#  Emotion Granularity from Text:
An Aggregate-Level Indicator of Mental Health

Krishnapriya Vishnubhotla1,2 Daniela Teodorescu3 Mallory J. Feldman4
Kristen A. Lindquist4 Saif M. Mohammad5
1Department of Computer Science, University of Toronto
2Vector Institute for Artificial Intelligence
3Department of Computing Science, University of Alberta
4Department of Psychology and Neuroscience, University of North Carolina at
Chapel Hill
5National Research Council Canada

###### Abstract

We are united in how emotions are central to shaping our experiences; and yet,
individuals differ greatly in how we each identify, categorize, and express
emotions. In psychology, variation in the ability of individuals to
differentiate between emotion concepts is called emotion granularity
(determined through self-reports of one’s emotions). High emotion granularity
has been linked with better mental and physical health; whereas low emotion
granularity has been linked with maladaptive emotion regulation strategies and
poor health outcomes. In this work, we propose computational measures of
emotion granularity derived from temporally-ordered speaker utterances in
social media (in lieu of self-reports that suffer from various biases). We
then investigate the effectiveness of such text-derived measures of emotion
granularity in functioning as markers of various mental health conditions
(MHCs). We establish baseline measures of emotion granularity derived from
textual utterances, and show that, at an aggregate level, emotion
granularities are significantly lower for people self-reporting as having an
MHC than for the control population. This paves the way towards a better
understanding of the MHCs, and specifically the role emotions play in our
well-being.

Emotion Granularity from Text:
An Aggregate-Level Indicator of Mental Health

Krishnapriya Vishnubhotla1,2 Daniela Teodorescu3 Mallory J. Feldman4 Kristen
A. Lindquist4 Saif M. Mohammad5 1Department of Computer Science, University of
Toronto 2Vector Institute for Artificial Intelligence 3Department of Computing
Science, University of Alberta 4Department of Psychology and Neuroscience,
University of North Carolina at Chapel Hill 5National Research Council Canada

##  1 Introduction

Emotions play a central role in how we construct meaning and communicate with
those around us. Yet, individuals vary in their understanding and experience
of emotions, or “emotional expertise” Hoemann et al. (2021b). Some people are
able to recognize, identify, and describe what they feel using precise,
context-specific terms like guilt, anger, frustration, or helplessness; others
tend to use more broad terms to convey a general sense of feeling bad or
feeling low.

Emotion granularity, aka emotion differentiation, is defined by psychologists
as the ability of an individual to experience and categorize emotions in very
specific terms Barrett et al. (2001). Highly granular individuals have a broad
range of highly situated and differentiated emotion concepts, and can reliably
describe these concepts using language — for example, distinguishing between
when they are feeling angry vs. when they are feeling sad, or when they are
feeling elated from when they are feeling content.

In psychology and the affective sciences, emotion granularity is often
measured using repeated measurements, where individuals are asked to rate the
intensity of experiencing certain emotions multiple times over a period of
days (e.g., 2–3 times each day for 5 days). An individual’s emotional
granularity is then operationalized as the extent to which multiple emotions
are co-endorsed over time, i.e, how similarly the emotions are rated across
all measurements. Individuals who tend to frequently rate multiple emotions at
the same intensity levels are defined as low in granularity — the frequent co-
endorsement across time indicates that they are failing to differentiate
between these emotions in their reports. In contrast, individuals high in
emotion granularity co-endorse multiple emotions less frequently over time.
The level of co-endorsement is operationalized in psychology using the
intraclass correlation coefficient (ICC) (Shrout and Fleiss, 1979), which
measures the extent to which the emotions co-vary in reports at the aggregate
level (Tugade et al., 2004; Hoemann et al., 2021a; Lee et al., 2017; Reitsema
et al., 2022). Typically, granularity is measured across emotions with the
same valence; one can therefore have a measure of negative emotion
granularity, measured as the granularity of negative emotions (such as anger,
sadness, and fear) and positive emotion granularity, measured as the
granularity of positive emotions (such as joy, excitement, and satisfaction).
Some works also look at the co-endorsement of emotions that express opposite
valence, such as joy and sadness (Lindquist and Barrett, 2008).

Evidence collected in the last two decades provides consistent support for a
link between emotional granularity and mental health Erbas et al. (2014,
2018); Starr et al. (2017); Seah et al. (2020), physical health Hoemann et al.
(2021b); Bonar et al. (2023), and adaptive health behavior Dixon-Gordon et al.
(2014); Kashdan et al. (2015).

Traditionally, past work in in psychology has measured emotion granularity
using self-reports of emotions felt; however, there are several limitations of
this approach. The number of participants and the duration over which the data
can be collected are limited. Further, several biases may be present in the
collected reports such as social desirability (e.g., tendency to report more
desirable traits and under-report less desirable traits) (Kragel et al., 2022;
Latkin et al., 2017a).

In this work, we, for the first time, propose a way to compute emotion
granularity from the textual utterances of an individual. Our method uses the
temporal sequence of the utterances to first construct emotion arcs along
multiple emotions, and computes granularity as the correlation of these
emotion arcs. We hypothesize that this measure is indicative of the individual
consistently expressing the same set of emotions together over a period of
time, and can therefore act as a proxy measure of emotional granularity. We
generate emotion arcs for eight emotions: anger, anticipation, disgust, fear,
joy, sadness, surprise, and trust using the NRC Emotion Intensity Lexicon
Mohammad (2018). Similar to work in psychology, we group anger, sadness, fear,
and disgust into one group (the negative valence group); joy and trust into a
second group (the positive valence group); and surprise and anticipation into
a third group (variable valence group).111Different instances of surprise can
correspond to very different amounts of valence—some positive and some
negative. Same for anticipation. We compute aggregate measures of negative,
positive, and variable emotional granularity; these are further aggregated to
obtain an overall measure of granularity between all within-group emotion
pairs.

We then study the relationship between aggregate, population-level measures of
emotion granularity in text for eight Mental Health Conditions (MHCs), namely
attention-deficit hyperactivity disorder (ADHD), anxiety, bipolar disorder,
depression, major depressive disorder (MDD), obsessive-compulsive disorder
(OCD), postpartum depression (PPD), and post-traumatic stress disorder (PTSD),
and compare them to a control group. We use two social media datasets (Suhavi
et al., 2022; Losada et al., 2017, 2018) where users have self-disclosed their
mental health diagnosis. We compute emotion granularity metrics for each of
these groups to answer the following questions:

  1. 1.

Do measures of emotional granularity differ between the MHCs and the control
group?

  2. 2.

Which measures of emotion granularity are the most effective at
differentiating between the MHCs and the control group?

  3. 3.

Which emotion pairs lead to the greatest difference in granularity between an
MHC and the control group?

Exploring this line of questions helps us better understand how emotion
granularity presents itself in text, whether emotion granularity from text can
be a useful tool to study MHCs, and how an MHC impacts our perception of
emotions (and perhaps even, how the perception of emotions impacts our mental
health). While this work is the first in exploring emotion granularity from
text, and presents early results in how they correlate with MHCs, we hope it
opens up an avenue of future work to substantively explore these and other
questions.

We establish baseline measures of emotion granularity from text, and show that
these measures function as reliable indicators, at the aggregate-level, for
the presence of many of the mental health conditions we study. Our work makes
an important contribution to the growing wealth of research on textual
measures of emotional expression as biosocial markers of MHCs, and has a
broader utility in functioning as an additional indicator of the mental well-
being of populations.222The term biosocial marker (Lena, 2021) was coined to
indicate the crucial role social factors (e.g., socioeconomic status, years of
education, bilingualism, etc.) have on quantitative features associated with
medical conditions (biomarkers). We make use of publicly available datasets
and all our code will be made available through the project
webpage.333https://github.com/Priya22/emotion-granularity

##  2 Related Work

###  2.1 Emotions and Mental Health

Measures of emotional experience and their patterns of change over time have
been extensively studied as markers of mental and physical well-being Lewis et
al. (2010). The Emotion Dynamics framework in psychology quantifies the
patterns with which emotions change over time, allowing researchers to better
understand emotional experiences and individual variation Kuppens and Verduyn
(2017). The framework includes several measures such as the duration,
intensity, variability, and granularity of one’s emotional experiences.
Numerous studies in psychology have shown emotion dynamics correlate with
overall well-being, mental health, and psychopathology (the scientific study
of mental illness or disorders) (Kuppens and Verduyn, 2017; Houben et al.,
2015; Silk et al., 2011; Sperry et al., 2020).

Emotion granularity in particular is positively associated with better
adaptive behaviour in adverse conditions — accurately labeling our emotions
can inform us of the right coping strategies to use in different contexts.
Individuals with higher emotion granularity tend to use a broader range of
strategies to deal with negative emotions, and are more successful at doing so
Barrett et al. (2001). Several studies have shown that emotion granularity is
lower in individuals with mental health conditions like bipolar disorder Suvak
et al. (2011); Dixon-Gordon et al. (2014), manic depressive disorder Demiralp
et al. (2012), schizophrenia Kring et al. (2003), autism spectrum disorder
Erbas et al. (2013), and affective disorders like anxiety Seah et al. (2020)
and depression Starr et al. (2017); Willroth et al. (2020). Lower granularity
is also associated with increased tendencies to engage in maladaptive
behaviour, such as alcohol consumption Kashdan et al. (2015); Emery et al.
(2014) and aggression Pond Jr et al. (2012).

Researchers in affective science typically measure emotional granularity
through experience sampling methodologies (ESMs), or ecological momentary
assessments (EMAs), where individuals (participants) are repeatedly asked to
report on their emotional states on several occasions throughout the day, for
several days. Granularity is then quantified by tracking the endorsement of
emotions over time. For example, participants may be asked to endorse a series
of ten emotion words (e.g., "Anger," "Fear," "Happy", etc.) on a Likert scale
many times across several sampling instances. Emotional granularity would then
be computed as the intraclass correlation (ICC) of ratings across sampling
instances. A high ICC would suggest that a participant experiences all of the
emotions in a similar way across trials (treating them as synonyms for more
general states like "unpleasantness" or "pleasantness"), whereas a low ICC
would suggest that a participant experienced emotions in a granular and
context-specific way.

While emotion granularity is generally measured between emotion categories
that are close to each other in the affective space (i.e, express similar
valence), the concept of dialecticism refers to the co-incidental experience
of both negative and positive emotions Lindquist and Barrett (2008).
Dialecticism can therefore be operationalized as the co-endorsement of emotion
pairs that express positive and negative valence.

###  2.2 Language and Mental Health

Given the limitations of self-report surveys (e.g., limited data coverage and
time spans, biases, etc. (Kragel et al., 2022)), another approach to measure
well-being indicators is through one’s language usage. Some well-known
linguistic indicators of mental health include the proportion of pronouns used
for those with depression (Koops et al., 2023), syntax reduction for anorexia
nervosa (Cuteri et al., 2022), certain lexical and syntactic features for mild
cognitive impairment and dementia (Calzà et al., 2021; Gagliardi and
Tamburini, 2021), and semantic connectedness for schizophrenia (Corcoran et
al., 2020).

Recently, another linguistic feature that researchers leveraged for insights
into overall well-being, are the emotions expressed in language. Largely, only
sentiment has been explored and mainly from social media data (a rich source
of language data). For example, more negative sentiment was expressed in text
by individuals with depression (De Choudhury et al., 2013; Seabrook et al.,
2018; De Choudhury et al., 2021). Other work has found that suicide watch,
anxiety, and self-harm subreddits had markedly lower negative sentiment
compared to other mental health subreddits such as Autism and Asperger’s
(Gkotsis et al., 2016). Teodorescu et al. (2023) found that measures of
emotion dynamics from text correlate with various mental health diagnoses.
These works overall show that the average emotion expressed in text and also
the characteristics of emotion change over time (e.g., variability) are
meaningful indicators of well-being. In this work, we explore if measures of
emotion granularity from text are likewise meaningful indicators of mental
health.

##  3 Datasets

We use the Twitter-STMHD dataset Suhavi et al. (2022) for our experiments and
also verify our results with a smaller Reddit eRisk Losada et al. (2017, 2018)
dataset. We describe both of them below.

Twitter-STMHD dataset: Suhavi et al. (2022) identified tweeters who self-
disclosed as having an MHC diagnosis using carefully constructed regular
expression patterns and manual verification. We summarize key details on the
dataset creation process in Appendix A. The control group consists of users
identified from a random sample of tweets (who posted during approximately the
same time period as the MHC tweets). These tweeters did not post any tweets
meeting the MHC regex described above. Additionally, users who had any posts
about mental health discourse were removed from the control group. Note that
this process does not guarantee that users in the control group did not have
an MHC diagnosis, but rather the group as a whole may have very few MHC
tweeters. The number of users in the control group was selected to match the
size of the depression dataset, which had the largest number of users.

For the final set of users, four years of tweets were collected for each user:
two years before self-reporting a mental health diagnosis and two years after.
For the control group, tweets were randomly sampled from between January 2017
and May 2021 (same date range as the other MHC classes).

Users with less than 50 tweets collected were removed so as to allow for more
generalizable conclusions to be drawn. Similarly, users with more than 5000
followers were removed to not include celebrities or other organizations that
use Twitter to discuss well-being.
Reddit eRisk dataset: To further add to our findings, we also included the
eRisk 2018 dataset Losada et al. (2017, 2018) in our experiments. It consists
of users who self-disclosed as having depression on Reddit (expressions were
manually checked), and a control group (individuals were randomly sampled).
The dataset includes several hundred posts per user, over approximately a
500-day period. We combined users and their instances from both the training
set (which is from the eRisk 2017 task Losada et al. (2017)) and the test set
(from the eRisk 2018 task Losada et al. (2018)).

###  3.1 Preprocessing

We further preprocessed both the Twitter-STMHD dataset and the eRisk dataset
for our experiments (Section 4), as we are specifically interested in the
relationship between emotion granularity and each disorder. Several users
self-reported as being diagnosed with more than one disorder, referred to as
comorbidity. We found a high comorbidity rate between users who self-reported
as having anxiety and depression, as is also supported in the literature
(Pollack, 2005; Gorman, 1996; Hirschfeld, 2001; Cummings et al., 2014). Since
we wanted to focus on each MHC separately (and not on co-morbidity) we only
considered users who self-reported as having one MHC. We also performed the
following preprocessing steps:

  * •

We only considered posts in English, removing posts in other languages.

  * •

We filtered out posts that contained URLs (as the text in such posts is often
not self-contained; and one might need to view the page pointed to by the url
to get a better sense of the post text).

  * •

We removed retweets (identified through tweets containing ‘RT’, ‘rt’). This is
to focus exclusively on texts written by the user.

  * •

To ensure that we did not include users that post very infrequently or very
frequently we excluded those who were outliers based on the number of posts
per individual. We determine outliers as those who either had less than 100
posts (as was similarly done in Vishnubhotla and Mohammad (2022)) or those who
had posted more than 1.5 times the interquartile range above quartile three
(75th percentile) of the control group.444The interquartile range is from the
25th to 75th percentile.

  * •

We removed punctuation and stop words.

Table 3.1 shows key details of the filtered Twitter-STMHD and Reddit eRisk
datasets.

Dataset, Group | #people | Av. #posts |  Av. #tokens
---|---|---|---
|  |  | per post
Twitter |  |  |
MHC | 19,324 | 2,590.48 | 17.59
ADHD | 6,356 | 2,497.43 | 17.46
Anxiety | 3,036 | 2,921.05 | 17.46
Bipolar | 1,061 | 2,820.17 | 17.32
Depression | 4,855 | 2,526.62 | 16.75
MDD | 219 | 2,640.69 | 16.40
OCD | 1,009 | 2,388.73 | 18.38
PPD | 179 | 2,581.19 | 19.18
PTSD | 2,609 | 2,533.85 | 19.41
Control | 6,001 | 2,420.50 | 16.16
Reddit |  |  |
Depression | 112 | 556.57 | 47.22
Control | 907 | 665.00 | 41.09

Table 1:  The number of users in each mental health condition, the average
number of posts per user, and the average number of tokens per post in the
preprocessed version of the Twitter-STMHD and Reddit eRisk datasets. We use
this data for our experiments.

##  4 Emotion Granularity from Text

The core metric that we want to capture from the text utterances of an
individual is emotion granularity—what psychologists term the “co-endorsement"
of pairs of emotions. Analogous to their operationalization of granularity in
terms of the Intra-Class Correlation (ICC) of repeated emotion intensity
measurements along emotion adjectives, we use textual utterances to derive a
temporal sequence of emotion states, referred to as an emotion arc for the
speaker (section 4.1), and operationalize granularity as the correlations of
these arcs. We construct emotion arcs for multiple emotions, for each user in
the MHC groups and the control group.

##### Emotion Dimensions:

A key requirement of our computational method is that we must be able to
quantify the emotional score of a text along a selected emotion dimension. We
are therefore limited by the resources and models available to compute such a
score for an emotion dimension.

Here, keeping in mind the necessity of including multiple emotion dimensions
that are similarly-valenced, we work with the eight emotions represented in
the NRC Emotion Intensity Lexicon: anger, anticipation, disgust, fear, joy,
sadness, surprise, and trust Mohammad (2018).

We partition these emotions into three groups based on the valence
association: joy and trust are in the positive valence group; anger, sadness,
fear, and disgust are in the negative valence group; and anticipation and
surprise are in the variable valence group. The distinction for surprise and
anticipation is necessary because specific instances of these emotions can
have a positive or a negative connotation (e.g., a good or a bad surprise).

###  4.1 Constructing Emotion Arcs

We order the utterances for each user based on timestamp information in the
metadata. We construct emotion arcs for the temporal sequence of utterances of
each user, along each of the eight emotions, in two ways pertaining to
different window sizes. This is to make sure that the results are largely
robust even when varying the window size to some extent.

##### Utterance-level Window:

Emotion scores (for each emotion category) are computed for each utterance
(i.e, tweet or Reddit post). Here, an utterance is assumed to represent the
speaker’s emotion state at a particular point in time (analogous to sampling
instances). The sequence of utterance emotion scores for a user forms their
temporal emotion arc.555The frequency and time of posting often differs
between users, but we ignore that for now.

##### Word-Count based Window:

Here, the emotion score at a point in time is computed for a window of words
(say, 100 words) that are uttered around that point, and the window is moved
forward by a fixed step size (say, 1 word at a time) to obtain the emotion
score for the next time step. In prior work on constructing emotion arcs from
temporally-ordered text, such sliding windows are usually employed to ensure
smoother arcs that more accurately capture the flow of emotions over time.

Teodorescu and Mohammad (2023) conducted an extensive quantitative evaluation
of several hyperparameters involved in emotion arc construction on datasets
from diverse domains (tweets, movie reviews, diary posts) annotated with
emotion scores. We follow many of their recommendation to construct emotion
arcs for the utterances of each of our users.

The texts are tokenized using the twokenizer666https://github.com/myleott/ark-
twokenize-py package to obtain a similarly-ordered sequence of words. Emotion
scores are computed with window sizes of 100 words and 500 words each, and the
window is moved forward by one word at each timepoint to obtain a series of
overlapping emotion scores. The final emotion score is for the window that
subsumes the final word of the individual’s utterance sequence.

#### Emotion scoring method

Keeping in mind the necessity of an interpretable method of emotion scoring,
we use word–emotion lexicons to compute the emotion scores of longer text
spans. For each window, the emotion scores of its constituent words are
averaged to obtain the window-level score for that emotion. Teodorescu and
Mohammad (2023) showed that emotion arcs constructed with lexicon-based
scoring methods, when used with sliding window sizes of 100 instances or more,
can mimic the ground-truth emotion arcs with an accuracy of 0.9 or more
(higher window sizes also lead to greater accuracies).

Word–emotion scores are obtained from the NRC Emotion Intensity Lexicon, which
associates close to 10,000 English words with a real-valued score between 0
and 1 for each dimension. A score of 0 indicates that the word has little to
no association with that particular emotion, and a score of 1 indicates a high
association.

###  4.2 Quantifying Emotion Granularity

We compute the emotion granularity metric as the negative of the Spearman
correlation between each pair of emotions arcs, for each user.777We choose
Spearman correlation as it is rank-based as compared to Pearson correlation
which utilizes the raw-values. A high correlation between two arcs indicates
that the speaker is consistently and repeatedly expressing the two emotions
concurrently; we hypothesize that this is an indicator of a lower ability to
differentiate between the two emotions, and therefore a lower emotion
granularity.888We choose to use Spearman correlation over ICC-based metrics
because the emotion scores that we extract from textual utterances are a
relative indicator of the intensity of the emotion, and not an absolute
measure. Further, these scores cannot be directly compared across different
emotions in terms of absolute intensity (a score of 0.9 for anger may not
equate to the same level of anger as a score of 0.9 would for joy) due to
differences in how overtly different emotions are expressed via language.

For each person, we average the correlation scores between emotion pairs in
the following valence groups to obtain the following measures of emotion
granularity (EG):

  * •

$EG(pos)$: The negative of (i.e., $-1$ times) the average of the correlation
scores between each of the pairs of emotions in the positive valence group
(joy–trust).

  * •

$EG(neg)$: The negative of the average of the correlation scores between each
of the pairs of emotions in the negative valence group (anger–fear,
fear–disgust, etc.).

  * •

$EG(var)$: The negative of the average of the correlation scores between each
of the pairs of emotions in the variable valence group
(surprise–anticipation).

  * •

$EG(overall)$: Overall emotion granularity, measured as the negative of the
average of the correlation scores between emotion pairs whose constituents are
in the same group. Here, the average is taken across all of the emotion pairs
drawn from the positive valence group, the negative valence group, and the
variable valence group.

  * •

$EG(cross)$: Emotion granularity of cross-group emotion pairs. That is, the
negative of the average of the correlation scores between emotion pairs whose
constituents come from different groups. This measure to some extent
quantifies the amount of dialecticism (expressing both positive and negative
emotions in a narrow window of time); however, note that EG(cross) also
includes emotions that express variable valence (surprise and anticipation),
rather than only considering positive–negative valence emotion pairs.

We consider $EG(overall)$ to be the bottom line measure of emotion granularity
for a user (analogous to that used in psychology studies). Note that cross-
group pairs are not included in this measure.

Dataset, MHC–Control | $IC(n)$ | $IC(v)$ | $EG(pos)$ | $EG(neg)$ | $EG(var)$ | $EG(cross)$ | $EG(overall)$
---|---|---|---|---|---|---|---
Twitter-STMHD |  |  |  |  |  |  |
ADHD–control | – | – | lower | lower | higher | lower | lower
Anxiety–control | – | – | lower | lower | lower | lower | lower
Bipolar–control | – | – | lower | lower | lower | – | lower
MDD–control | – | – | lower | lower | lower | – | lower
OCD–control | – | – | lower | lower | lower | lower | lower
PPD–control | – | – | – | lower | lower | – | lower
PTSD–control | – | – | lower | lower | lower | lower | lower
Depression–control | – | – | lower | lower | lower | lower | lower
Reddit eRisk |  |  |  |  |  |  |
Depression–control | – | – | lower | lower | lower | lower | lower
Table 2:  Emotion Granularity - using Utterance-level Window: The difference
in emotion granularity between each MHC group and the control. A significant
difference is indicated by the word ‘lower’ or ‘higher’, indicating the
direction of the difference in granularity. E.g., ‘lower’ for ADHD–control
under $EG(pos)$ means that the ADHD group has significantly lower positive
emotion granularity than the control group.

##  5 Examining the Link between Emotion Granularity and Mental Health

After preprocessing the posts for each user and computing their emotion
granularity scores for the various emotions groups, we performed statistical
analyses to determine if there were significant differences between the
emotion granularities of each of the MHC groups and the control group.

To test this, we performed an independent t-test between each MHC and the
control. However, in order to perform this test the data must meet certain
assumptions. These assumptions are explained further in Appendix B. In
summary, the data is approximately normally distributed and the groups are
independent of each other; however the data does not meet the equal variance
(i.e., homoscedasticity) assumption (therefore we specify this when performing
the t-test).

We performed independent t-tests to compare emotion granularities between each
of the MHCs and the control group for each emotion group using the SciPy
library Virtanen et al. (2020). To correct for multiple comparisons (eight
tests performed for each MHC per emotion granularity group), we used the
Benjamini–Hochberg procedure in the statsmodels library Seabold and Perktold
(2010).

###  5.1 Term Specificity as a Control

Lower emotion granularity occurs when, for a person, the concepts of the
relevant emotions are so broad (and non-specific) that their meanings overlap
substantially. This work is testing the hypothesis of whether people who have
self-disclosed as having an MHC have lower emotion granularity than those that
do not. However, another plausible hypothesis is that people in a particular
group (e.g., MHC or the control) tend to use more specific words overall.
Doing so would imply a higher specificity (i.e, a higher granularity) in their
usage of all words, and that the high granularity of emotion words is simply a
by-product of their general style of speaking (posting online).

To ensure that the level of word specificity does not differ between MHCs and
the control group and act as a confounder for our measure of emotion
granularity, we performed a control experiment. We compute the average
information content of the noun and verb terms in the posts of users in each
group, and use this as a measure of the specificity of their language. The
information content of terms has been studied in NLP for applications
involving semantic similarity, particularly in information retrieval Resnik
(1995); Sánchez et al. (2011).

We use the metric proposed in Resnik (1995), and implemented in the NLTK
WordNet library,999https://github.com/nltk/wordnet which combines information
about the depth of the term in the WordNet tree hierarchy and its frequency of
occurrence in a large corpus (here, the Brown corpus) to compute an
information content score Miller (1995). We lemmatize each word using the
inbuilt WordNet Morphy library, and consider the most frequent synset to
obtain the information content score. We then compute the following measures
of term specificity:

  * •

$IC(n)$: The information content score for all nouns is averaged across all
posts of each individual in each group

* •

$IC(v)$: The information content score for all verbs is averaged across all
posts of each individual in each group. Statistical tests for significant
differences are similarly performed as described above (Section 5).

##  6 Results

In Table 2 we report the statistical results from the pairwise comparisons
between each MHC and the control group, for the control experiment on general
term specificity as well as emotion granularity, when scores are computed at
the utterance-level. All statistically significant differences between an MHC
and the control group are described as either ‘higher’ or ‘lower’, and a dash
(–) for no statistical difference. A ‘lower’ value in a cell indicates that
the MHC (rows) has lower emotion granularity (or lower term specificity) than
the control group, i.e., higher correlation between emotion pairs in that
group (columns); ‘higher’ indicates the MHC has higher emotion granularity (or
higher term specificity) than the control group (i.e., lower correlation
between emotion pairs in that group). Below we summarize the results for each
column.

IC(n) and IC(v): We do not see any significant differences in the information
content of noun and verb terms ($IC(n)$ and $IC(v)$) between MHCs and the
control group. This indicates that no group tends to use more specific or less
specific language in general when posting on these platforms. Details on the
statistical results are shown in Appendix E.

EG(pos): All MHCs except for PPD had significantly lower positive emotion
granularity than the control group (which had similar granularity compared to
the control group). That is, these MHCs (ADHD, Anxiety, etc.) consistently
expressed multiple positive emotions concurrently, more so than the control
group.

EG(neg): All MHCs had significantly lower negative emotion granularity than
the control group, in both datasets. Thus these MHCs were generally not
differentiating between the negative emotions of anger, disgust, fear, and
sadness, as well as compared to the control group.

EG(var): All MHCs except ADHD had significantly lower variable emotion
granularity than the control group (i.e., these MHCs generally differentiated
between surprise and anticipation less than the control group). On the other
hand, ADHD had significantly higher variable emotion granularity than the
control group (i.e., the ADHD group was better at differentiating between
surprise and anticipation).

EG(overall): All MHCs had significantly lower emotion granularity for emotion
categories that express the same valence (the mixed valence emotions of
surprise and anticipation are also included here). These MHCs are therefore
expressing multiple close emotions frequently with one another – more so than
the control group.

EG(cross): ADHD, Anxiety, OCD, PTSD, and Depression (in both datasets) have
significantly lower granularity between emotion pairs that come from different
valence groups. This indicates that positive and negative emotions are
expressed together more frequently in these MHCs compared to the control, as
well as emotions like joy (positive valence) and surprise (variable valence).

Discussion: These results demonstrate that our measures of emotion granularity
from text are consistently lower for users in the MHC groups compared to the
control. The term specificity results also tell us that it is the specificity
of emotion word usage in particular that is differentiating MHCs from the
control group.

Aligning with self-report studies in psychology, the emotion granularity
between negative-valence emotions is lower for all MHCs with utterance-level
operationalizations. Positive emotion granularity is also correlated with many
of the MHCs (except PPD). In general, the granularity of emotional expression
between within-group emotion pairs is lower for all MHC groups compared to the
control in both datasets. This is in line with both the theoretical and
conceptual links established in the psychology literature on emotion
granularity and mental health: the ability to better differentiate between
emotion concepts that are close to one another leads to more adaptive health
behaviour.

While emotion pairs from differently-valenced emotion groups are not usually
operationalized in affective science experiments, we find that this measure is
also significantly lower in many MHCs. Further investigations into what the
concurrent expression of positive and negative emotions means, for emotion
granularity and emotion dynamics in general, are interesting research
directions.

###  6.1 Additional Window Sizes

We examine how the measure of emotion granularity between MHCs and the control
changes when we compute emotion scores with larger window sizes (multiple
tweets/posts are included in computing the emotion score at a single time
point).

Many of the utterance-level outcomes are replicated for negative, positive,
and overall emotion granularity with window size 100. Some measures are no
longer significantly different between certain MHCs and the control (e.g.,
$EG(overall)$ for PPD). We also find that $EG(cross)$ is higher for certain
MHCs (Anxiety, PPD, PTSD, Depression in Twitter-STMHD) when compared to the
control, i.e, users in the control group are expressing negative and positive
emotions together more frequently than those in the MHCs. When the window size
is increased to 500 words, this reversal of trends for cross-valence emotions
persists.

With larger window sizes, we end up capturing emotions expressed by the
individual over longer time spans (tweets posted over the span of several
hours or days), rather than co-endorsement at the same time. We hypothesize
that these effects of dialecticism, where the Control group has a higher co-
occurrence of cross-valence group emotions, are capturing the extent to which
users balance negative emotions with positive emotions (and vice versa), and
how people bounce back from negative events, find the silver linings, etc. The
consistent effects with 100 and 500-word windows, and for several MHCs, makes
this a promising area for future work.

All emotion granularity measures with window sized 100 and 500 are reported in
Tables 3 and 4 in Appendix C.

###  6.2 Individual Emotion Pairs

In order to understand which emotion pairs are expressed together more
frequently (resulting in lower emotion granularity), we performed the same
significance tests as before between MHCs and the control for correlation
scores between all individual emotion pairs. We found that:

  * •

All eight MHCs in the Twitter-STMHD dataset had a lower granularity (a higher
correlation) for anger–disgust and anger–sadness in the negative valence
group.

  * •

Among cross-group emotion pairs, all eight MHCs had a lower emotion
granularity (higher correlation) between anticipation–fear and
anticipation–sadness.

  * •

Certain MHCs had a higher emotion granularity (lower correlation) between the
following cross-group emotions: anger–joy co-occurs more in the control than
in Bipolar, Depression, MDD, PPD, PTSD; however, it co-occurs less in control
compared to ADHD.

  * •

Similarly, sadness–surprise co-occur more in the control when compared to
Anxiety, Bipolar, Depression, and PPD; but less when compared to ADHD and
PTSD.

Detailed results for each of the emotion pairs and all MHCs are in Appendix D,
Table 5.

Discussion: While lower granularity among certain emotion pairs consistently
function as indicators of all MHCs, we also see many instances where MHCs have
a higher granularity between the emotions when compared to the control. These
findings are of interest to researchers studying the links between how
emotions are expressed in text, and how they are vary with with different
MHCs.

##  7 Conclusion

In this work, we operationalized for the first time a computational measure of
emotion granularity that can be derived from the textual utterances of
individuals. We applied this measure to two social media datasets of posts
from individuals who have self-disclosed as having an MHC. Our findings showed
that our measure of emotion granularity is significantly lower for all the MHC
groups when compared to a control group at an aggregate-level, for emotion
pairs that express similar valence. Specifically, we found significantly lower
values of negative and overall emotion granularity for all eight MHCs
represented in our dataset. Similarly, we found all MHCs except for PPD had
lower positive emotion granularity than compared to the control group. Our
work makes an important contribution towards deriving aggregate-level
indicators of emotional health from the large amounts of utterance data
available on social media platforms.

## Limitations

Our work uses the social media utterances of individuals to derive measures of
emotional expression that, at an aggregate level, are found to correlate with
multiple mental health conditions. While we use datasets that were compiled by
other researchers in the field, we stress that they may not be representative
of the general population. Our methods therefore cannot be directly applied to
make inferences on other datasets without a careful experimental validation
first. The datasets we study rely on self-disclosures made on social media
platforms; it is possible that users report only one such MHC but are
diagnosed with others, or that they misrepresent their diagnoses. Further, the
users in the control groups may include those who have chosen to simply not
self-disclose on these platforms. This can occur due to many reasons, like
social desirability Latkin et al. (2017b) or impression management Tedeschi
(2013)). Nonetheless, since we draw inferences at an aggregate level, the
methods used can overcome some amount of noisy data.

The set of emotions that we have considered in our measurement of emotion
granularity are also limited to those for which we can computationally obtain
text-derived emotion scores. These eight emotions do not represent the wide
range of emotion concepts that exist and are experienced and expressed by us
with language, and future research can attempt to expand our
operationalization to more emotion concepts. It should be noted though, that
past psychology studies on emotion granularity have also tended to explore
small sets of emotions, largely because it is cumbersome to ask users about
how they feel for a large set of emotions.

The emotion lexicons that we use are some of the largest that exist with wide
coverage and large number of annotators (thousands of people as opposed to
just a handful). However, no lexicon can cover the full range of linguistic
and cultural diversity in emotion expression. The lexicons are largely
restricted to words that are most commonly used in Standard American English
and the capture emotion associations as judged by American native speakers of
English. See Mohammad (2020) for a discussion of the limitations and best-
practises in the use of emotion lexicons.

## Ethics Statement

Our approach, as with all data-driven models of determining indicators of
mental health, should be considered as aggregate-level indicators, rather than
biomarkers for individuals Guntuku et al. (2017). We do not attempt to predict
the presence of MHCs for individual users at any stage of the process. These
measures should also not be taken as standalone indicators of mental health or
mental wellness, even at the population level, but rather as an additional
metric that can be used in conjunction with other population-level markers,
and with the expertise of clinicians, psychologists, and public health
experts.

Individuals vary considerably in how, and how well, they express their
internal emotional states using language. Our method of assessing the
emotional states of users based on their utterances may miss several
linguistic cues of emotion expression, and may not account for individual
variation or the extent to which these emotions are expressed on social media.
The emotionality of one’s language may also be conveying information about the
emotions of the speaker, the listener, or something or someone else mentioned
in the utterances. See further discussions of ethical considerations when
using computational methods for affective science in Mohammad (2020, 2022).

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##  Appendix A Twitter-STMHD Dataset

Suhavi et al. (2022) created a regular expression pattern to identify posts
which contained a self-disclosure of a diagnosis and the diagnosis name (using
a lexicon of common synonyms, abbreviations, etc.) such as ‘diagnosed with X’.
They collected a large set of tweets using the regex. This resulted in a
preliminary dataset of users with potential MHC diagnoses. To handle false
positives (e.g., ‘my family member has been diagnosed with X’, or ‘I was not
diagnosed with X’), the dataset was split into two non-overlapping parts, one
of which was manually annotated, and the other using an updated and high-
precision regex. In the part that was annotated by hand, each tweet was
annotated by two members of the team. A user was only included in the dataset
if both annotations were positive as self-disclosing for a particular class. A
licensed clinical psychologist found the 500-tweet sample to be 99.2%
accurate. The manual annotations were used to refine the regular expressions
and diagnosis name lexicon. This updated search pattern was applied to the
other dataset split. To verify the quality of the updated regex, the authors
applied it to the manually annotated dataset split. When considering the
manual annotations as correct, the regex was found to be 94% accurate.

##  Appendix B Statistical Assumptions

Below we describe in more depth the requirements for performing an independent
t-test, which was done in our analyses.

  * •

The dependent variable must be measured using a continuous scale: emotion
granularity is measured as the average of Spearman correlation between emotion
arcs in the group, resulting in continuous values.

  * •

The independent variable must have two categorical and independent groups: Our
independent variable is diagnosis, which is either an MHC or the control
group.

  * •

Independence of observations: Since the text stream of utterances come from
different people, we can assume these are independent observations.101010In
reality individuals are largely influenced by one another as we see interact
and engage with content from various communities, which can influence our
emotional state and therefore utterances. However, for the purposes of our
experiments since the utterances come from different people we can assume they
are independent.

  * •

Approximately normally distributed dependent variable for each group of
independent variable: Given the large number of people and number of
utterances per person in our dataset, we can assume that the means of the data
for each group is approximately normally distributed according to the law of
large numbers. Further, the t-test is robust to violations of normality.

  * •

Homogeneity of variance: We performed Levene’s test for homogeneity of
variance to verify whether this assumption is met. Our data did not meet this
assumption, therefore we performed t-tests with the unequal variance setting
as True in SciPy.

##  Appendix C Emotion Granularity: Various Window Sizes

We report the results of the statistical analyses of emotion granularity when
emotion arcs were generated using two other window sizes: 100 (Table 3) and
500 (Table 4). We find that largely the results do not change, however there
are some differences in the scenario when the dataset was smaller (e.g., eRisk
dataset or MHC such as MDD). In such cases, when the window size is increased,
it is possible that several emotional experiences occurred, resulting in a
weaker signal of emotion granularity.

Dataset, MHC–Control | $IC(n)$ | $IC(v)$ | $EG(pos)$ | $EG(neg)$ | $EG(var)$ | $EG(cross)$ | $EG(overall)$
---|---|---|---|---|---|---|---
Twitter-STMHD |  |  |  |  |  |  |
ADHD–control | – | – | lower | lower | higher | lower | lower
Anxiety–control | – | – | lower | lower | lower | higher | lower
Bipolar–control | – | – | lower | lower | lower | – | lower
MDD–control | – | – | lower | lower | – | – | lower
OCD–control | – | – | lower | lower | – | – | lower
PPD–control | – | – | – | – | lower | higher | –
PTSD–control | – | – | – | lower | – | higher | lower
Depression–control | – | – | lower | lower | lower | higher | lower
Reddit eRisk |  |  |  |  |  |  |
Depression–control | – | – | lower | – | lower | – | lower
Table 3:  Emotion Granularity - using Window 100: The difference in emotion granularity between each MHC group and the control. A significant difference is indicated by the word ‘lower’ or ‘higher’, indicating the direction of the difference in granularity.  Dataset, MHC–Control | $IC(n)$ | $IC(v)$ | $EG(pos)$ | $EG(neg)$ | $EG(var)$ | $EG(cross)$ | $EG(overall)$
---|---|---|---|---|---|---|---
Twitter-STMHD |  |  |  |  |  |  |
ADHD–control | – | – | lower | lower | – | lower | lower
Anxiety–control | – | – | lower | lower | lower | higher | lower
Bipolar–control | – | – | lower | lower | lower | – | lower
MDD–control | – | – | – | – | – | – | lower
OCD–control | – | – | lower | – | – | – | –
PPD–control | – | – | – | – | – | higher | –
PTSD–control | – | – | – | lower | – | higher | lower
Depression–control | – | – | lower | lower | lower | higher | lower
Reddit eRisk |  |  |  |  |  |  |
Depression–control | – | – | lower | – | – | – | –
Table 4:  Emotion Granularity - using Window 500: The difference in emotion
granularity between each MHC group and the control. A significant difference
is indicated by the word ‘lower’ or ‘higher’, indicating the direction of the
difference in granularity.

##  Appendix D Emotion Granularity: Emotion Pairs

In Table 5 we report the pairwise emotion granularity results when testing for
significant differences between MHCs and the control group.

Emotion Pair, MHC-Control | ADHD | Anxiety | Bipolar | Depression | MDD | OCD | PPD | PTSD
---|---|---|---|---|---|---|---|---
anger–anticipation | lower | lower | lower | lower | – | lower | – | lower
anger–disgust | lower | lower | lower | lower | lower | lower | lower | lower
anger–fear | lower | lower | – | lower | – | lower | – | lower
anger–joy | lower | – | higher | higher | higher | – | higher | higher
anger–sadness | lower | lower | lower | lower | lower | lower | lower | lower
anger–surprise | lower | higher | – | – | – | lower | higher | lower
anger–trust | lower | lower | – | lower | – | lower | – | lower
anticipation–disgust | lower | lower | lower | lower | – | lower | – | lower
anticipation–fear | lower | lower | lower | lower | lower | lower | lower | lower
anticipation–joy | lower | – | – | lower | – | – | higher | –
anticipation–sadness | lower | lower | lower | lower | lower | lower | lower | lower
anticipation–surprise | higher | lower | lower | lower | lower | lower | lower | lower
anticipation–trust | lower | lower | – | lower | lower | – | – | lower
disgust–fear | lower | lower | lower | lower | lower | lower | – | lower
disgust–joy | lower | – | higher | – | higher | – | – | higher
disgust–sadness | lower | lower | lower | lower | – | lower | – | lower
disgust–surprise | lower | lower | – | lower | – | lower | higher | lower
disgust–trust | lower | lower | – | lower | – | lower | – | lower
fear–joy | lower | – | – | – | – | – | – | –
fear–sadness | lower | lower | lower | lower | lower | lower | lower | lower
fear–surprise | lower | higher | – | higher | – | – | higher | –
fear–trust | lower | lower | – | lower | – | lower | lower | lower
joy–sadness | lower | – | – | – | – | – | higher | –
joy–surprise | higher | lower | – | lower | – | – | – | –
joy–trust | lower | lower | lower | lower | lower | lower | – | lower
sadness–surprise | lower | higher | higher | higher | – | – | higher | lower
sadness–trust | lower | lower | – | lower | – | lower | – | lower
surprise–trust | – | lower | lower | lower | lower | lower | lower | lower
Table 5:  Emotion Granularity - Utterance-Level: The difference in emotion
granularity between each emotion pair, for each MHC group and the control in
the Twitter-STMHD dataset. A significant difference is indicated by the word
‘lower’ or ‘higher’, indicating the direction of the difference.

##  Appendix E Term Specificity Results

Table 6 shows the results of the term specificity experiments described in
Section 5.1 measuring information content. For both nouns and verbs, none of
the diagnoses had significantly different term specificity levels compared to
the control group in both the Twitter-STMHD and eRisk datasets. This verifies
that the significant differences between the MHCs and the control group for
emotion granularity is not due to varying word specificity levels in these
groups.

|  |  | df | T-Statistic | P-value
---|---|---|---|---|---
POS | Dataset | MHC–Control |  |  |
Noun | Twitter-STMHD | ADHD–control | 2368.64 | -1.94 | 0.144
|  | Anxiety–control | 2233.36 | -0.58 | 0.718
|  | Bipolar–control | 1726.26 | -2.40 | 0.131
|  | MDD–control | 226.83 | -0.52 | 0.718
|  | OCD–control | 1817.93 | 1.93 | 0.144
|  | PPD–control | 178.33 | -0.49 | 0.718
|  | PTSD–control | 2237.82 | -0.54 | 0.718
|  | Depression–control | 2245.64 | -0.27 | 0.787
| Reddit eRisk | Depression–control | 128.95 | 0.98 | 0.330
Verb | Twitter-STMHD | ADHD–control | 2248.45 | -0.73 | 0.530
|  | Anxiety–control | 2235.85 | 1.12 | 0.420
|  | Bipolar–control | 1852.44 | -2.10 | 0.096
|  | MDD–control | 213.0 | 1.36 | 0.354
|  | OCD–control | 1645.17 | 2.28 | 0.091
|  | PPD–control | 169.49 | 0.98 | 0.438
|  | PTSD–control | 2351.12 | 2.53 | 0.091
|  | Depression–control | 2274.59 | 0.54 | 0.589
| Reddit eRisk | Depression–control | 110.40 | 1.59 | 0.116
Table 6:  Information Content: The degrees of freedom, t-statistic and p-value
for the word specificity experiments described in Section 5.1.

##  Appendix F Emotion Correlations

Table 7 shows the Spearman correlation between emotion arcs for all pairs of
emotions for the control group. These results indicate that as baselines
largely emotions in the same group (e.g., positive, negative, mixed, overall)
co-occur more often than emotions across groups.

Table 8 shows the group-averaged Spearman correlations for emotion pairs in
the positive, negative, mixed valence groups, and the within-group and cross-
group averages, for the MHCs and Control groups in both datasets.

emo2 | anticipation | disgust | fear | joy | sadness | surprise | trust
---|---|---|---|---|---|---|---
emo1 |  |  |  |  |  |  |
anger | 0.020 | 0.593 | 0.536 | -0.015 | 0.447 | 0.113 | 0.003
anticipation | – | 0.009 | 0.040 | 0.432 | 0.022 | 0.251 | 0.404
disgust | – | – | 0.429 | -0.014 | 0.434 | 0.061 | 0.001
fear | – | – | – | -0.010 | 0.567 | 0.138 | 0.004
joy | – | – | – | – | -0.009 | 0.221 | 0.467
sadness | – | – | – | – | – | 0.100 | -0.001
surprise | – | – | – | – | – | – | 0.145
Table 7:  Emotion–Emotion Spearman Correlation: Spearman correlation values between pairs of utterance-level emotion arcs for the control users in the Twitter-STMHD dataset.  Dataset, MHC | $EG(pos)$ | $EG(neg)$ | $EG(var)$ | $EG(cross)$ | $EG(overall)$
---|---|---|---|---|---
Twitter-STMHD |  |  |  |  |
Control | 0.467 | 0.501 | 0.251 | 0.083 | 0.465
ADHD | 0.495 | 0.524 | 0.243 | 0.096 | 0.486
Anxiety | 0.505 | 0.522 | 0.274 | 0.089 | 0.489
Bipolar | 0.491 | 0.524 | 0.273 | 0.084 | 0.488
Depression | 0.505 | 0.520 | 0.272 | 0.087 | 0.487
MDD | 0.508 | 0.518 | 0.267 | 0.085 | 0.486
OCD | 0.497 | 0.523 | 0.260 | 0.090 | 0.487
PPD | 0.473 | 0.511 | 0.269 | 0.081 | 0.476
PTSD | 0.485 | 0.521 | 0.259 | 0.090 | 0.484
Reddit eRisk |  |  |  |  |
Control | 0.475 | 0.537 | 0.271 | 0.168 | 0.496
Depression | 0.521 | 0.568 | 0.319 | 0.184 | 0.531
Table 8:  Emotion Groups Spearman Correlation: Spearman correlation values
between utterance-level emotion arcs of emotion pairs in the positive,
negative, and variable valence groups, and within-group (overall), and cross-
group averages, for the Twitter-STMHD and Reddit eRisk datasets. Emotion
granularity is defined as the negative of these correlations (i.e, higher
correlations imply a lower granularity).