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Figure A.3.: Distributed speech dynamic graphs SSS (a), GOT (b), and GTC (c).

Figure A.3.: Distributed speech dynamic graphs SSS (a), GOT (b), and GTC (c).

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The tools of social network analysis offer a promising framework for studying fictional texts and the relational activity of the characters therein. The goal of this paper is to offer both a conceptual refinement of the project of measuring the centrality of characters within narratives using network tools, as well as the proposal of a novel measur...

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... Total influence is not dominated by apparent factors. This is unlike, for instance, in Jones et al. (36), where a time-dependent speaking measure is proposed to assess the positions of characters in networks of movie dialogs. Transferring that approach to the AFL, we would determine coaches' influence from the temporal network of coaches "speaking" to future coaches they are currently coaching. ...
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We study the lineage network of coaches in the Australian Football League (AFL) using a novel process of influence propagation through temporal social networks. Coaching and being coached are considered major opportunities for learning, and the vast majority of AFL coaches are former AFL players. We, therefore, establish influence via two antagonistic components: as players, future coaches are influenced by their coaches, and later liberate themselves from these influences while being coaches themselves. Influence thus propagates through time-dependent player–coach relationships, and we obtain a ranking of coaches by their aggregated influence on others. In addition to being based on an explicit process, we argue that the ranking has face validity, because it indeed favors highly reputed coaches, and is not determined by temporal or activity indicators such as the starting year of a coaching career, its length, or the number of future coaches coached.
... Similarly, Bal (2017) suggests that the more a character does something (such as speaking), the more attention they receive. Verbal dialogue can be used to construct the network (Jones, 2020;Jones et al., 2020). Character network analysis has been used in both film (Jones, 2020;Jones et al., 2020;Park et al., 2012;Weng et al., 2009) and television series (Bost et al., 2018;Tan et al., 2017) and provides the methods to successfully investigate the use of language in television as recommended by Bednarek (2018). ...
... Verbal dialogue can be used to construct the network (Jones, 2020;Jones et al., 2020). Character network analysis has been used in both film (Jones, 2020;Jones et al., 2020;Park et al., 2012;Weng et al., 2009) and television series (Bost et al., 2018;Tan et al., 2017) and provides the methods to successfully investigate the use of language in television as recommended by Bednarek (2018). ...
... The weight refers to how many lines of dialogue were spoken by a nurse or doctor. Centrality measures were used to interpret the results (see Jones et al., 2020;Park et al., 2012). Degree centrality is the total number of links coming into and out of a node. ...
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Aim The aim of this study was to describe how nurses' dialogue compares with that of doctors in the Australian reality television program Emergency and to explain how this dialogue and the use of narration and direct‐to‐camera monologues contribute to the portrayal of nurses. The final aim was to outline how these findings can inform the actions of nurses, nursing organizations and writers and journalists. Design A descriptive study conducted in 2021 and 2022. Methods Character network analysis was used to describe the verbal interaction of nurses and doctors and identify major, minor and background characters. Narration and direct‐to‐camera monologues were analysed through frequency counts. Cultivation theory was used to interpret the results. Results Fifty‐four characters were identified, with 19 of these being nurses. Doctors spoke 87.9% of the dialogue. Most of the dialogue (43%) was monologue by doctors directly to the camera. All major characters were doctors, and only one nurse was a minor character. The mean number of mentions by the narrator of a nurse per episode was 4, and 30 for a doctor. Conclusion The portrayal of nurses in Emergency is inaccurate and the production methods are used to privilege the role of doctors. This finding comes after decades of research showing inaccurate images of nursing in television. Drawing on recent Australian inquiries and the challenges experienced by mental health and aged care nursing, several approaches for collaborative action to improve these images are suggested. Impact This is the first study of the portrayal of nursing in reality television. These results suggest that much effort is still required even in contemporary reality television to accurately reflect the work and contribution of nurses. It is the shared responsibility of individual nurses, nursing organizations and writers and journalists to accurately portray nurses in the media. No patient or public contribution This network analysis of a postproduction television program did not allow patient or public involvement in the design or analysis.
... Determining the type of the oncological network under consideration boils down to the problem of differences between centralized and decentralized interorganizational networks [17][18][19][20][21][22][23][24][25]. For each voivodeship coordinator, there is a central unit coordinating the activities of a centralized subnet of entities. ...
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The global pandemic triggered by the SARS-CoV-2 virus has caused marked changes in the economic landscape in essentially every branch of the economy. The pandemic has disordered lives across all countries in the world and also affected the public sector in interesting ways. Our empirical research, of which selected elements are presented in this paper, was conducted under pandemic conditions. This paper aims to identify the relationships between selected distinguishing features of the oncological interorganizational network (exchange, engagement, reciprocity) and determine their effectiveness under the conditions of the economic crisis caused by the COVID-19 pandemic. A side thread, which concludes the article, is the introduction of the category “economic virus” into management terminology; i.e., a set of factors causing economic crises with a microbiological genesis. Of particular importance are considerations regarding the uncertainty of the length and depth of the health crisis-related economic effects in financial markets and corporate decision making.
... A recently introduced framework for the scalable examination of character representations adopts computational tools from network science. In this line of research, narratives are typically computationally parsed, abstracted, and parsimoniously encoded in a mathematical object called a graph (Bollobás, 2001), where the set of parts (network nodes) frequently represent a story's characters and connections (network edges) between characters denote some quality or strength of their (social) relation (e.g., Hopp et al., 2020;Jones et al., 2020;Kagan et al., 2020;Skowron et al., 2016). ...
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In the Hollywood film industry, racial minorities remain underrepresented. Characters from racially underrepresented groups receive less screen time, fewer central story positions, and frequently inherit plotlines, motivations, and actions that are primarily driven by White characters. Currently, there are no clearly defined, standardized, and scalable metrics for taking stock of racial minorities’ cinematographic representation. In this paper, we combine methodological tools from computer vision and network science to develop a content analytic framework for identifying visual and structural racial biases in film productions. We apply our approach on a set of 89 popular, full-length movies, demonstrating that this method provides a scalable examination of racial inclusion in film production and predicts movie performance. We integrate our method into larger theoretical discussions on audiences’ perception of racial minorities and illuminate future research trajectories towards the computational assessment of racial biases in audiovisual narratives.
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A character network is a graph extracted from a narrative in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fiction (e.g., novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties that make these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches and the most promising perspectives.
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Full-text available
A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.
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Identifying and characterizing the dynamics of modern tv series subplots is an open problem. One way is to study the underlying social network of interactions between the characters. Standard dynamic network extraction methods rely on temporal integration, either over the whole considered period, or as a sequence of several time-slices. However, they turn out to be inappropriate in the case of tv series, because the scenes shown onscreen alternatively focus on parallel storylines, and do not necessarily respect a traditional chronology. In this article, we introduce Narrative Smoothing, a novel network extraction method taking advantage of the plot properties to solve some of their limitations. We apply our method to a corpus of 3 popular series, and compare it to both standard approaches. Narrative smoothing leads to more relevant observations when it comes to the characterization of the protagonists and their relationships, confirming its appropriateness to model the intertwined storylines constituting the plots.