Quantifying Intimacy in Language (2020)

Abstract: Intimacy is a fundamental aspect of how we relate to others in social settings. Language encodes the social information of intimacy through both topics and other more subtle cues (such as linguistic hedging and swearing). Here, we introduce a new computational framework for studying expressions of the intimacy in language with an accompanying dataset and deep learning model for accurately predicting the intimacy level of questions (Pearson r = 0.87). Through analyzing a dataset of 80.5M questions across social media, books, and films, we show that individuals employ interpersonal pragmatic moves in their language to align their intimacy with social settings. Then, in three studies, we further demonstrate how individuals modulate their intimacy to match social norms around gender, social distance, and audience, each validating key findings from studies in social psychology. Our work demonstrates that intimacy is a pervasive and impactful social dimension of language.

## introduction intimacy is a vital ingredient in the hierarchy of human needs @xcite @xcite , playing key roles in development @xcite and well-being @xcite . language provides multiple means of conveying intimacy in a social context as individuals make decisions on the topic of conversation, phrasing, and markers relating the speaker to the world. much like how social status and power are constructed and expressed, individuals negotiate intimacy in language to fulfill fundamental and strategic needs, while respecting social norms about the appropriate intimacy @xcite . in this paper, we aim to quantify the intimacy expressed in language and demonstrate how this intimacy is constructed and employed across diverse social settings. while sociolinguistics and social psychology have long pointed to how people shape their language to convey social information @xcite @xcite @xcite , only recently, have computational models focused on making this information explicit @xcite @xcite . in particular, works on social status and power have shown how individuals use lexical cues and linguistic strategies like accommodation to express their perceived status in relation to others @xcite . much like status in society, intimacy is a natural concept describing how an individual relates to their audience in their perceived interdependence, warmth, and willingness to personally share @xcite . our work provides the first model of intimacy in language and tests its implications. in this paper, we examine the intimacy of questions. as requests for information, questions provide a natural mechanism for studying how people shape the intimacy of their questions in response to the social context @xcite . questions serve a fundamental role in dialogues for interpersonal exchange @xcite , and increasingly intimate questions are known to foster deep social ties @xcite . our work starts from a new dataset of 2,397 questions rated for intimacy using best-worst-scaling @xcite . we use this dataset to train a deep learning model that obtains high correlation with human judgment on both in-domain and out-domain settings. using this model, we rate 80,484,458 questions across reddit, twitter, literature, and film. through this massive dataset, we demonstrate how individuals actively construct their social context through linguistic choices that signal the acceptability of a question's intimacy. given the role of intimacy in social interactions, we examine the social perspective of intimacy in conversations in three settings. first, we show that the intimacy level of language reflects cultural norms of masculinity and femininity, which our study shows persist across real and imagined settingsand even across other gender's perceptions of the norms. second, even online, individuals reserve their most intimate questions for close friends and strangers, mirroring offline observations, where the risk of social capital loss is greatest for acquaintances. finally, online communication affords a new communication mechanism-complete anonymity-for communicating, which we demonstrate is used as a strategy to ask more intimate questions. both the model and datasets used in this paper are released at https://blablablab. si.umich.edu/projects/intimacy/ . ## theories of intimacy as a natural concept in social settings @xcite , intimacy has been widely explored in interpersonal, behavioral, and social domains @xcite @xcite . studies of intimacy in communication have primarily focused on the exchange of personal information as a measure of intimacy @xcite . yet, intimacy in language is revealed more than just through disclosure, as individuals select topics, phrasings, and styles to indicate their intimacy with another within the social context. here, we study the role of intimacy in language and argue that intimacy is a natural component of language similar to politeness @xcite . following, we discuss the theoretical background of intimacy in social psychology and linguistics. the concept of intimacy the concept of intimacy has long been proposed by psychologists with various definitions. while intimacy generally refers to the closeness and interdependence of partners, the extent of self-disclosure, and the warmth or affection experienced within the relationship @xcite , the concept of intimacy is not restricted to the closeness or interactions between people in intimate relationships, as even people who are not in intimate relationships can have intimate interactions in a certain space and time @xcite . therefore, a widely-accepted conceptualization of intimacy is to distinguish between intimate interactions and intimate relationships @xcite . the former refers to dyadic communicative exchanges, while the latter is the history and future expectations of intimate contact over time @xcite . intimate interactions and intimate relationships are interrelated in the following way: while intimate interactions are necessary in the formation of intimate relationships and are regular within them @xcite , intimate relationships also build expectations for the depth and types of interactions between people @xcite . in this work, we focus on the language aspect of intimate interactions as dialogue is one of the core forms of intimate interactions @xcite and certain ways of communications can facilitate the experience of interpersonal closeness @xcite . ## quantifying language intimacy questions provide a natural mechanism to study the intimacy of language. in conversation, questions are frequently used to request information @xcite , providing the interlocutor with the opportunity to respond at a desired level of intimacy. this interactive questioning process can lead to the formation of intimate relationships as the subject matter and nature of disclosure increase over time @xcite . in this study, we aim to quantify intimacy in questions as a continuous variable because people naturally perceive intimacy along a continuum @xcite . following, we describe the dataset and annotation process for rating questions by intimacy. data questions are drawn from 41 manually selected question-centered subreddits, e.g., r/askreddit, which encompass a wide variety of topics and conversation styles. the initial set of questions is derived from all post titles made in 2018 containing one question mark. then for each raw question, we remove reddit-specific markup, e.g., "[17m]" or address terms to the community, e.g., "members of r/askscience, . . .", replaced common abbreviations, e.g., "aita (am i the asshole)" with their full expressions. to ensure questions are self-contained, we require the question to be the entire post title and be a single sentence with at least four words. in total, this process yielded 3,212,969 questions; appendices a and g contain the list of selected subreddits and question cleaning process. from this dataset, we select 2,247 questions to annotate, balancing across months. annotation rating the precise intimacy of question is a challenging task due to the potentially subjective nature of the question and the relative expectations of intimacy. rather than directly estimating an intimacy value through scale-based annotation, we treat intimacy as a latent variable to be inferred from relative comparisons between questions. following prior work in nlp on annotating social aspects of language @xcite , we adopt a best-worst-scaling (bws) @xcite scheme to estimate the latent intimacy values of questions. here, four questions are shown as a tuple, and annotators are asked to identify the most intimate and least intimate questions of the tuple. as questions could be interpreted in multiple ways depending on the context, annotators were instructed to consider their judgments according to the expected intimacy if the question is asked in a scenario appropriate to its usage. each tuple annotation generates five pair-wise comparisons between questions' intimacy values that act as constraints when inferring the latent value on a continuous scale. prior to annotation, two annotators went through three rounds of training and discussed all disagreements. following, annotators labeled an initial 212 tuples to assess exact agreement and subsequently, all other tuples were divided up between the two for annotation; these 8,563 tuples included 2,397 questions comprising 2,247 reddit questions and an additional 150 questions from books, movies and twitter for generalizability tests. each question was presented in at least 12 tuples to ensure an accurate approximation. to infer the latent intimacy values of all questions, we use iterative luce spectral ranking @xcite to convert the pair-wise comparisons into real-valued scores ranging from -1 (least intimate) to 1 (most intimate). to test the reliability of the ranked scores, we follow best practices @xcite and compute the split half ranking (shr) by randomly splitting all the tuples into two sets, compute the intimacy scores within each, and compare the rankings; note that as the same questions appeared in both annotators' sets of tuples, the inferred ranks in each split reflect the judgments of both annotators. the pearson's r between the two sets' ranking scores is 0.776, which demonstrates high reliability in the annotations. see appendices h and i for annotation guidelines and data samples. additionally, annotators attained krippendorff's α=0.548 on 212 tuples. while this α is moderate as inter-annotator agreement (iaa) is normally measured, in bws, lower agreement is expected when annotators encounter tuples where all four items are perceived to have essentially the same value, e.g., four factual questions asking nothing intimate; in such settings, annotators are likely to arbitrarily ## predicting the intimacy of questions question intimacy is predicted using model-based regressors. we test two baseline models and two deep-learning regressors based on neural language models. as baselines, we include two linear regression models with l2 regularization separately trained on either (1) bag of words features or (2) topic features. bag of words features are constructed with unigrams, bigrams and trigrams. the second model uses an lda model trained with 50 topics using mallet foot_1 over a sample of 1m questions that includes the annotated questions; each question is then represented using its topic distribution for regression. our neural regressors use the roberta @xcite language model as a base. we include two variants: one which is fine-tuned on 3m unannotated questions on a masked language modeling task, and a second which uses the default parameters in roberta. training uses only the 2,247 annotated reddit questions, split 8:1:1 into training, validation, and test. model settings and validation performance are listed in appendix e. ## intimacy and pragmatic choices in language, individuals can construct intimacy through stylistic choices that signal their view of the world and personal relationship to the proposed ideas @xcite . when questions carry the risk of losing face-e.g., broaching more intimate topics beyond what is socially acceptable in the current context-individuals reduce their commitment to the act through linguistic mitigation @xcite . here, we connect interpersonal pragmatics to the language of intimacy, showing how individuals perform pragmatic acts in their questions to mitigates risk as intimacy increases, much like how politeness is employed to save face @xcite . in particular, we examine pragmatic choices in questions around (i) the speaker's certainty, expressed in hedges from @xcite (ii) the speaker's belief of the social distance, expressed in swearing. to analyze these choices, we compare the mean intimacy ratings in questions relative to whether a specific strategy is employed; to ease comparison across datasets, we first standardize intimacy ratings within each domain. certainty hedging is a marker of intentional vagueness @xcite and aims to reduce risks in interpersonal communications @xcite . expressing uncertainty in a question can allow people to ask more intimate questions without the risk of inappropriateness. for example, in the following two questions, the hedge (i.e., might) serves to allow the respondent to answer with uncertainty or vagueness, reducing the risk from forcing an overly-intimate answer. figure 1 (right) shows that questions containing hedging words are generally more intimate than other questions, which is highly consistent across different domains. this result indicates people regularly employ hedging as a strategy to reduce risk when asking more intimate questions. social distance while the use of swear words explicitly transgresses social norms @xcite , the act of swearing can express the speaker's perceived solidarity with the audience @xcite . in this discursive act, the intentional act of swearing emphasizes in-group status with the audience and normalizes the use of words that would be taboo to out-group members (fägersten, 2012, p. 99). therefore, when asking questions, swearing may be employed to construct the perception of stronger social ties that would license more intimate questions. large-scale analysis across domains also supports this hypothesis. figure 1 (left) shows that questions containing swear words are far more intimate than others. this finding is consistent in both real conversations (twitter, reddit) and imagined conversations (movies, books). ## gender norms in language intimacy gender is one of the earliest learned social norms for individuals @xcite , with strong gender expectations around intimacy in conversation @xcite . social psychologists have found that women show more interest in verbal intimacy than men @xcite , and are more likely to initiate intimate verbal interactions in marriages @xcite . even in friendship, female friendships typically involve more intimate self-disclosures than male friendships @xcite @xcite . our four datasets provide an ideal setting for testing theories of gender expectations of intimacy along two fronts. first, relatively-anonymous social media like reddit provide few social cues about the identity of the person; in these deindividuated settings, do gender norms persist? second, film and literature reflect imagined conversations that require authors to "do gender" @xcite ) from their internalized expectations around intimacy, which is not regulated by actual loss of face for norm violations. in these imagined settings, do authors perform gender expectations on their characters, and are expectations consistent for authors of a different gender? methods a user's gender foot_2 in social media is inferred from their username using genderperformer @xcite , which was trained to operate on social media like reddit and only returns a gender label for usernames that strongly perform male or female. in movie scripts, we use both the gender labels provided for 3,015 characters in the cornell movie dialogue dataset (danescu-niculescu-mizil and lee, 2011) and a second approach to infer gender for another 2,872 characters using a name database based on us baby names from 1930-2015. for questions in books, booknlp @xcite is used to identify the speaker of each question using coreference resolution to identify a canonical name; the speaker's name is then matched using us census names and checked against gendered titles (e.g., mr.) or roles ("mother"). additional details on the matching process are provided in appendix b. to test for differences in intimacy norms for authors, we construct separate mixed-effect regressions to predict the intimacy of the question for male and female authors. each domain may have its own level of intimacy, therefore we standardize all intimacy scores within domain to compare z-scores across domain when examining the effect of dyadic gender composition. each regression includes a fixed effect for the gender of the speaker and audience and nested random effects for the author and book. these random effects effectively control for idiosyncratic differences in authors' perceptions of intimacy, relative differences across genres, and the time period in which the book was written. from this regression, we estimate the average marginal effect on intimacy for depicting a particular gender composition of the dyad, using female-female as the reference category. results dyadic interactions in all four datasetsreal and imagined-follow expected social norms for gender and intimacy (figure 2 ). although the relative intimacy levels differ across datasets, female-female questions were the most intimate and the presence of a female audience licenses males to ask more intimate questions, on par with those of females. in contrast, male-male dyadic interactions follow the low-intimacy hegemonic norms of masculinity, where men are supposed to be strong, rational, and inexpressive of personal emotions @xcite . do female authors also perpetuate gendered intimacy norms of males or are the trends in figure 2 driven by male authors only? shown in figure 3 , these norms persist regardless of whether the interaction is described by a male or female author: male-male interactions have the lowest intimacy 0.06 0.04 0.02 0.00 ## social distance and intimacy the appropriateness of a specific level of intimacy and associated cost for transgressing expectations vary depending on the social expectations. among close friends, intimate questions are a natural form of discourse and carry low social risk @xcite . however, people may also share very intimate information with strangers @xcite , commonly referred to as the strangers on a train effect @xcite ). individuals in these encounters have little likelihood of future interactions, removing the consequences for violating intimacy norms around increased disclosure @xcite . in contrast to both friends and strangers, individuals are least intimate with casual acquaintances for which there are some expectations of potential future interaction and, therefore, longer-term consequences for norm violations. together these behaviors point to a hypothesized ushaped relationship between intimacy and social distance in in-person settings @xcite . in social media, individuals come in contact with all three of these cohorts and have the potential to regularly connect individuals with complete strangers. here, we ask whether these offline behaviors translate to a "strangers on the internet" phenomenon. ## anonymity as audience design social media creates a new affordance for sidestepping the norms around intimacy: anonymity. by communicating through an anonymous account, an individual ensures that they are viewed as a stranger, removing the social cost of norm transgressions around intimacy for gender and social distance. prior work has shown that the use of anonymous accounts is not necessarily driven by their willingness to publicly disclose, but rather around perceived anonymity and privacy as a way of performing identity and boundary management @xcite . individuals shift their language based on the expected audience, with @xcite ) noting that these shifts can "simulate or create intimacy with a stranger;" social media complicates this audience design process through its context collapse @xcite where individuals must choose content and style to simultaneously match the norms and expectations of their different social circles @xcite . given an audience of an unknown composition, individuals may be inhibited from style-shifting into more intimate language due to the perceived risk of social capital loss. however, full anonymity could free the speaker from the penalty of norm violations, allowing them to shift to a desired intimacy level without risk. following, we test to what degree does anonymity facilitate increased intimacy. methods anonymous accounts were collected by identifying posts made in 2018 on reddit by usernames containing throwaway or anonymous, which are recognized markers of intentional anonymity on reddit @xcite . the intimacy of language by anonymous accounts is compared relative to three groups: (1) accounts containing a first name in the username, e.g., samiscool, as these potentially signal a closer association with personal identity, which we refer to as name containing; (2) accounts without any explicit demographic or identity marker, e.g., atomiccyle, 0.000 0.005 0.010 0.015 0.020 intimacy name containing depersonalized anonymous figure 5 : averaged marginal effect on intimacy in language for specific types of reddit accounts, relative to all other accounts as the reference category, shows that anonymous accounts have higher intimacy in their language than other accounts in the same communities. ## discussion and ethics this work has focused on analyzing intimacy in language through questions, showing consistent findings across four studies of how individuals modulate intimacy in their communication with respect to the norms of their social surroundings. although questions are only one part of language, they represent a natural starting point due to their interpersonal nature and our findings open the door to broader studies on other types of communication. further, our work has applications in many nlp settings. for example, intimacy measurements can provide a useful metric for context-sensitive offensive language detection; given an ongoing conversation, a question that is substantially more intimate than normal suggests that it might be offensive (or at least violate social norms). our models would allow tracking intimacy changes to separate offensive questions from those in conversations that gradually become more intimate. as a second example, dialog systems can benefit from intimacy models through adjusting their language to match user preferences-or potentially encourage interactions that lead to more intimate topics. the study of intimacy in language necessitates a discussion of ethical choices and implications. all experiments were performed on public data, in accordance with terms of service; as users of social media have contextual expectations of privacy, all examples of questions and usernames in this paper have been paraphrased to preserve anonymity. one risk posed by our technology is using these models to seek out especially-intimate questions from users in order to abuse or embarrass them. as one potential mitigation, platforms might use this same technology to prompt users to switch to a throw-away account when asking the question. ## conclusion this paper represents a step towards a full understanding of the social information in language through new data and models for studying intimacy in language. by developing a high-quality dataset of questions rated for their intimacy and a corresponding model that closely correlates with human judgments, we study 80.5m questions across social media, books, and movies to reveal how individuals shape and react to their social setting through selecting the intimacy of their language. in four studies, we show that the intimacy of language is not only a personal choice, where people may use different linguistic strategies for the expressions of intimacy but reflects constraints from social norms, including gender and social distance. our study provides strong evidence for existing findings in social psychology and also enriches the study of computational sociolinguistics in nlp community.