Using Daily Language to Understand Drinking: Multi-Level Longitudinal Differential Language Analysis (2024)

Abstract: Analyses for linking language with psychological factors or behaviors predominately treat linguistic features as a static set, working with a single document per person or aggregating across multiple posts (e.g. on social media) into a single set of features. This limits language to mostly shed light on between-person differences rather than changes in behavior within-person. Here, we collected a novel dataset of daily surveys where participants were asked to describe their experienced well-being and report the number of alcoholic beverages they had within the past 24 hours. Through this data, we first build a multi-level forecasting model that is able to capture within-person change and leverage both the psychological features of the person and daily well-being responses. Then, we propose a longitudinal version of differential language analysis that finds patterns associated with drinking more (e.g. social events) and less (e.g. task-oriented), as well as distinguishing patterns of heavy drinks versus light drinkers.

## introduction language generated by people occurs at multiple levels of analysis, from tokens to documents to sequences of documents @xcite . while past works have suggested modeling language hierarchically given the available history of a person's language @xcite @xcite @xcite , few techniques exist for language analyses geared toward eliciting language associated with psychological or behavioral changes @xcite . where traditional techniques like differential language analysis @xcite only reveal differences between people rather than changes within people around particular behaviors. typically, nlp-based approaches represent language from people as aggregations, such as of message or token embeddings over all time @xcite @xcite . while there have been some predictivefocused works that have experimented with forecasting based on language, they are either focused on psychological (latent) attributes @xcite or focused on groups/communities of people rather than individuals @xcite , less has been done toward bringing out linguistic insights (e.g. differential language analysis @xcite ) leveraging the inherent multi-level longitudinal structure of human language. in this work, we present and evaluate (1) a longitudinal, multi-level approach to forecasting an individual's behavior rather than latent human attributes (e.g. emotions), namely daily consumption of alcoholic beverages, and (2) a longitudinal, multi-level differential language analysis to illuminate daily language patterns most commonly associated with heavier drinking both across different individuals and within one individual. with roughly 10% of u.s. adults having an alcohol use disorder @xcite , research to understand an individual's alcohol consumption pattern and motivation is a pressing health concern. by modeling one's behavior over time we can more accurately predict future consumption or interpret their motivations for drinking alcohol through the use of longitudinal multi-level models. such a model could be used to detect the risk of unhealthy drinking. these personalized models are naturally geared towards time-series forecasting, where the goal is to understand coming trends @xcite . our contributions include: (1) introduction of a sequential forecasting model that leverages language to accurately predict the number of alcoholic drinks a person will consume within a 24-hour window, (2) integration of user-level features (static across time) to build a multi-level sequential model for additional context in prediction, (3) empirical evaluation on dimensionality reduction of language features cross-time concerning predictive power, and (4) insights into linguistic patterns that are longitudinally predictable of high or low daily drinking rates. ## related work alcohol consumption psychological research has long demonstrated the complexities of alcohol consumption. on one hand, the general person drinks more on days when they feel more positive affect and not when they feel more negative effect @xcite , and general drinking level has a positive correlation to life satisfaction @xcite . on the other hand, this relationship is hump-shaped such that the happiest people are low to moderate drinkers and heavy drinkers are worse off with decreases in well-being @xcite . heavy alcohol consumption can lead to an alcohol use disorder, a disorder that can cause morbidity @xcite and decreased psychosocial functioning @xcite . predicting within-person alcohol consumption from scales that measure emotion such as positive affect have shown correlations between participant-aggregated affect and participant-aggregated number of drinks consumed of @xmath0.10 and a non-significant relationship to negative affect @xcite . a likely reason for the positive relationship between drinking and positive affect is that most drinking occurs socially @xcite and spending time with others is strongly associated with reporting high levels of positive affect @xcite @xcite . language and drinking while there exists a few studies focused on predicting who is at risk for alcohol abuse from language, they use historical data to make a single prediction in time rather than predicting how behaviors may change. both works of @xcite and @xcite investigate the connection of historical social media language and their association with at-risk drinking. however, they both focus on different levels of analysis and outcomes with @xcite focusing on individual-level and the ability to predict one's risk-level for alcohol consumption (e.g. audit-c) @xcite and @xcite leveraging county data with responses to the behavioral risk factor surveillance system (brfss); a u.s. health survey where someone may self-report their level of heavy drinking. longitudinal & multi-level nlp is very familiar with sequence processing leveraging various techniques such as attention networks @xcite and seq2seq modeling @xcite . even still, explicitly modeling the temporal dimension is largely under-utilized by most nlp models as words and sentences are often uttered at what can be assumed as the same point in time except for the case where language is considered to reflect a person @xcite . sequential models designed explicitly for temporal modeling have been proposed but not widely adopted by the nlp community @xcite . one could go one step further and adapt these sequential time-series models to account for the inherent hierarchical nature of language over time from a person through multi-level modeling. multilevel modeling allows the model to operate on different levels of granularity and offers a natural way of framing the problem @xcite . due to this natural hierarchy, in this case, defined by dynamic states and static traits cross-time @xcite @xcite , we can develop a model to account for this. multilevel modeling is a common approach in psychology research, for example understanding substance cravings and personality @xcite . lastly, we extend past works that explored the associations between social media language and drinking behavior by examining the association between topics of daily language, through selfreported experienced well-being responses, and alcohol consumption or risk. differential language analysis (dla) is commonly used to study topics of conversation and their ability to reliably predict certain outcomes @xcite @xcite . while the work of @xcite also investigated the relationship between specific social media topics and drinking risk, they focused on the between-person signals instead of within-person signals as in our approach. these within-person language signals are important for understanding what drives an individual to drink and are extracted via a fixed effects model that accounts for betweenperson heterogeneity @xcite . ## data we collected a novel dataset with the consent of study participants for a longitudinal investigation of drinking behavior. upon enrollment, each participant also gave consent to access their facebook posts and answer a "baseline" survey that asks various questions regarding mental health and well-being. responses from the baseline survey include: measures of depression and anxiety @xcite , audit-c @xcite , and demographics. further, participants are asked to complete 14 days of ecological momentary assessments (ema), short surveys expected to take a few minutes to complete on their phones. each ema contains a free response field called affective essay, where the participant describes their experienced well-being, emotions and daily experiences, as well as a question asking them how many alcoholic beverages they consumed in the past 24 hours 1 . participants were selected to respond once or thrice daily (morning, afternoon, evening). the assignment was performed randomly (50/50) for which group a person was placed into. the dataset samples from u.s. restaurant and hospitality workers (e.g., bartenders, servers, etc). recruitment occurred between june 2020 and june 2021 from various sources such as organizations reaching out to their members via mailing lists or snowball sampling from social media. sign-up and consent was handled via qualtrix, where directions were given to download a companion app designed to be used for data collection. figure 1 illustrates the drinking behaviors from a random sample of 30 participants over the 14 days ordered by audit-c score. the white empty cells indicate missing data points (no response) for that particular day. we observe that participants with higher audit-c scores tend to drink more often and with a higher number of drinks. time-series processing we split our time series into a train and test set based on out-of-sample time (e.g., forecasting) with a split such that each person's last two days of responses are reserved for testing. when building our forecasting dataset, we filter participants for those that responded to at least three days of emas. this is done so that these users can still be used for testing, as they have at least one authentic response to use as input. for building our time-series features, we include an averaged roberta embedding @xcite of all affective essays of a given day, which is then dimensionality reduced to using pre-trained pca models from @xcite . at each time step, we concatenate these language features with the number of drinks and another small set of features representing a day-of-week marker defined as a 7-dimension one hot encoded feature space. lastly, to deal with participants who do not always remember to respond each day, we apply a simple imputation technique that fills missing gaps with the last available authentic response @xcite . ## results here, we showcase results using three separate metrics. first, we focus on mean squared error (mse) as it is helpful to measure the impact of outliers where our models failed to predict as accurately and is also the metric we optimize for during training. second, mean absolute error (mae) shows errors within the same units (drinks per day). lastly, pearson r is used as a scale-invariant metric to show the relationship between model predictions and the actual trend. for all tables shown, linar refers to a linear ridge (l2-normalized) autoregressive model, gru is a gated recurrent neural network, and trns is our transformer based architecture. ## conclusion longitudinal, multi-level language analyses can be important for understanding human behavior, such as alcohol consumption and its motivations. in this work, we propose a multi-level longitudinal approach to analyze the language associations with drinking behaviors to find within-person signals. while much of previous work about language and drinking found characteristic differences between people, our approach yielded results that signal dayto-day changes, aligning with previous research on within-person changes in drinking associated with emotions and socializing. our multi-level approach also yielded evidence for differing drinking motivations between people depending on their alcohol use disorder risk level, with lower audit-c drinkers (those at lower risk) mentioning celebrations or special occasions more than those with higher risk.