researchings

Saturday, 3 August 2019

Using Python to Analyse a Dream Diary

A few years ago I started keeping a dream diary. The volume of writing has now accumulated to a point where I thought it might be useful to analyse it systematically on a larger scale. This post describes how I've used Python programming language to do this. Quantitative analysis of writing, and dreams in particular, are topics that already have a fair bit of academic literature and tools dedicated to them. I took a couple of approaches developed by other people (referenced below) and made them into a Python script for personal use.

Here is a quick summary of what the script does:

Takes a text file containing a collection of dreams and quantifies their contents. This gives the frequency of words related to categories like cognitive processes, positive/negative emotions, social interactions, sensory experiences etc.

Compares these scores to those derived from dreams of the general population. This makes the scores meaningful by showing which aspects of an individual's dream life are statistically different from the baselines found by researchers.

Graphically plots changes in the content of dreams over time. This can be used to explore the temporal development of specific themes (e.g. changes in emotional valence over several months/years)

Plots a network of names (people, places), where nodes represent names and links represent the occurrence of these entities together in the same dream.

Below I describe each of these functionalities and present examples of insights from an individual's dream diary. If you want to try it for yourself, the script can be found on GitHub, along with the example diary. This is my first time sharing code, all feedback welcome. The script can also be tweaked to work with a normal diary or expressive writing, where baselines from the general population can be added for comparison. I suggest having a corpus of at least 100 diary entries to work with.

Dream Content

To analyse the content of dreams, the script uses the Linguistic Inventory and Word Count (LIWC) system. This was developed by psychologist James Pennebaker and colleagues to automatically analyse text samples and calculate the frequencies of words used in different domains. The intention was to identify sets of words that reflect basic emotional and cognitive dimensions. The latest version of LIWC (2015) has over 70 categories including emotions, cognition, personal concerns, work and leisure activities, as well as grammar and vocabulary dimensions like the use of pronouns and verbs. Each category is triggered when the input text features a relevant word. For instance, the words “dish”, “eat” or “pizza” will contribute to the score for the “ingestion” category, and the words “worried” or “fearful” will count towards the “anxiety” category. Some words belong to more than one category. For example, the word “cried” will increment the scores for categories such as “sadness”, “negative emotion” and “past focus”. These scores are expressed as the percentage of words in the text sample that are related to each category.

The LIWC scoring system is a proprietary piece of software. The script presented here uses only the LIWC dictionary, which is a text file with a long list of words and the categories to which they belong. This file is fed into Python and category scores are calculated using LIWC dictionary reader functions developed by Sean Rife. I tweaked his script to work with the 2015 version of the LIWC dictionary.

Comparison to Baselines

Researchers have applied the LIWC system of analysis to study different kinds of text samples (e.g. blogs, novels, expressive writing). Most relevant here are the recent results of psychologists Kelly Bulkeley and Mark Graves (2018), who used LIWC to study the linguistic properties of dreams. Using over five thousand dream reports from a diverse collection of people, the authors identified baseline rates for the usage of each LIWC category. These baselines enable us to compare the linguistic contents of our personal dreams with those of a “normal” person, to highlight distinguishing aspects that may be unique to us as individuals.

In order to quantify the difference between a person's scores and population baselines, the script uses measurements known as Z-scores. These scores take into account the variability of scores in the population to estimate the degree to which an individual's value is different from the population average. If a Z-score exceeds an absolute threshold of 1.96, this would give 95% certainty that the value is significantly different.

Below is an example output produced using a publicly available diary containing 315 dreams from a woman named Merri. I scraped her diary from the DreamBank repository managed by Adam Schneider & William Domhoff.

The plot displays a selection of only the top 10 LIWC categories that show the greatest difference between the individual's dream diary and the population baselines. The height of the bars corresponds to the degree to which the category scores are different from the population average (either higher or lower). The red dashed line indicates the 95% confidence interval, a threshold that would be exceeded when the difference is statistically significant.

From the above plot we can see that Merri's use of words generally lies within the expected thresholds. Still, some interesting differences can be observed. For example, she uses fewer function words, more 2nd person pronouns ("you") and fewer 1st person singulars ("i") compared to the general population. For more insight into what this means, check out this great TED talk on the psychology of pronouns.

Temporal Analysis

To examine changes in dream content over time, the script splits the diary into quarterly chunks (3 months each) and calculates the LIWC scores for every period. It then identifies and plots the categories that show the greatest change over time.

Merri's dream diary shows a general decline in 2nd person pronouns ("you"), a decline in conjunctions ("conj") and a gradual increase in filler words over time.

If there is a specific category that interests you that isn't captured in the automated plot, it can be plotted separately like so:

The "affect" category covers words related to affective processes including positive and negative emotions. From the above plot, we see that Merri's references to affect peaked in the third quarter of 1999, and declined since then. Notice that these changes are very small, with an absolute drop in frequency from roughly 2.8% to 1.8% in relation to the overall number of words.

Extraction of Names

Dream diaries often include references to people or places by their names. It is possible to use named entity recognition functions from the nltk library in Python to automatically extract all the names mentioned in a diary. I used these to make some network visualisations.

In the image below, names are represented by nodes and the links show which names have appeared together in a dream. This can give a sense of whether certain people or places play a central role or have a tendency to cluster together. The script produces a very basic network plot like this:

This particular plot shows only names that occur in at least 3 dreams and co-occur with their neighbours beyond a certain level (these thresholds can be adjusted). We see hubs formed around Merri's two siblings, Dora and Rudy, whose names frequently get mentioned alongside other characters and place names.

There is a lot that can be improved in the visualisations and analyses. Nonetheless, I hope this post gives a sense of some of the dream insights that can be automated with Python.

I'd love to know if this project will be useful or interesting to some of you. If you have any feedback or suggestions please let me know!

GitHub repository: https://github.com/mpriestley/dream-analysis

References:
Bulkeley, K., & Graves, M. (2018). Using the LIWC program to study dreams. Dreaming, 28(1), 43.
Pennebaker, J.W., Boyd, R.L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. Austin, TX: University of Texas at Austin.
Sean Rife's PsyLex functions for reading a LIWC dictionary in Python: https://github.com/seanrife/psyLex
DreamBank repository of dream reports: http://www.dreambank.net/

Wednesday, 16 July 2014

What gets upvoted and downvoted on reddit? - part 2

This post contains the comment voting results to accompany the main findings discussed earlier. The survey contained 15 submission titles with single response comments sourced from reddit, where participants had to vote on each comment and then requested to write brief explanations for these decisions. I read all the explanations and classified them into recurring reasons for voting. Due to the huge volume of responses that were analysed manually under time constraints, some subtle concepts had to be clumped together into cruder categories, and it is possible that I made occasional errors during the analyses. Therefore, these results are only here to give us an approximate impression of what can influence people's votes. Please note that some people gave multiple reasons for voting, all of which were included. Vague reasons like "good comment" or "meh" were ignored. In case you start wondering on what basis the titles/comments were selected for the survey, it was mainly to just have some variety and address the theoretical framework proposed in my thesis.

Below are screenshots of the response comments (in dark blue) with pie charts that display their received votes, followed by summaries of common voting explanations. Please forgive the lack of colour.
I will be available in the comments section to answer any questions.

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
61% said the comment was relevant	33% didn't find it interesting	31% didn't find it interesting
26% drank/liked the same milk	33% didn't drink/like that milk	23% found it irrelevant to themselves
24% said that it contributes	11% thought it was irrelevant	18% said the comment was relevant
9% liked the presentation	11% didn't like the presentation	12% didn't find it compelling

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
64% said the comment was funny	30% didn't find it funny	24% found it irrelevant to themselves
20% said it was relevant	20% said it had bad grammar	16% didn't find it original
13% liked that kind of jokes	20% fount it unoriginal	16% didn't find it funny
6% said that it contributes	18% didn't like that kind of jokes	5% found it funny

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
50% liked the sourcing	59% said it is irrelevant	18% said it is irrelevant
46% found it interesting	29% didn't like the sources	18% found it irrelevant to themselves
20% said it was relevant	6% said that it doesn't contribute	16% found it interesting
19% liked the presentation	6% found it interesting	15% said it is too long

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
33% said the comment is relevant	32% didn't like the presentation	28% said it was irrelevant to themselves
18% liked the presentation	26% didn't like the sources	15% didn't like the presentation
18% said that it contributes	16% disagreed	10% said it is too long
15% found it reasonable

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
53% found it funny	29% found it unintelligent	24% didn't find it funny
35% said that it is relevant	14% didn't like that kind of jokes	16% said it was irrelevant to themselves
7% liked that kind of jokes	14% found it unoriginal	10% found it unoriginal
6% found it intelligent	14% said it was irrelevant to themselves	10% found it relevant

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
41% agreed with the comment	51% disagreed with the comment	16% found it irrelevant to themselves
24% said that it contributes	21% said it lacks sources	14% said it was too long
20% liked the presentation	12% found it irrelevant	13% disagreed with the comment
16% found it interesting	9% didn't like the presentation	9% said it lacks sources

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
61% said the comment is true	40% found it unhelpful	20% found it unhelpful
32% agreed with it	22% found it rude	17% said it is true
7% found it helpful	19% said that it doesn't contribute	8% found it irrelevant
	11% found it irrelevant	7% said it's a troll

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
47% said that it's relevant	40% didn't feel sympathetic	15% didn't find it interesting
29% felt sympathetic	40% didn't think it's true	15% felt sad
19% felt sad	20% thought that it's a troll	15% found it irrelevant to themselves
14% found it interesting		11% didn't find it compelling

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
58% found it interesting	71% said it is inaccurate	16% said it lacks sources
19% said that it's relevant	14% found it unintelligent	12% found it interesting
17% found it educational	14% said it lacked sources	11% didn't find it interesting
16% said that it contributes		7% knew it already

10.

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
55% agreed with the comment	34% found the comment hostile	17% found it irrelevant to themselves
15% said it's true	20% didn't like the presentation	16% found it hostile
12% said that it contributes	11% didn't find it intelligent	11% agreed with the comment
8% said that it's relevant	10% said that it doesn't contribute	10% didn't like the presentation

11.

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
30% found it reasonable	56% disagreed with the advice	24% found it irrelevant to themselves
23% said it's helpful	11% found it unhelpful
11% said it's relevant	11% didn't think it's relevant
4% agreed with the advice

12.

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
56% found it cute	29% found it irrelevant to themselves	30% found it irrelevant to themselves
15% thought it was relevant	18% didn't find it original	7% didn't find it interesting
14% said that it contributes	11% didn't find it intelligent	6% found it cute
6% found it positive	11% had an issue with puppies

13.

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
41% felt sympathetic	32% didn't think it contributes	17% found it irrelevant to themselves
39% shared the feeling	11% thought it was attention seeking	14% didn't think it contributes
4% felt sad

14.

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
33% praised the responder	62% said it lacks detail	58% said it lacks detail
20% thought it was relevant	15% didn't think it was true	10% didn't find it interesting
19% thought it was worthy	9% didn't find it interesting	7% thought it was relevant
14% said it lacks detail	6% didn't think it contributes	4% praised the responder

15.

Of those who upvoted:	Of those who downvoted:	Of those who ignored:
72% agreed with the comment	37% disliked superfluous "almost"	14% didn't find it interesting
9% said it was true	25% disagreed	11% found it unoriginal
9% thought it was relevant	13% thought it was karma whoring	10% agreed with the comment
4% disliked superfluous "almost"		10% thought it doesn't contribute

What gets upvoted and downvoted on reddit? (new research) - part 1

My name is Maria Priestley and this post is based on research that was done as part of my undergraduate degree in Biological Anthropology at the University of Durham. All data were collected during November 2013 and used for nonprofit educational purposes. A published version of the findings can be found in PLoS One journal.

We all have a vague idea of the reasons why content gets rated positively or negatively on reddit, but it's always nice to get empirical evidence to explore these things. I attempted to do this using an online questionnaire consisting of two parts. Redditors were first asked to vote on a selection of 15 comments and to write brief explanations for these decisions. All explanations were then read and recurring reasons for upvotes and downvotes were identified. The second part of the survey had 29 questions asking participants to rate the importance of different content traits as influences on voting (e.g. content sounding intelligent, following reddit rules, expressing agreeable opinions...). These ratings underwent statistical analyses to group correlated traits into a smaller number of broad influences, which were then examined against variables like age, gender and reddit membership duration. The participant sample consisted of 489 adults recruited mainly from /r/TheoryOfReddit, /r/Anthropology and /r/SampleSize. I will summarise the main findings here, but there is a second instalment here containing full details on the comment voting results.

The chart below shows the average importance of different influences on upvotes and downvotes as reported by the participants.

We can see that wishing content to be seen by others was rated as the most important influence in upvotes. The other influences appear less important, but they give a better idea of the specific content traits that may contribute to upvotes and downvotes.

Informational skill was an influence that covered questions on the interestingness and intellectual value of content, so its high importance is understandable. When voting on comments, participants often explained their decisions with references to how interesting the comment was, as seen in the example below.

Common reasons for voting on other comments also included references to quality of writing and sources.

These results were interpreted in an evolutionary context as part of my undergraduate anthropology thesis. With regard to the high importance of informational value in voting, I suggested that upvotes could be a way of showing deference to people who are deemed to possess informational skill. This kind of tendency may have helped our ancestors to secure access to skilful individuals who possessed valuable information.

Relevance and compliance with subreddit rules also had reasonably high importance scores. Statistical analyses showed that the importance of these influences decreased slightly with age, and that women and participants with longer reddit membership durations gave higher scores. These differences were small but statistically significant. When writing about their voting decisions, participants often mentioned whether or not comments were relevant to the submission title.

With regard to prosociality, people tended to prefer content that was helpful and considerate of others, with rudeness or aggressiveness often eliciting downvotes. For example, a comment stating: “Don't ask for advice from reddit, it's full of the most smug self righteous assholes this side of the internet” was downvoted by 65% of participants, most of whom said that it was unhelpful or rude. This is unsurprising, given that prosocial norms are almost universal and vital across different cultures. Numerous scientists have even suggested that people may be biologically adapted to favour prosocial behaviour and to punish violators.

For unoriginality and Karma-whoring as influences in downvotes, it was found that their importance decreased slightly with age. The statistical analyses also indicated that longer-serving redditors were most averse to these traits. Average importance scores for each membership duration group are shown below.

Empathy, agreement and humour were quite important with regard to upvotes, and women gave significantly higher importance to this group of influences compared to men. The survey contained another question asking participants about the extent to which their voting is influenced by emotional reactions to content, where women also gave higher scores. Both differences are displayed in the chart below.

In the written vote explanations, participants often commented on their emotional reactions as reasons for upvoting emotive comments.

Disagreement was quite important in downvotes. The statistical procedure I used showed that people didn't give consistent levels of importance to agreement and disagreement (perhaps because downvoting in disagreement is more frowned upon and varies depending on the subreddits where one resides). Nonetheless, both agreement and disagreement were common in the written vote explanations for opinionated comments, as seen in the example below.

In my thesis I suggested that the involvement of personal opinions in upvotes and downvotes may reflect the human tendency to use rewards and punishments to enforce our cultural norms. No matter how ignoble it may seem on reddit, encouraging each other to hold similar beliefs and to behave in accordance with shared standards probably helped our ancestors to efficiently maintain complex institutions in trade, education and marriages.

Social influence, or the existing number of votes on a piece of content, was reported as being mildly important in voting. Interestingly, the importance of this influence decreased slightly with age, and younger people reported taking more notice of content that is accompanied by high numbers of votes and Gold badges.

That's it for now. As usual with this kind of research, it's hard to know if the findings are truly representative of the general voting behaviour of redditors, but I hope that these results can be useful in some way.