Mobile experience sampling methods~(ESMs) are widely used to measure users' affective states by randomly sending self-report requests. However, this random probing can interrupt users and adversely influence users' emotional states by inducing disturbance and stress. This work aims to understand how ESMs themselves may compromise the validity of ESM responses and what contextual factors contribute to changes in emotions when users respond to ESMs. Towards this goal, we analyze 2,227 samples of the mobile ESM data collected from 78 participants. Our results show ESM interruptions positively or negatively affected users' emotional states in at least 38\% of ESMs, and the changes in emotions are closely related to the contexts users were in prior to ESMs. Finally, we discuss the implications of using the ESM and possible considerations for mitigating the variability in emotional responses in the context of mobile data collection for affective computing.
With the growing prevalence of affective computing applications, Automatic Emotion Recognition (AER) technologies have garnered attention in both research and industry settings. Initially limited to speech-based applications, AER technologies now include analysis of facial landmarks to provide predicted probabilities of a common subset of emotions (e.g., anger, happiness) for faces observed in an image or video frame. In this paper, we study the relationship between AER outputs and self-reports of affect employed by prior work, in the context of information work at a technology company. We compare the continuous observed emotion output from an AER tool to discrete reported affect obtained via a one-day combined tool-use and diary study (N=15). We provide empirical evidence showing that these signals do not completely align, and find that using additional workplace context only improves alignment up to 58.6%. These results suggest affect must be studied in the context it is being expressed, and observed emotion signal should not replace internal reported affect for affective computing applications.
We frequently utilize face emojis to express emotions in digital communication. But how wholly and precisely do such pictographs sample the emotional spectrum, and are there gaps to be closed? Our research establishes emoji intensity scales for seven basic emotions: happiness, anger, disgust, sadness, shock, annoyance, and love. In our survey (N = 1195), participants worldwide assigned emotions and intensities to 68 face emojis. According to our results, certain feelings, such as happiness or shock, are visualized by manifold emojis covering a broad spectrum of intensities. Other feelings, such as anger, have limited and only very intense representative visualizations. We further emphasize that the cultural background influences emojis' perception: for instance, linear-active cultures (e.g., UK, Germany) rate the intensity of such visualizations higher than multi-active (e.g., Brazil, Russia) or reactive cultures (e.g., Indonesia, Singapore). To summarize, our manuscript promotes future research on more expressive, culture-aware emoji design.
``Time spent on platform'' is a widely used measure in many studies examining social media use and well-being, yet the current literature presents unresolved findings about the relationship between time on platform and well-being. In this paper, we consider the moderating effect of people’s mindsets about social media — whether they think a platform is good or bad for themselves and for society more generally. Combining survey responses from 29,284 participants in 15 countries with server-logged data of Facebook use, we found that when people thought that Facebook was good for them and for society, time spent on the platform was not significantly associated with well-being. Conversely, when they thought Facebook was bad, greater time spent was associated with lower well-being. On average, there was a small, negative correlation between time spent and well-being and the causal direction is not known. Beliefs had a stronger moderating relationship when time-spent measures were self-reported rather than coming from server logs. We discuss potential mechanisms for these results and implications for future research on well-being and social media use.