# BERT meets LIWC: Exploring State-of-the-Art Language Models for Predicting Communication Behavior in Couples' Conflict Interactions

arXiv: https://arxiv.org/abs/2106.01536 · HTML: https://mlsanity.com/paper/2106.01536/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Multiscale Visualization of Attention in the Transformer Model](https://mlsanity.com/paper/1906.05714/) (2019)
- [Sparsely Connected and Disjointly Trained Deep Neural Networks for Low Resource Behavioral Annotation: Acoustic Classification in Couples' Therapy](https://mlsanity.com/paper/1606.04518/) (2016)
- [Predicting Behavior in Cancer-Afflicted Patient and Spouse Interactions using Speech and Language](https://mlsanity.com/paper/1908.00908/) (2019)
- [Modeling Interpersonal Influence of Verbal Behavior in Couples Therapy Dyadic Interactions](https://mlsanity.com/paper/1805.09436/) (2018)

## Built on by (most cited first, 8 of 8)

- [A Causal Research Pipeline and Tutorial for Psychologists and Social Scientists](https://mlsanity.com/paper/2206.05175/) (2022)
- [Leveraging Open Data and Task Augmentation to Automated Behavioral Coding of Psychotherapy Conversations in Low-Resource Scenarios](https://mlsanity.com/paper/2210.14254/) (2022)
- [SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modeling](https://mlsanity.com/paper/2308.04365/) (2023)
- [The More Similar, the Better? Associations between Latent Semantic Similarity and Emotional Experiences Differ across Conversation Contexts](https://mlsanity.com/paper/2309.12646/) (2023)
- [Empirical Analysis of Korean Public AI Hub Parallel Corpora and in-depth Analysis using LIWC](https://mlsanity.com/paper/2110.15023/) (2021)
- [Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data](https://mlsanity.com/paper/2207.00489/) (2022)
- ["Are you okay, honey?": Recognizing Emotions among Couples Managing Diabetes in Daily Life using Multimodal Real-World Smartwatch Data](https://mlsanity.com/paper/2208.08909/) (2022)
- [psifx -- Psychological and Social Interactions Feature Extraction Package](https://mlsanity.com/paper/2407.10266/) (2024)

## Abstract (arXiv)

Many processes in psychology are complex, such as dyadic interactions between two interacting partners (e.g. patient-therapist, intimate relationship partners). Nevertheless, many basic questions about interactions are difficult to investigate because dyadic processes can be within a person and between partners, they are based on multimodal aspects of behavior and unfold rapidly. Current analyses are mainly based on the behavioral coding method, whereby human coders annotate behavior based on a coding schema. But coding is labor-intensive, expensive, slow, focuses on few modalities. Current approaches in psychology use LIWC for analyzing couples' interactions. However, advances in natural language processing such as BERT could enable the development of systems to potentially automate behavioral coding, which in turn could substantially improve psychological research. In this work, we train machine learning models to automatically predict positive and negative communication behavioral codes of 368 German-speaking Swiss couples during an 8-minute conflict interaction on a fine-grained scale (10-seconds sequences) using linguistic features and paralinguistic features derived with openSMILE. Our results show that both simpler TF-IDF features as well as more complex BERT features performed better than LIWC, and that adding paralinguistic features did not improve the performance. These results suggest it might be time to consider modern alternatives to LIWC, the de facto linguistic features in psychology, for prediction tasks in couples research. This work is a further step towards the automated coding of couples' behavior which could enhance couple research and therapy, and be utilized for other dyadic interactions as well.