Paths to Empathy: Heterogeneous Effects of Reading Personal Stories Online 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 storiesmore attractive to readers than others? What features of thesepersonal narratives make readers empathize with the storyteller? Do the readersu0027 personal characteristics and experiences play arole in feeling connection to the story they read? Experimentalstudies in psychology show that there are several factors thatincrease empathy in the aggregate, but there is a need for deeperunderstanding of empathetic feelings at the individual level ofstoryteller, story, and reader. Here, we present the design andanalysis of a survey that studied the impact of story featuresand reader predispositions and perceptions on the empathy theyfeel when reading online stories. We use causal trees to find theindividuallevel causal factors for empathy and to understand theheterogeneity in the treatment effects. One of our main findingsis that empathy is contextual and, while reader personality playsa significant role in evoking empathy, the mood of the readerprior to reading the story and linguistic story features have animpact as well. The results of our analyses can be used to helppeople create content that others care about and to help themcommunicate more effectively.

Emotions Extracted from Text vs. True Emotionsxe2x80x93An Empirical Evaluation in SE Context Emotion awareness research in SE context has been growing in recent years. Currently, researchers often rely on textual communication records to extract emotion states using natural language processing techniques. However, how well these extracted emotion states reflect peopleu0027s real emotions has not been thoroughly investigated. In this paper, we report a multilevel, longitudinal empirical study with 82 individual members in 27 project teams. We collected their selfreported retrospective emotion states on a weekly basis during their yearlong projects and also extracted corresponding emotions from the textual communication records. We then model and compare the dynamics of these two types of emotions using multiple statistical and time series analysis methods. Our analyses yield a rich set of findings. The most important one is that the dynamics of emotions extracted using textbased algorithms often do not well reflect the dynamics of selfreported retrospective emotions. Besides, the extracted emotions match selfreported retrospective emotions better at the teamlevel. Our results also suggest that individual personalities and the teamu0027s emotion display norms significantly impact the matchmismatch. Our results should warn the research community about the limitations and challenges of applying textbased emotion recognition tools in SE research.