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1. Condolence and Empathy in Online Communities

   https://doi.org/10.18653/v1/2020.emnlp-main.45  

   Zhou, Naitian; Jurgens, David (January 2020, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP))

   null (Ed.)

   Offering condolence is a natural reaction to hearing someone’s distress. Individuals frequently express distress in social media, where some communities can provide support. However, not all condolence is equal—trite responses offer little actual support despite their good intentions. Here, we develop computational tools to create a massive dataset of 11.4M expressions of distress and 2.8M corresponding offerings of condolence in order to examine the dynamics of condolence online. Our study reveals widespread disparity in what types of distress receive supportive condolence rather than just engagement. Building on studies from social psychology, we analyze the language of condolence and develop a new dataset for quantifying the empathy in a condolence using appraisal theory. Finally, we demonstrate that the features of condolence individuals find most helpful online differ substantially in their features from those seen in interpersonal settings. 

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2. Observing empathy in informal engineering activities with girls ages 7-14.

   Letourneau, S.M.; Bennett, D.; Liu, C.J.; Argudo, Y.; Peppler, K.; Keune, A.; Dahn, M.; & Culp, K.M. (July 2021, Annual Conference and Exposition, American Society for Engineering Education (ASEE))

   null (Ed.)

   There is growing evidence that emphasizing the social and personal contexts of engineering can open up the field to people who have been conspicuously underrepresented, particularly women and girls. Recent research and advances in educational policy have advocated for reframing engineering education to prioritize social responsibility, empathy, and care for others as integral aspects of engineering practice. But how do we measure things like empathy in engineering practices of younger children? This paper features work from a three-year design-based research project in which we used narrative elements to frame engineering problems in ways that evoked empathy for others’ needs, and examined the intersections between expressions of empathy and engineering design practices among girls ages 7-14. This paper outlines the theoretical underpinnings of this approach, and our methods for observing empathy and engineering practices in this age group. 

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3. Paths to Empathy: Heterogeneous Effects of Reading Personal Stories Online

   Roshanaei, M.; Tran, C.; Morelli, S.; Caragea, C.; Zheleva, E. (January 2019, Proceedings of IEEE Conference on Data Science and Advanced Analytics (DSAA))

   Every day people share personal stories online, reaching millions of users around the world through blogs, social media and news websites. Why are some of these stories more attractive to readers than others? What features of these personal narratives make readers empathize with the storyteller? Do the readers’ personal characteristics and experiences play a role in feeling connection to the story they read? Experimental studies in psychology show that there are several factors that increase empathy in the aggregate, but there is a need for deeper understanding of empathetic feelings at the individual level of storyteller, story, and reader. Here, we present the design and analysis of a survey that studied the impact of story features and reader predispositions and perceptions on the empathy they feel when reading online stories. We use causal trees to find the individual-level causal factors for empathy and to understand the heterogeneity in the treatment effects. One of our main findings is that empathy is contextual and, while reader personality plays a significant role in evoking empathy, the mood of the reader prior to reading the story and linguistic story features have an impact as well. The results of our analyses can be used to help people create content that others care about and to help them communicate more effectively 

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4. Using narratives to evoke empathy and support girls’ engagement in engineering

   Letourneau, S. M.; Bennett, D. (January 2020, Connected science learning)

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   Reframing engineering activities to emphasize the needs of others has the potential to strengthen engineering practices like problem scoping, while also providing more inclusive and socially relevant entry points into engineering problems. In this design-based research project, we developed novel strategies for adding narratives to engineering activities to deepen girls’ engagement in engineering practices by evoking empathy for the users of their designs. We describe a set of hands-on engineering activities developed through iterative development and testing with 190 girls (ages 7-14) at the New York Hall of Science. Findings show how elements of narrative (like characters and settings) evoked learners’ empathy, and how learners’ expressions of empathy related to practices like problem scoping and iteration. A set of design principles summarizes critical features of the narrative activities for evoking empathy and supporting the engineering design process. Finally, we offer recommendations for practitioners who would like to use narratives to engage learners in approaching engineering problems from a user-centered perspective. This work has implications for the development of inclusive and engaging engineering activities that appeal to elementary and middle school learners in a wide range of settings. 

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5. Depth Videos for the Classification of Micro-Expressions

   https://doi.org/10.1109/ICPR48806.2021.9412976  

   Rakesh Kumar, Ankith Jain; Bhanu, Bir; Casey, Christopher; Cheung, Sierra Grace; Seitz, Aaron (January 2021, 2020 25th International Conference on Pattern Recognition (ICPR))

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   Facial micro-expressions are spontaneous, subtle, involuntary muscle movements occurring briefly on the face. The spotting and recognition of these expressions are difficult due to the subtle behavior, and the time duration of these expressions is about half a second, which makes it difficult for humans to identify them. These micro-expressions have many applications in our daily life, such as in the field of online learning, game playing, lie detection, and therapy sessions. Traditionally, researchers use RGB images/videos to spot and classify these micro-expressions, which pose challenging problems, such as illumination, privacy concerns and pose variation. The use of depth videos solves these issues to some extent, as the depth videos are not susceptible to the variation in illumination. This paper describes the collection of a first RGB-D dataset for the classification of facial micro-expressions into 6 universal expressions: Anger, Happy, Sad, Fear, Disgust, and Surprise. This paper shows the comparison between the RGB and Depth videos for the classification of facial micro-expressions. Further, a comparison of results shows that depth videos alone can be used to classify facial micro-expressions correctly in a decision tree structure by using the traditional and deep learning approaches with good classification accuracy. The dataset will be released to the public in the near future. 

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