Department of Communication and Media

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Now showing 1 - 5 of 16
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    Digital Reputation Management in American Cancer Hospitals: A Proposed Model
    (Polish Communication Association, 2022-03) Aguerrebere, Pablo Medina; Medina, Eva; Pacanowski, Toni Gonzalez
    Cancer patients face complicated situations from an emotional, social and physical perspective. Hospitals help them through implementing corporate communication initiatives based on social media platforms. This win-win relationship allows hospitals to reinforce their brand reputation. This paper aims to better understand how cancer hospitals manage social media platforms for enhancing their brand as well as their relationships with stakeholders. To do that, we carried out a literature review about corporate communication in health organizations, as well as a content analysis about how the top 100 American cancer hospitals managed their corporate website as well as their corporate profile on Facebook, Twitter and YouTube for branding initiatives. Finally, we proposed the Reb Model for Branding Cancer Hospitals. We concluded that thanks to social media, cancer hospitals can reinforce their brand because these platforms allow them to promote human values, improve their internal processes and become a true source of scientific information. © 2022 Polish Communication Association. All rights reserved.
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    Promoting Health Brands through Social Media. A Quantitative Analysis about the World’s Best Cancer Hospitals
    (Servicio de Publicaciones de la Universidad de Navarra, 2022) Aguerrebere, Pablo Medina; Medina, Eva; Pacanowski, Toni González
    Cancer hospitals enforce different initiatives to accelerate digital transformation, such as mobile health or artificial intelligence. Nevertheless, some health professionals are not willing to adopt these technologies. In order to change some employees’ perspectives, these hospitals resort to social media platforms. This paper aims to evaluate how the worlds’ best cancer hospitals manage social media platforms, as well as their corporate website, with the aim of disseminating brand-related content and reinforce their reputation. Therefore, we reviewed literature on cancer hospitals’ corporate communication strategies, brand, social media platforms and online patient communities. We then resorted to 48 quantitative indicators to analyze how the 200 best cancer hospitals in the world managed Facebook, Twitter and YouTube, as well as their corporate website, for branding purposes. In order to identify the 200 best hospitals, we explored the World’s Best Specialized Hospitals 2021, an annual ranking published by Newsweek and Statista. The 48 indicators covered different elements concerning the hospitals’ identity and communication activities, as well as patient engagement on social media platforms. Our quantitative analysis proved that most cancer hospitals had a corporate website (70.5%) as well as a profile on Facebook (74%), Twitter (74.5%) and YouTube (67.5%). Nevertheless, most of them did not respect the 48 key performance indicators. Finally, we proposed three main conclusions: a) cancer hospitals should establish a Corporate Communication Department employing different experts in communication, health and big data; b) they should promote an integrated corporate communication approach; and c) they should implement brand ambassador programmes. © 2022 Communication & Society ISSN 0214-0039 E ISSN 2386-7876.
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    Compressed feature vector-based effective object recognition model in detection of COVID-19
    (Elsevier B.V., 2022-02) Chen, Chao; Mao, Jinhong; Liu, Xinzhi; Tan, Yi; Abaido, Ghada M; Alsayed, Hamdy
    To better understand the structure of the COVID-19, and to improve the recognition speed, an effective recognition model based on compressed feature vector is proposed. Object recognition plays an important role in computer vison aera. To improve the recognition accuracy, most recent approaches always adopt a set of complicated hand-craft feature vectors and build the complex classifiers. Although such approaches achieve the favourable performance on recognition accuracy, they are inefficient. To raise the recognition speed without decreasing the accuracy loss, this paper proposed an efficient recognition modeltrained witha kind of compressed feature vectors. Firstly, we propose a kind of compressed feature vector based on the theory of compressive sensing. A sparse matrix is adopted to compress feature vector from very high dimensions to very low dimensions, which reduces the computation complexity and saves enough information for model training and predicting. Moreover, to improve the inference efficiency during the classification stage, an efficient recognition model is built by a novel optimization approach, which reduces the support vectors of kernel-support vector machine (kernel SVM). The SVM model is established with whether the subject is infected with the COVID-19 as the dependent variable, and the age, gender, nationality, and other factors as independent variables. The proposed approach iteratively builds a compact set of the support vectors from the original kernel SVM, and then the new generated model achieves approximate recognition accuracy with the original kernel SVM. Additionally, with the reduction of support vectors, the recognition time of new generated is greatly improved. Finally, the COVID-19 patients have specific epidemiological characteristics, and the SVM recognition model has strong fitting ability. From the extensive experimental results conducted on two datasets, the proposed object recognition model achieves favourable performance not only on recognition accuracy but also on recognition speed. © 2021
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    Government failure or irresponsible residents? Framing Detroit’s water shutoffs before and during the COVID-19 pandemic
    (SAGE Publications Ltd, 2022-03) Mesmer, Kelsey; Frazier, Darryl; Burgess, Scott
    This content analysis of news stories about the Detroit water shutoffs sought to understand how the ongoing water crisis is framed in local Detroit newspapers—as a human rights issue, or in relation to the city’s financial burden. Using a deductive framing approach, we paid special attention to the frames used within stories and whether articles contained context related to the water shutoffs, specifically about health implications. We paid particular attention to how the focus on health implications changed in response to the COVID-19 pandemic in 2020. Results showed that stories about the water shutoffs often included an economic consequences and responsibility frame which put the blame for the water shutoffs on the city’s government and simultaneously called for the city to step up and fix the problem. Very few news articles focused on the human element of the story, with only a small fraction of the stories including the voices of residents living with no water or focusing on the health implications for those without running water in their homes. These findings led us to make strategic recommendations for reporters covering the water shutoffs in Detroit and similar areas. © 2022 NOND of AEJMC.
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    Research on a reference signal optimisation algorithm for indoor Bluetooth positioning
    (Sciendo, 2021) Luo, Heng; Hu, Xinyu; Zou, Youmin; Jing, Xinglei; Song, Chengyi; Ni, Qidong
    GPS has a sharp performance decline in terms of accuracy indoors due to the complex building structure. A combined algorithm, targeting at received signal strength indication (RSSI) calibration optimisation, depending on deep neural network training via input vector Γ and the target output vector ψ, termed reference signal optimisation algorithm (RSOA) is proposed to improve the positioning accuracy in the indoor Bluetooth positioning networks. Experimental results show that the relative error of the proposed RSOA between the estimated results and the measured ones can reach as low as 0.2%, and the absolute errors can be reduced to 0.13 m at most within 10 m. © 2021 Heng Luo et al., published by Sciendo 2021.