Condolence and Empathy in Online Communities (2020)

Abstract: Offering condolence is a natural reaction to hearing someone’s distress. Individuals frequently express distress in social media, where some communities can provide support. However, not all condolence is equal—trite responses offer little actual support despite their good intentions. Here, we develop computational tools to create a massive dataset of 11.4M expressions of distress and 2.8M corresponding offerings of condolence in order to examine the dynamics of condolence online. Our study reveals widespread disparity in what types of distress receive supportive condolence rather than just engagement. Building on studies from social psychology, we analyze the language of condolence and develop a new dataset for quantifying the empathy in a condolence using appraisal theory. Finally, we demonstrate that the features of condolence individuals find most helpful online differ substantially in their features from those seen in interpersonal settings.

## introduction millions of individuals experience emotional distress each year from diverse circumstances such as personal loss or abuse. after such experiences, people often turn to their social circle in social media to convey their experiences and seek out emotional support @xcite @xcite . often, support comes in the form of condolence where individuals connect with the distressed person, and express forms of sympathy, empathy, advice, and social connection, among others @xcite . however, not all expressions of distress receive emotional support, nor do all condolence messages offer equal levels of support @xcite . given the wide-spread use of social media for seeking social support, what makes for an effective supportive message? here, we perform the first major study of condolence in social media, examining what type of distress individuals seek support for, what linguistics factors are more likely to elicit condolence, and what types of condolence viewed as more helpful. distress and emotional support have long been explored in work in social psychology and counseling @xcite , frequently around bereavement and helping victims of abuse. nlp works have only recently examined emotional support in online spaces for mental and physical health @xcite @xcite and in communities oriented around goals like weight-loss @xcite ; however, these focus on the general concept of supportiveness. in this work, we examine distress as a universal phenomenon-not just related to health and death-and examine the strategies and helpfulness of responses to this distress. this study aims to computationally identify mechanisms and strategies for delivering effective and impactful condolence on social media. conveying condolence is often difficult for many people @xcite , who fall back to common responses to distress such as "thoughts and prayers" or "i'm so sorry for your loss" due to the emotional and mental effort required to relate to the distressed person. to identify effective strategies of condolence, we construct a dataset of 14.1m expressions of distress from reddit by developing computational models for recognizing distress and condolence. we then use this dataset to analyze how the community embraces the individual and which condolence responses were found helpful. this work offers the following three contributions. first, we introduce a new massive dataset of 11.4m public expressions of distress and 2.8m of condolence labeled using two deep learning models for identifying each, showing that our data mirrors known trends in seasonality and theme. sec-ond, using an analysis of 11.4m expressions of distress, we demonstrate that the community selectively engages in condolence; not all distress messages which attract attention actually receive support. third, we introduce a new dataset and model for identifying empathy in condolences and, using the empathy estimates, find that distressed individuals less frequently offer gratitude for deeply empathetic condolences and instead prefer compassionate, positive messages, which runs counter to observations from in-person settings. recognizing distress a set of stereotypical condolence expressions, e.g., "sorry for your loss" or "my heart goes out to you" is first manually identified. due to their ubiquitous use in the face of distress, such expressions act as heuristics to identify posts containing a variety of circumstances and topics. all reddit comments receiving at least one of these stereotypical-condolence replies are treated as positive examples of distress, identified from all reddit comments in the year 2017. 1 an equivalent number of randomly-selected comments that do not receive any of these stereotyped-condolence responses are sampled from the same communities in the same month, which ensures the corpus is topically and temporally balanced. in total, 229,204 comments are collected as training data from reddit during the year 2017. two classifiers are trained from this balanced dataset. the first is a svm classifier using unigrams and bigrams, which is known to be a robust baseline @xcite . the second is a bert-based classifier @xcite trained using a linear layer on top of the pooled [cls] token for classification over 2 epochs. the base pretrained model was recognizing condolence condolence-giving comments are heuristically identified in a similar manner to those for distress. when a comment receives a reply containing one of the stereotypedcondolence expressions, a single different reply to that same comment is selected as another expression of condolence. the assumption is that distress attracts multiple condolences, allowing us to learn a variety of condolence expressions. to minimize potential confounds, condolence comments were collected from all reddit comments from a different year than distress comments @xcite . negative examples of condolence are randomly sampled from replies to different non-distress comments under the same post, which ensures a balance in time and subreddit between positive and negative examples. svm and bert-based classifiers were tested to recognize condolence in comments, using the same setup as those for recognizing distress. performance at recognizing condolence, shown in tuning for precision decision thresholds were set at 0.9 for both classifiers to focus on precision after a manual review of a subset of classifications found this to produce sufficiently correct results. dataset description our final condolence and distress datasets were collected by running the respective classifiers on a random sample of 2018 reddit comments made in the top ten thousand most popular safe-for-work subreddits. condolence comments have a length centered around a median of 21 words, with a long right tail (mean of 47.7 words, standard deviation of 79.8 words). distress comments have a similarly shaped distribution, with a median of 25 words, mean of 41.3 words, and standard deviation of 57.8 words. ## condolence behavior in social media as an initial demonstration of the model, we label a random sample of reddit comments from 2018 made in the top ten thousand most popular safefor-work subreddits and examine where and when distress and condolence are exhibited. distress and condolence communities figure 1 (left) shows that while health topics are prominent, individuals frequently seek out communities based around bereavement (e.g., r/miscarriage) and abuse (e.g., r/domesticviolence). this result confirms that our model is able to identify a diverse set of circumstances in which individuals experience distress, mirroring some of those highlighted in prior work for online support of distress @xcite @xcite . surprisingly, the location of condolence behavior (figure 1 , right) does not mirror that of distress. instead, condolence is frequently offered to those suffering from the loss of a pet and, less frequently, those experiencing the death of a loved one. many people find the death of a pet more relatable compared with other circumstances like domestic violence, lessening the effort required to relate to the person experiencing the loss and offer condolence @xcite . indeed, to express effective condolence, an empathetic response requires effort to relate on a personal level to the feelings of the affected person @xcite , which many may find more challenging emotionally in circumstances like abuse. seasonal effects in distress changes in seasons and holidays are both known to increase distress and anxiety levels @xcite @xcite . as figure 2 figure 2 : relative rates for distress (right axis) and condolence (left axis) show that while distress mirrors expected seasonal trends, condolence does not; instead, condolence trends are partially driven by response to events, e.g., mass shootings. throughout the paper, error bars show 95% confidence intervals. as individuals turn to social platforms for emotional support for a variety of reasons, which types of distress messages receive condolence? we contrast whether an expression of distress receives condolence with receiving any reply. ## the structure of condolence individuals regularly employ a common set of strategies in condolence (e.g., @xcite @xcite , from trope-like expressions ("sorry for your loss") to thoughtful and empathetic statements that validate the other's experience. these statements often fall along a spectrum of person-centeredness (high and dillard, 2012) with respect to their acknowledgment, understanding, and legitimization of the distressed person's state. here, we analyze the structure of reddit condolences to examine reg-ularities in strategies individuals employ in crafting their responses. we use a data-driven approach to identify themes by fitting a 20-topic lda model to identify broad themes; to test for structure, we measure the probability of each topic in the sequence of sentences for condolences of different lengths. results condolences follow regular patterns in their strategies for support. figure 3 shows the presence of different topics by position in the sentence across condolences of different lengths; the most probable words for each topic are listed in second, as condolences become longer, individuals begin adding their own experience within the response (personal experience). this behavior features prominently in middle-length condolences that still begin with sympathy and then try to relate their own personal experience to that of the suffering. at a high-level, these experiences aim to help the person experiencing distress reframe their own mindset and correspond to a higher-level of person-centeredness @xcite . finally, the longest condolences contain significant amounts of advice and reframing, with less focus on the condolence giver. these condolences can correspond to even higher levels of personcenteredness by trying to engage with the other's experience through advice. ## empathy in condolence at a high level, empathy requires a person to imagine the experience of another as they felt it-to put themselves in the other's shoes. in condolence, empathy provides a powerful, person-centered framing for validating and connecting with those in distress. distressed individuals have found empathetic condolences more supportive than sympathetic messages @xcite ; estate you your they can money them estate their pay funeral sadness rip rest peace crying i'm you're onions you man missed travel you they your can car them when back their fire sports his game him team fan fans they when play hit dieting you your can yourself care time good when day don't music his made time when song love story it's cry music video games game you play games your can they playing when time body his him back when they eyes head face their you personal exp. his him when years time dad died family day ago pets your you dog loss him they lost love cat life shootings people they their our tragedy them gun thoughts country medical you they can your help health pain doctor mental care relationships you your him they can his them yourself their dodged sympathy you your loss i'm hope hear love man family god cursing man i'm you shit made sad fuck fucking cry damn memes amp you respects pay press sad post play alexa your religion you god our they your their his people life can advice you your can yourself time feel life help don better school you your work school job can time they good college advice2 you it's don't your i'm you're people can they them shapiro, 2001) and more effective in clinical settings at helping the distressed resolve their emotions @xcite . empathy itself has many varying definitions in social psychology @xcite and the limited computational work employing empathy has largely focused only on mirroring emotional state as a way of empathizing @xcite @xcite . more recently abdul-mageed et al. (2017) and @xcite have gone beyond these simple models to develop and use a corpus for distress and empathy in reactions to news stories. these works adopt a broader definition drawn from multiple sources of empathy which mixes empathy with related concepts of compassion, altruism, and prosocial behavior. @xcite @xcite . in this work, we adopt a stricter definition of measuring empathy based on appraisal theory @xcite . here, empathy occurs when an observer appraises a person's situation in the same way as the person experiencing the distress. this definition more closely mirrors the person-centeredness of the response in terms of how the observer acknowledges and validates different aspects of the distressed person's mental state. following this definition, we create a new corpus around appraisalbased empathy and develop a classifier that can be used to label condolences for their empathy. ## ethics distress is inherently personal and computational studies on such matters warrant ethical consideration. in weighing the risks and benefits of our studies, the largest risk has been the loss of privacy, as individuals expressing their distress may have contextual expectations of privacy or anonymity @xcite . to mitigate this risk, we report only paraphrased examples and aggregate statistics. further, we only release this data to researchers upon request and provided they follow similar privacy practices. as a counter balance, this study has considerable benefit by providing better information on what makes for effective condolences; the insights from this study can be distilled into practical advice that can make for more supportive online communities. ## conclusion distress is an omnipresent part of life, and individuals turn to their social circle and social platforms for support when experiencing it. in this paper, we have developed new computational models for recognizing distress, condolences to that distress, and empathy within condolence. applying those models, we examine the dynamics of distress and condolence, showing that not all distress is treated equally online, and there exist regular structures within condolence. through analyzing millions of condolence responses, we test what makes for effective condolence online, showing that while some features predicted from observation studies hold true online, e.g., increasing person-centeredness of the message (high and dillard, 2012), distressed individuals did not find empathetic comments more helpful, suggesting different goals from online support. our results have important implications for (i) individuals by providing concrete suggestions of how to express one's distress to make it more likely to receive support, (ii) site operators by allowing them to observe the emotional health and responsiveness of their community, potentially reaching out to underserved individuals who have yet to receive support, and (iii) the general public for authoring more effective supportive messages. models and reproducible code are available at https://blablablab.si. umich.edu/projects/condolence/ and data is made available upon request.