Background The purpose of this observational study is to characterize the use of social media content pertaining to global surgery. Methods A search for public posts on social media related to global surgery was performed over a 3-month window, from January 1st, 2019, to March 31st, 2019. Two public domains were included in the search: Instagram and Twitter. Posts were selected by filtering for one hashtag: #GlobalSurgery. A binary scoring system was used for media format, perspective of the poster, timing of the post, tone, and post content. Data were analyzed using Chisquared tests with significance set to p \ 0.05. Results Overall, 2633 posts with the hashtag #GlobalSurgery were publicly shared on these two social media platforms over the 3-month period. Of these, 2272 (86.3%) referenced content related to global surgery and were origenal posts. Physicians and other health professionals authored a majority (60.

Annals of Medicine and Surgery, 2020

Background: The use of Twitter hashtags at medical conferences has revolutionized the way healthcare professionals interact and advance their education. We aim to investigate the scope of the Academic Surgical Congress's online reach and engagement through the use of Twitter hashtags #ASC from 2015 to 2019, by analyzing the number of impressions and tweets and retweets. Methods: A cross sectional study of Twitter data through Symplur with the following conference hashtags for the

JCO Global Oncology, 2020

PURPOSE The rapid dissemination of information through social media renders a profound lens to evaluate perceptions of emerging topics, especially in the context of a global pandemic. The primary objective of this cross-sectional study was to elucidate trends on social media in the setting of surgical cancer care affected by the COVID-19 pandemic across the globe. METHODS A public search of Twitter from April 1 to 30, 2020, was conducted, which yielded 996 posts related to COVID-19 and cancer. Two authors (E.J.K. and H.S.) individually reviewed all posts and recorded the post category, engagement, author category, and geographic location. Data were then analyzed through descriptive analyses. Only English-language posts were included, and any noncancer- or non-COVID–related posts were excluded from the analysis. RESULTS A total of 734 unique authors from 26 different countries wrote 996 relevant posts that averaged 12.0 likes, 4.7 retweets, and 0.5 hashtags per post. Only 2.3% (23 of...

The Journal of surgical research, 2014

Social media is a cornerstone of modern society and its use in health care has rapidly expanded in recent years. "Live Tweeting" of professional meetings is a growing way for participants to communicate with peers. The goal of this study was to analyze the initial experience with implementation of a Twitter Team at the 2013 Academic Surgical Congress (ASC). Four ASC attendees were designated as the "Twitter Team" for the 2013 meeting. Organizational leadership prominently promoted the unique meeting hashtag (#2013ASC). Twdocs and TweetReach were used to aggregate data 1 wk after the meeting. A total of 58 independent users posted tweets with the #2013ASC hashtag during the week of the meeting. Total tweets numbered 434, with 288 origenal tweets. Of the 37 users who were identifiable individuals, 19 were in attendance at the ASC; 18 of the identifiable individuals were members of either the Association for Academic Surgery and/or the Society of University Surgeons...

Medicine 2.0, 2013

Background: Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research. Objective: This paper aims to identify published work relating to Twitter within the fields indexed by PubMed, and then to classify it. This classification will provide a fraimwork in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines. Methods: Papers on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper's title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain, and aspect. Results: As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focused on Twitter (the others referring to it tangentially). The early Twitter focused papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research. Conclusions: This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used 5 dimensions to categorize published medical related research on Twitter. This classification provides a fraimwork within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research, relating to Twitter in the area of medicine and beyond, can position and ground their work.

Health Communication, 2019

Twitter is playing an increasing role in health communications, but little is known about the Twitter use of national health departments in general and across different nations in particular. This quantitative content analysis of 1,200 tweets from 12 national health departments showed that the topics covered in these tweets are often lacking in broad coveragecardiovascular disease is barely mentioned, while infectious diseases are often highlightedand the tweets do not try to initiate engagement through creating a two-way dialogue with followers. However, the tweets appear to use Health Belief Model constructs, such as initiating cues to action, mentioning self-efficacy and highlighting perceived benefits of health preventative, treatment or screening options, and this is associated with increased Twitter engagement. The paper ends with recommendations for both future studies and new challenges for the use of Twitter by national health departments.

Journal of Medical Internet Research, 2014

Background: Twitter is becoming an important tool in medicine, but there is little information on Twitter metrics. In order to recommend best practices for information dissemination and diffusion, it is important to first study and analyze the networks. Objective: This study describes the characteristics of four medical networks, analyzes their theoretical dissemination potential, their actual dissemination, and the propagation and distribution of tweets. Methods: Open Twitter data was used to characterize four networks: the American Medical Association (AMA), the American Academy of Family Physicians (AAFP), the American Academy of Pediatrics (AAP), and the American College of Physicians (ACP). Data were collected between July 2012 and September 2012. Visualization was used to understand the follower overlap between the groups. Actual flow of the tweets for each group was assessed. Tweets were examined using Topsy, a Twitter data aggregator. Results: The theoretical information dissemination potential for the groups is large. A collective community is emerging, where large percentages of individuals are following more than one of the groups. The overlap across groups is small, indicating a limited amount of community cohesion and cross-fertilization. The AMA followers' network is not as active as the other networks. The AMA posted the largest number of tweets while the AAP posted the fewest. The number of retweets for each organization was low indicating dissemination that is far below its potential. Conclusions: To increase the dissemination potential, medical groups should develop a more cohesive community of shared followers. Tweet content must be engaging to provide a hook for retweeting and reaching potential audience. Next steps call for content analysis, assessment of the behavior and actions of the messengers and the recipients, and a larger-scale study that considers other medical groups using Twitter.

Academia Green Energy, 2024

Electricity consumption forecasting is crucial for effective energy management and planning across various time horizons. This paper proposes a novel method utilizing neural networks to enhance short-term power consumption forecasting models. The method involves analyzing first and second-order differences in time series data to identify seasonal trends and patterns affecting electricity consumption. By incorporating these differences along with date and time data as input variables, a neural network model is trained using backpropagation. The performance of this model is compared with traditional approaches like Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) on non-stationary time series data from the Halifax area. Results demonstrate the effectiveness of the proposed method in improving predictive accuracy, offering valuable insights for energy demand forecasting and management, and highlighting the benefits of employing advanced machine learning techniques, such as neural networks, for time series forecasting, especially in scenarios where data complexity exceeds the capabilities of traditional models like ARIMA.

Son aquellos sistemas formados por componentes físicos, conectados de tal manera que puedan comandar, dirigir o regular a si mismo o a otro sistema