Emotion Granularity from Text: An Aggregate-Level Indicator of Mental Health (2024)

Abstract: We are united in how emotions are central to shaping our experiences; 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.

## 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" @xcite . 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 (eg), aka emotion differentiation, is defined by psychologists as the ability of an individual to experience and categorize emotions in very specific terms @xcite . 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. evidence collected in the last two decades provides consistent support for a link between emotional granularity and mental health @xcite @xcite , physical health @xcite , and adaptive health behavior @xcite . note that this is different from other findings that study how the prevalence of specific emotions varies with mental health, (for example, people with depression tend to use more sadness-associated words). the link between eg and mental health suggests that there is a fundamental difference in how one perceives an emotion (broadly or specifically), and that in turn can impact their mental health. 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 @xcite . 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), i.e, with self-reports of emotions felt. an individual's emotional granularity is then operationalized as the extent to which multi-ple emotions are co-endorsed over time, i.e, how similarly the emotions are rated across all measurements, using the intraclass correlation coefficient (icc) @xcite , which measures the extent to which the emotions co-vary in reports at the aggregate level. 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 @xcite @xcite . while prior work in nlp has studied the link between emotions and mental health, these have largely been limited to measuring the prevalence or intensity of positive and negative emotions. 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 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 where users have chosen to self-disclose their mental health diagnosis @xcite @xcite . we compute emotion granularity metrics for each of these groups to answer the following questions: our results 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. 1 all our code will be made available through the project webpage. 2 2 related work ## datasets we use the twitter-stmhd dataset @xcite for our experiments and also verify our results with a smaller reddit erisk @xcite dataset. we describe both of them below. twitter-stmhd dataset: @xcite 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 tweeters from these mhc groups. 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 selfreporting 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). reddit erisk dataset: to further add to our findings, we also included the erisk 2018 dataset @xcite 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 500day period. we combined users and their instances from both the training set (which is from the erisk 2017 task @xcite ) and the test set (from the erisk 2018 task @xcite ). ## 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 "coendorsement" 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 granularity and mental health we now test if there are significant differences between the emotion granularities of each of the mhc groups and the control group, using t-tests. we first limit the users in each group by placing thresholds on (a) the number of user tweets with a valid emotion score (set to a minimum of 50), and (b) the number of unique lexicon terms used in their tweets (set to a minimum of 25). these thresholds ensure that we are drawing inferences based on users with valid emotion arcs, with sufficient lexicon coverage and temporal information. 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 @xcite . to correct for multiple comparisons (eight tests performed for each mhc per emotion granularity group), we used the benjamini-hochberg proce- 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 lower anxiety-control --lower lower lower lower lower bipolar-control --lower lower lower lower lower mdd-control --lower --lower lower ocd-control --lower lower lower lower lower ppd-control ---lower ---ptsd-control --lower lower lower lower lower depression-control --lower lower lower lower lower reddit erisk depression-control --lower lower -lower lower ## results in 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). in 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. variation with hyperparameter choices: we observe only minor variations from the results reported in ## 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 negative emotion granularity is significantly lower for 7 out of the 8 mhc groups under consideration when compared to a control group, at an aggregate-level. also, all mhcs except for ppd had lower overall emotion granularity (and lower positive emotion granularity) 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. we hope this opens up an avenue of future work to explore emotional expression in text and mental health.