In order to gain a deeper understanding of social media, we analyzed relevant abstracts that were downloaded from the Web of Science (WOS) database. Our search termsFootnote 1 yielded a total of 13,177 records, out of which 12,597 unique abstracts were obtained. The analysis of these records was undertaken in two steps. First, we used VOSviewer (Van Eck and Waltman 2011) to perform a co-citation analysis of first authors in the downloaded corpus. VOSviewer allows visualization of similarities in publications and authors through an examination of bibliometric networks. Furthermore, we used VOSviewer to analyze words derived from titles and abstracts. Second, we used Latent Dirichlet Allocation (LDA) (see Blei 2012) to extract key thematic areas latent in the literature on social media. Further details about these analyses and results are presented in section 3.
Relevant articles were then identified and downloaded from each of the target journals by going through their archives. Specifically, all volumes and issues published in these journals between 1997 and 2017 were considered in our analysis. Articles, research notes, introductions, research commentaries, and editorial overviews relevant to social media were downloaded and numbered to prepare an APA style reference list. The first literature search resulted in 181 articles that had some relevance to the social media domain. A closer examination of individual abstracts and full articles led to the elimination of 49 irrelevant articles, thus giving us a total of 132 articles pertinent to the domain of interest (i.e., social media).
Chen et al. (2011) investigate the effect of moderation and reveal that the commentators generate high quality content to build a stronger reputation. Wei et al. (2013) developed a multi-collaborative filtering trust network algorithm for Web 2.0 with improved accuracy for filtering information based on user preferences and trusted peer users. Luo and Zhang (2013) refer to user-generated reviews and recommendations as consumer buzz to find that advocacy and consumer attitude can impact firm value. Hildebrand et al. (2013) use data from a European car manufacturer allowing self-designed products to reveal that feedback from other community members lessens uniqueness whilst increasing dissatisfaction. Centeno et al. (2015) address the skewed reputation rankings problem in movie ratings by suggesting the use of comparative user opinions. Ma et al. (2013) analyse data from Yelp to test bias in online reviews and find that frequent and longer reviews successfully combat such biases. Lukyanenko et al. (2014) demonstrate that participants tend to provide accurate information in classifying a phenomenon at a general level, and higher accuracy where they are allowed free form data. Shi and Whinston (2013) explore the possible impact of friend check-ins on social media, and find it has no positive effect in generating new user visits.
Notes: A 48-year-old male presented at the emergency department with pain in the entire abdomen, but concentrating in the right lower quadrant. Palpation of the entire abdomen was extremely painful and laboratory values showed elevated inflammatory parameters (leucocyte count 17.9 and C-reactive protein 43). Upright abdominal radiography showed no abnormalities. Computed tomography of the abdomen showed free intraperitoneal air and signs of appendicitis acuta. Patient underwent an emergency laparotomy, which confirmed the diagnosis of perforated appendicitis acuta.
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The full Chromecast functionality available in the Met Opera on Demand Android app is only accessible after you have logged into the app as a subscriber or free 7-day trial user. The Cast icon will only appear automatically in the video player within the app when you are logged in. New users can sign-up for a free 7-day trial on the Met website, and then login to the app to unlock all of the Chromecast functionality.
Met Opera on Demand is now available on Samsung Smart TVs with the new Met Opera on Demand Samsung Smart TV app. The app is free to add to your Samsung. To download the new app to your TV, open the Smart Hub, select \"Samsung Apps\", then select \"Categories\" > \"Videos\" and look for Met Opera on Demand within this section. The app will only appear in the \"Samsung Apps\" section of the Smart Hub on your TV if it is available to download on that specific model.
The app itself is free to download from Samsung Apps, accessible from the Smart Hub button on your Samsung remote control. However, a Met Opera on Demand subscription is required for full access to the app and its content.
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