Prevention or Promotion? Predicting Author’s Regulatory Focus (2023)

Abstract: People differ fundamentally in what motivates them to pursue a goal and how they approach it. For instance, some people seek growth and show eagerness, whereas others prefer security and are vigilant. The concept of regulatory focus is employed in psychology, to explain and predict this goal-directed behavior of humans underpinned by two unique motivational systems – the promotion and the prevention system. Traditionally, text analysis methods using closed-vocabularies are employed to assess the distinctive linguistic patterns associated with the two systems. From an NLP perspective, automatically detecting the regulatory focus of individuals from text provides valuable insights into the behavioral inclinations of the author, finding its applications in areas like marketing or health communication. However, the concept never made an impactful debut in computational linguistics research. To bridge this gap we introduce the novel task of regulatory focus classification from text and present two complementary German datasets – (1) experimentally generated event descriptions and (2) manually annotated short social media texts used for evaluating the generalizability of models on real-world data. First, we conduct a correlation analysis to verify if the linguistic footprints of regulatory focus reported in psychology studies are observable and to what extent in our datasets. For automatic classification, we compare closed-vocabulary-based analyses with a state-of-the-art BERT-based text classification model and observe that the latter outperforms lexicon-based approaches on experimental data and is notably better on out-of-domain Twitter data.

## introduction what motivates a person to pursue a goal and what type of strategies they apply to achieve this goal differs from person to person. for instance, some people brush regularly to maintain healthy teeth and gums, while others do the same to avoid cavities; their goal is the same but the motivation is different. the regulatory focus (rf) theory @xcite from psychology, explains the goal-directed behavior of humans underpinned by two unique motivational systemspromotion and prevention. promotion-focused individuals approach a goal by striving for achievements, taking an eager approach, and are interested in maximizing the gains. on the other hand, prevention-focused ones strive for security, are sensitive to losses, avoid negative outcomes, avert risks, and are vigilant. this framework is predominantly used to explain consumer behavior, organizational behavior, message framing, or information processing @xcite @xcite . automatic detection of regulatory focus helps psychology researchers to bypass the need for manual coding or self-reports which are prone to social desirability. automatic detection would allow for a more objective and standardized measurement of regulatory focus, removing egocentric biases and subjectivity. in the case of downstream applications, like computer-mediated communication, understanding the behavioral inclination of a person allows one to tailor response messages to fit their motivational orientation, facilitating a more persuasive and effective dialogue between the interlocutors. such tailoring of messages to match the dominant regulatory focus has proven effective in health communication, promoting positive behavior change in areas like exercise @xcite , diet @xcite , and dental hygiene @xcite . it has also been successfully applied in organizational behavior, marketing, leadership, and many other domains of psychology @xcite . with more than 1,500 journal publications and more than 70,000 citations, the concept is prominent in psychology previous studies on the topic of regulatory focus have reported that distinctive linguistic signatures are observed in an individual's text formulation corresponding to their goal attainment strategies @xcite . other studies relied on differences in linguistic features to manipulate regulatory focus or persuade people with a specific regulatory focus. overall, we conclude that promotion and prevention focus resonate with different linguistic patterns. inspired by these findings and their practical application in communication, we formulate the novel task of regulatory focus classification as an author profiling task. consider the two statements in in the course of our study, we create datasets containing event descriptions and social media data, in german, labeled with regulatory focus notions. we use correlation analysis to investigate the linguistic signatures of regulatory focus and ascertain the validity of our datasets. further, we conduct text classification experiments to explore the possibility of automatically detecting regulatory focus concepts from text. our experiments show that a bert-based classifier outperforms lexicon-based approaches popularly used in psychology and the classifier can generalize from experimental data to twitter data. the main contributions of the paper are (1) an introduction to the task of regulatory focus classification, (2) experimental and real-world datasets annotated with rf categories, (3) a correlation analysis to 1 https://www.webofscience.com/wos/woscc/citation-report/ aac080af- @xcite verify to what extent the findings from studies on regulatory focus as observable in the dataset using traditional methods and (4) performance comparison of rf classification using standard measures from psychology vs. state-of-the-art methods from nlp. our research aims to serve as a starting point for exploring the concept from a computational linguistics perspective and enable future studies. the datasets created as part of this study are freely available for research purposes. they can be accessed together with the corresponding code at https://www.ims.uni-stuttgart.de/ data/author-regulatory-focus-detection . ## data collection we create three different datasets as part of this studytwo containing self-reported event descriptions (edd-1 and edd-2) and one manually annotated twitter dataset (twd). the event description datasets are created by regulatory focus manipulation experiments and contain self-reported event descriptions provided by participants who were experimentally primed for one of the regu- focus instructions promotion ... a situation in which you felt you made progress towards (being successful in your life / achieving a goal that is important to you). ... a situation in which, compared to others, you felt like you were not making any progress towards (being successful in your life / achieving something). ... a situation in which you wanted to attain something that was very important to you personally, and in which you were able to do as well as you ideally would like. prevention ... a situation in which being careful enough avoided from getting into trouble. ... a situation in which lack of caution caused you to get into trouble. ... a situation in which you behaved in a way that no one could have found fault with. ## linguistic correlation analysis as discussed in section 2, previous research has reported that authors' regulatory focus orientation leaves distinctive markers in their language use. @xcite studied abstractness or concreteness of words used, while brockner and @xcite looked into expressed emotionality and vaughn (2018) investigated the differences in the description of hopes vs. duties. conley and higgins (2018 ) used lexical analysis of essays as a measure to regulatory focus. to investigate these linguistic features, they use the psychological categories defined in dictionary-based methods like linguistic inquiry and word count (liwc, @xcite . our analysis aims at confirming that these findings on the linguistic intricacies of regulatory focus are observable in our datasets as well. this serves on one side as a replication study of previous work and on the other side as a preliminary study for developing automatic rft classifiers based on these lexical resources. we consider the datasets edd-1, edd-2, and twd for this analysis. ## classification experiments the linguistic correlation analysis sheds some light on the strengths and limitations of traditional automated text analyses. we go one step further to assess how well we can automatically predict the regulatory focus of the author from the text. to this end, we explore open and closed vocabulary text classification methods. ## conclusion in this study, we bring attention to regulatory focus, a construct used in psychology to explain the goaloriented behavior of humans. to encourage nlp research into this topic, we introduce the novel task of regulatory focus classification (promotion vs. prevention) and datasets of experimental and real-world data annotated with the concept. our correlation analysis with lexicons uncovers corroborating evidence from previous research and also highlights some limitations of dictionary-based approaches. further, we apply automatic text classification methods for regulatory focus detection. the results show that a language-model-based classifier outperforms models which rely on lexical-level features. our best model identifies the regulatory focus inclination of a person from text with high accuracy and can be considered a strong baseline for future research. further, by evaluating the best model on manually annotated twitter data, we confirm the generalization capability of the model on unseen domains. we achieve good results by disregarding the preconceived relationship between an a priori list of words and psychological categories. instead, relying only on the language model's capability to learn them shows the best performance on the task. nevertheless, a model that can combine these two aspects would be worth investigating further. we also acknowledge that tweets might be too short or sometimes too vague in terms of context for the model to make a reliable prediction. as rft is a concept in between stable traits and variable states, consolidating multiple texts from the same author could be one possible way to produce a more accurate prediction on the author's regulatory focus. regulatory focus detection can find practical applications in general computer-mediated communication and human-computer interaction, where automatically identifying the needs, motivations, traits, etc., of the collocutor, ensures more efficient communication. for instance, a message that addresses the needs and motivation of the collocutor could be more persuasive or be received more positively. in future research, we would like to investigate paraphrasing of a given text to fit the regulatory focus of the counterpart and to what degree it influences the persuasiveness of a text.