(fct_reorder(to, logratio), logratio, 
               fill = logratio < 0)) +
    geom_col(show.legend = FALSE) +
    coord_flip() +
    labs(x = NULL, y  = "log odds ratio (Women/Men)",
         title = "Words that Small World of Words participants associate with pleasure",
         subtitle = str_wrap("Amongst other things, these differences highlight the perils of recommendation engines designed by men", 80))

And again using Julia Silge’s code we can see how our participants conceptions of pleasure change with age.

swow_freq <- swow_forward %>%
  filter(from == "pleasure",
         age < 80) %>%
  mutate(age = age %/% 5 * 5) %>%
  count(age, to) %>%
  complete(age, to, fill = list(n = 0)) %>%
  group_by(age) %>%
  mutate(age_total = sum(n),
         percent = n / age_total) %>%
  ungroup() %>%
  group_by(to) %>%
  filter(sum(n) > 15) %>%
  ungroup()

slopes <- swow_freq %>%
  nest(-to) %>%
  mutate(models = map(data, ~ glm(cbind(n, age_total) ~ age, ., 
                                  family = "binomial"))) %>%
  unnest(map(models, tidy)) %>%
  filter(term == "age") %>%
  arrange(estimate) %>%
  mutate(p.value = p.adjust(p.value))

slopes %>%
  top_n(6, -p.value) %>%
  inner_join(swow_freq) %>%
  ggplot(aes(age, percent)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~ to) +
  scale_y_continuous(labels = percent_format()) +
  labs(x = "Age of participant",
       y = "Frequency of word in association with pleasure",
       title = "What words do Small World of Words' participants associate with pleasure?",
       subtitle = "So the association of pleasure with fun and pain decreases in frequency with age")

All these differences highlight the notion that different groups of people have different associations with pleasure. While it would be easy to conclude from this that the personalisation of a recommendation system would then the the logical way to proceed, I feel a cause for concern with this approach.

The challenge to autonomy and personal identity from recommendation engines outlined by Silvia Milano, Mariarosaria Taddeo and Luciano Floridi notes that recommendation engines are not just reflecting users preferences but are active collaborators in creating these preferences:

“…the recommender system’s model of each user is continuously reconfigured on the basis of the feedback provided by other users’ interactions with the system. In this sense, the system should not be conceptualised as tracking a pre-established user identity and tailoring its recommendations to it, but rather as contributing to the construction of the user identity dynamically”

From this perspective I see two challenges from recommendation engines:

1. Users come to believe that the pleasures shown to them are the pleasures shared by society at large or at least a considerably large number of people.
2. The possibilities of other pleasures are hidden from users so that they are less able to expand their horizons or even come into constant with contrasting views.

These risks amount to a problem for the growth of users as human beings. It is through meeting alternate views of the world and coming to terms with the existence of other minds that we grow.

To highlight these risks I thought a recommendation engine for pleasure might help.

Make an app to recommend pleasure

Thanks to Julia Single’s code that I used above, these data were already in an edge list. This let me import the ego network around the word ‘pleasure’ to Gephi and look at the top 20 degree nodes:

To build the recommendation engine I again used tidygraph and ggraph. This meant first extracting the ego network around “pleasure” from the full SWoW data set.

Then I added some node characteristics:

• Centrality
• Community

I also included:

• Only the strongest edges
• Only the most connected nodes

pleasure_ego_undirected <- swow_forward %>%
  select(from, to) %>%
  graph_from_data_frame() %>%
  make_ego_graph(nodes = "pleasure", order = 1, mode = "all") %>%
  extract2(1) %>%
  as_tbl_graph(directed = FALSE)

pleasure_ego_trim <- pleasure_ego_undirected %>%
  activate(edges) %>%
  group_by(from, to) %>%
  mutate(weight = sum(n())) %>%
  ungroup() %>%
  distinct() %>%
  filter(weight > 10) %>%
  activate(nodes) %>%
  mutate(community = as.factor(group_walktrap())) %>%
  mutate(centrality = centrality_degree())  %>%
  top_n(100, centrality) %>%
  mutate(name = as.character(name))

Community detection often ends up with a few large, well characterized communities then a large number of small communities with one or two nodes in them.

pleasure_ego_trim %>%
  activate(nodes) %>%
  as.tibble() %>%
  count(community) %>%
  ggplot(aes(x = fct_reorder(community, n), y = n)) + 
  geom_col() + 
  coord_flip() + 
  labs(x = "community")

So there are four serious communities. I clumped the others into a single community and have a look at their makeup.

pleasure_ego_trim <- pleasure_ego_trim %>%
  activate(nodes) %>%
  mutate(community = fct_collapse(community, "5" = c("5", "6", "7", "8", "9", 
                                                     "10", "12", "13", "15", 
                                                     "16", "19", "20", "27", 
                                                     "28", "29", "30", "35", "37"))) %>%
  mutate(id = row_number()) %>%
  as_tbl_graph()

pleasure_ego_trim %>%
  activate(nodes) %>%
  as_tibble() %>%
  group_by(community) %>%
  top_n(10) %>%
  ungroup() %>%
  ggplot(aes(x = fct_reorder(name, centrality), y = centrality, fill = community)) + 
  geom_col() + 
  facet_wrap(vars(community), nrow = 2, scales = "free") + 
  coord_flip() + 
  guides(fill = FALSE) + 
  labs(title = str_wrap("Top ten most connected words in each community", 80), 
       x = "name", subtitle = "Remember that community 5 is made up of all the smaller communities")

These communities have clustered into somewhat interpretable groups.

1. A community related to sensations especially in the outdoors
2. Emotions (this community contains ‘pleasure’ itself)
3. Cooking and food
4. To some this community might be antonyms of pleasure. But I feel they may be pleasures for introverts.
5. This is the community of all the other communities.

Finally, I plotted the ego-network. The most central word in each community is labeled.

selected <- c("flower", "happy", "food", "sad", "pain")

ggraph(pleasure_ego_trim, layout = 'graphopt', charge = .05) + 
  geom_edge_link(alpha = .1, colour = "white") + 
  geom_node_text(aes(label = if_else(name %in% selected, as.character(name), ""), colour = community), nudge_y = 100) + 
  geom_node_point(aes(alpha = if_else(name %in% selected, 1, 0), colour = community, size = centrality)) +
  guides(colour = FALSE, alpha = FALSE, size = FALSE) + 
  theme_graph(background = "black")

So, the logic of the recommendation engine is to have:

1. An initialization stage in which the user is given a choice of three words from a set of five. Each of the five is a randomly chosen words from each of the five communities in the ego network.
2. Sum the weights of the edges to each word in the ego network from each of the selected words. Suggest the word with the greatest summed edge weight as the recommended pleasure.
3. If the recommendation is accepted then add it to the selected words and repeat the step above. If the recommendation is rejected remove it from the ego network and repeat the step above.
4. Repeat steps 2 and 3 as many times as is useful.

Its a really basic recommendation engine but it gets across the point that if we follow the suggestions of an algorithm we will end up with our preferences being constrained by the priorities of the programmer and the preferences of the public.

You can play with the recommendation engine here:

Feel free to tweet me your results and let me know what you think.

I’m not sure what the best way of handling the ethical problems with recommendation engines is. But I hope this project has highlighted some of the issues.

Finally, my application for this project to be installed at UniSA’s Museum of Discovery (MOD) was accepted! I’ve worked with the amazing team at Lightbulb Digital to make a version of this app that is way more beautiful and informative than I could have done with my amateur programming skills. They’ve done an amazing job and I’m really excited by the end result. So, from Friday the 24th of May, you can come along to MOD and investigate the the perils of leaving an algorithm to recommend pleasures to you.