There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

This document describes the rationale, the implementation and a preliminary evaluation of a semantic reasoning tool developed in the EU H2020 SPICE project to enhance the diversity of perspectives experienced by museum visitors. The tool, called DEGARI 2.0 for values, relies on the commonsense reasoning framework TCL, and exploits an ontological model formalizingthe Haidt's theory of moral values to associate museum items with combined values and emotions. Within a museum exhibition, this tool can suggest cultural items that are associated not only with the values of already experienced or preferred objects, but also with novel items with different value stances, opening the visit experience to more inclusive interpretations of cultural content. The system has been preliminarily tested, in the context of the SPICE project, on the collection of the Hecht Museum of Haifa.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. However, these sources of knowledge are fragmented and do not adequately represent non-Western writers and their works. In this paper we present The World Literature Knowledge Graph, a semantic resource containing 194,346 writers and 965,210 works, specifically designed for exploring facts about literary works and authors from different parts of the world. The knowledge graph integrates information about the reception of literary works gathered from 3 different communities of readers, aligned according to a single semantic model. The resource is accessible through an online visualization platform, which can be found at the following URL: https://literaturegraph.di.unito.it/. This platform has been rigorously tested and validated by $3$ distinct categories of experts who have found it to be highly beneficial for their respective work domains. These categories include teachers, researchers in the humanities, and professionals in the publishing industry. The feedback received from these experts confirms that they can effectively utilize the platform to enhance their work processes and achieve valuable outcomes.

Biographical event detection is a relevant task for the exploration and comparison of the ways in which people's lives are told and represented. In this sense, it may support several applications in digital humanities and in works aimed at exploring bias about minoritized groups. Despite that, there are no corpora and models specifically designed for this task. In this paper we fill this gap by presenting a new corpus annotated for biographical event detection. The corpus, which includes 20 Wikipedia biographies, was compared with five existing corpora to train a model for the biographical event detection task. The model was able to detect all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an F-score of 0.859. Finally, the model was used for performing an analysis of biases about women and non-Western people in Wikipedia biographies.

This extended abstract describes the preliminary quantitative and qualitative results coming from a therapeutic laboratory focused on the use of the Pepper robot to promote autonomies and functional acquisitions in highly functioning (Asperger) children with autism. The participants recruited were four highly functioning (Asperger) children, aged between 11 and 13 years. There have been in total 16 lab sessions, all recorded by a fixed camera, in addition to the Pepper's 2D cameras. Furthermore, trainees filled out evaluation forms provided by psychotherapists, noting the children autonomy's progress in a diary with the helping of rating scales [1]. These notes were then reworked to draw up shared reports, reflecting on the behavior's evolution and progress of the children meeting by meeting.

Digital media have enabled the access to unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.

The advent of computational tools and methods for investigating the way we respond to objects and situations has paved the way to a deeper understanding of the intricate relationship between emotions and artistic content. For example, [55] have studied how art affects emotional regulation by measuring the brain response through EEG: their research shows that, in comparison with photographs depicting real events, artworks determine stronger electro-physiological responses; in parallel, [19] argue that the emotional response to art - measured through facial muscle movements - is attenuated in art critics, and stronger in non-expert, thus showing the universality and spontaneity of this response. The association between art and emotions is even stronger when the artistic expression is conveyed by media, as in music and movies. For example, music has proven to be an effective tool for emotion regulation: as demonstrated by [54], music can induce specific emotional states in everyday situations, an effect which is sought for by the users and can be exploited to create effective affective recommender systems [3]. Finally, emotional engagement is of primary importance in narrative media, such as film and television, as extensively investigated by a line of research which draws from both film studies and emotion theories [47, 53]. As a consequence of the multifaceted, complex role played by emotions in the experience of art and media, the investigation of this phenomenon with computational tools has relied on a variety of models and methodologies, ranging from dimensional models, better suited to investigate physiological, continuous correlate of emotions [45, 57, 29], to categorical models, which lend themselves to inspecting the conscious level of emotional experience [39, 12, 4]. Dimensional models typically measure the emotional engagement along the arousal and hedonic axes, and are useful to study how the emotional response evolves over time.

The 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2017) was held at the Snowbird Ski and Summer Resort in Little Cottonwod Canyon in the Wasatch Range of the Rock Mountains near Salt Lake County, Utah. Along with the main conference presentations, the meeting included two tutorials, three workshops, and invited keynotes. This report summarizes the main conference. It also includes contributions from the organizers of the three workshops.

In this paper, we translate a model of value-based emo- tional agents into an architecture for narrative characters and we validate it in a narrative scenario. The advantage of using such model is that different moral behaviors can be obtained as a consequence of the emotional ap- praisal of moral values, a desirable feature for digital storytelling techniques.