What Motivates You? Benchmarking Automatic Detection of Basic Needs from Short Posts (2021)

Abstract: According to the self-determination theory, the levels of satisfaction of three basic needs (competence, autonomy and relatedness) have implications on people’s everyday life and career. We benchmark the novel task of automatically detecting those needs on short posts in English, by modelling it as a ternary classification task, and as three binary classification tasks. A detailed manual analysis shows that the latter has advantages in the real-world scenario, and that our best models achieve similar performances as a trained human annotator.

## introduction motivation is one of the most crucial aspects of human behaviour with implications ranging from daily life to career and educational contexts. self-determination theory (sdt) provides a metaframework for understanding the broad, as well as specific, nutriments of the function and application of the concept of motivation @xcite . sdt differs from the other motivational theories from the psychology literature in two substantial aspects @xcite : (1) unlike the drive theories that explain motivation as a function of its deficit (e.g. people are motivated by success to compensate its deficit), sdt focuses on growth and constructivism (e.g. people are naturally and universally motivated by success), thus giving the theory a more realistic understanding of the human behaviour, and making it applicable to wider contexts; and (2) due to the applicability advantage, sdt is based on strong behavioural evidence and is thus not only a wellvalidated model but also sustainable and actionable. the sdt framework is supported by a body of cross-cultural studies strengthening the universality of the theory. studies conducted in diverse countries showed that the basic needs are essentially represented across cultures @xcite . although universal, the sdt framework is also able to point out the impact of sociocultural environment on the variations of basic needs in different cultures. for example, a study conducted in 11 countries showed that the need for competence was more linked to school performance in eastern cultures than in the west @xcite . one of the central pillars of sdt are three basic psychological needs that drive the initiation of a behaviour and the maintenance of motivation: according to sdt, those three needs are universal and their importance does not change across individuals and situations. however, different contexts and time periods would require different support and resources for the maintenance of the motivations. for instance, cultivating autonomy need in students creates more engagement and willingness, thus leading to higher academic performance, lower dropouts, and more self-esteem in the long run @xcite . similarly, the sdt framework is used to increase levels of employee satisfaction and engagement, supportive leadership and parenting skills, healthier relationships, satisfactory consumer experience and better designed digital media and well-being tools @xcite @xcite @xcite @xcite . traditionally, basic motivations are assessed via questionnaires which provide intensity-based scores for each dimension. the scores represent the degree to which that particular dimension is satisfied @xcite . although these questionnaires were developed and validated via laboratory and field studies which provide a strong empirical basis, they could suffer from biases commonly observed in questionnaire respondents such as social desirability bias @xcite and the reference-group effect @xcite . the basic motivations can also be revealed in a more implicit way, by collecting subjects' narratives while showing them pictures and images @xcite . although being more expensive and time-consuming, as it requires the inclusion of trained assessors, this method shows that implicit motivations can be assessed from texts. a few studies attempted at automatic detection of basic motivations on the basis of their linguistic aspects from such naratives @xcite . to the best of our knowledge, our study is the first that attempts to automatically detect the three basic needs from short posts. in this study, we: ## dataset for our experiments, we used the first two layers of the basic psychological needs corpus @xcite , which is publicly available. 1 the 1 we obtained the original dataset directly from the authors. the final dataset with manual annotations of basic needs was already pre-filtered for nonemotional posts and those that do not contain enough signal @xcite . it contains 6334 posts with the following distribution of the labels: 1229 posts labelled with competence, 1771 with autonomy, and 3334 with relatedness label. in our experiments, we used this dataset and only the labels of the second layer of annotation (basic needs). several examples are given in here is important to note that the original dataset also contains, in the third layer, the annotation for the satisfaction level (satisfied, dissatisfied, neutral) of the assigned basic need. we acknowledge that the combination of the basic needs and their level of satisfaction are often used together, e.g. as indicators of person's well-being @xcite , violence and conflict possibility @xcite , stress and coping @xcite . however, we opted for discarding these additional labels for three reasons: (1) because the inter-annotator agreement was significantly lower for this annotation layer (the average pairwise agreement was 75% and the κ was 0.640); (2) so that we do not increase the total number of classes (to nine instead of three) and therefore significantly lower the number of instances in each class; (3) because this task appears similar to the task of assigning the sentiment polarity of the post @xcite , and therefore might be modelled with various other datasets. ## conclusions in this study, we benchmarked the automatic detection of basic motivations on short (twitter) posts in english, framing the problem as a ternary classification task, as well as three binary classification tasks. on the ternary classification task, our bert+bilstm model performed almost equally well as a trained human annotator. we showed that modelling this problem as three binary classification tasks, instead of modelling it as one ternary classification task, allows for better applicability of the models. the proposed setup with three binary models assigns none of the basic motivations to those posts without any signal (all three models assign a no class), and multiple basic motivations to those posts with signals from multiple motivations (more than one model assigns a yes class), achieving a high agreement with the human annotator. we also found a high association between the class probabilities of the binary models and the human-perceived motivation intensities.