Olya Hakobyan

David S. Johnson, Olya Hakobyan, Hanna Drimalla

Machine learning is frequently used in affective computing, but presents challenges due the opacity of state-of-the-art machine learning methods. Because of the impact affective machine learning systems may have on an individual's life, it is important that models be made transparent to detect and mitigate biased decision making. In this regard, affective machine learning could benefit from the recent advancements in explainable artificial intelligence (XAI) research. We perform a structured literature review to examine the use of interpretability in the context of affective machine learning. We focus on studies using audio, visual, or audiovisual data for model training and identified 29 research articles. Our findings show an emergence of the use of interpretability methods in the last five years. However, their use is currently limited regarding the range of methods used, the depth of evaluations, and the consideration of use-cases. We outline the main gaps in the research and provide recommendations for researchers that aim to implement interpretable methods for affective machine learning.

Florian Martin, Olya Hakobyan, Hanna Drimalla

Chat communication is often fast-paced, creating the expectation of quick replies. While the timing of exchanges is known to foster closeness and enjoyment, it remains largely unexplored whether chat partners with strong ties reciprocate each other's response times. Using 3.4 million messages from 889 chats across 97 donations of anonymous WhatsApp and Instagram chats, we analyzed response times, their balance between chat partners, and its stability over time. To our knowledge, this is the first study to examine response speed as an expression of reciprocity, bridging a key aspect of online communication with a fundamental principle of social interactions. We found that around 70% of WhatsApp and 44% of Instagram messages were answered within five minutes, confirming the fast pace of instant messaging. Overall, the response speed between chat partners was similar. The response speed similarity was evident both in the overall response-time distributions of chat partners assessed with Jensen-Shannon distance and in the steep regression slopes (0.786 for WhatsApp and 0.796 for Instagram) linking one person's probability of responding within five minutes to the partner's corresponding probability. Importantly, the dispersion of response time similarity over months showed that this balance persists over time. Our results position response time balance as a marker of reciprocity in computer-mediated communication, offering a new way to quantitatively study this fundamental principle of social interaction. We suggest using response speed balance as a complementary metric in the analysis of relationship dynamics, such as the strengthening or weakening of social ties.