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Second, we explore distinct groups that contribute to online discourses about misinformation. Using the theoretical lenses of socially curated flow and networked gatekeeping frameworks, we address the following three aims: First, we identify emergent opinion leaders in misinformation-related conversations on social media. information identified as false) spreads widely and quickly on social media – a space where crowds of ordinary citizens can become leading voices – during a crisis when information is in short supply.
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The research contributes to the academic literature on the impact of the dark side of personalities on masstige marketing and technology adoption propensity. We analyzed three market leader brands in the smartphone industry – Apple (iPhone), Samsung, and Huawei.
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The relationship is explored considering the technological propensity of consumers. The interest of our research is to investigate the masstige perception of smartphone brands, through the lens of the antecedents of consumers' behavior and the dark side of their personalities. Smartphones can be considered as objects able to extend the self of consumers and their status. The continuous evolution of technologies pushes consumers to face their technological adoption propensity. The purchase of luxury brands aims to satisfy utilitarian and hedonic customers’ motives, based on their personality traits, even the dark ones (such as narcissism, Machiavellianism, and psychopathy). Masstige marketing represents the democratization of luxury to middle-class consumers.
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The study can contribute to increasing the adoption of OPPC and reducing the burden of offline medical resources. This positive relationship was partially mediated by the decrease in the expectancy violation of PCC in OPPC scenarios. Nevertheless, patients’ satisfaction with OPPC significantly increased as the frequency of OPPC usage increased ( β = 0.209, p < 0.001). However, patient satisfaction with OPPC was lower than that in offline medical encounters ( M = 3.75, SD = 0.80), and patients suffered a higher expectancy violation of PCC in OPPC scenarios ( M = 0.45, SD = 0.76) than in offline medical encounters ( M = 0.27, SD = 0.69). The 471 qualified participants reported high satisfaction with OPPC (mean = 3.63, standard deviation = 0.81). This study investigates the role of patient expectancy and the expectancy violation of patient-centered communication (PCC) in patient satisfaction in emerging OPPC scenarios by integrating the concepts of PCC and expectancy violation theory (EVT).Īn online survey was conducted in October 2019 among Chinese respondents who experienced OPPC and offline medical services. However, its penetration rate remains low, and the underlying mechanisms of patient satisfaction with OPPC are underexamined. Online patient–provider communication (OPPC) has become an alternative approach to seek medical advice and contact health professionals. Our results demonstrate that epidemiological models fit rumor propagation well, while graph-based features lead to more effective classification of rumors the combination of epidemiological and graph-based features leads to improved performance. Evaluation is performed with a Gradient Boosting classifier on two benchmark fake rumor detection datasets. Using these features, we evaluate them for fake rumor detection with 3 configurations: (i) using only epidemiological features, (ii) using only graph-based features, and (iii) using the combination of epidemiological and graph-based features. Specifically, we extract epidemiological features exploiting epidemiological models for spreading false rumors furthermore, we extract graph-based features from the graph structure of the information cascade of the social graph. Specifically, we analyze temporal and structural characteristics of information flow in the social networks and we evaluate the importance and effect of two different types of features in the detection process of fake rumors. In this work, we focus on social media analyzing characteristics that are independent of the text language (language-independent) and social context (location-independent) and common to most social media, not only Twitter as mostly analyzed in the literature. Detection and identification of misinformation and fake news is a complex problem that intersects several disciplines, ranging from sociology to computer science and mathematics.