Global Trends in Plastic Surgery on Social Media: Analysis of 2 Million Posts

Abstract Background Plastic Surgeons and patients increasingly use social media. Despite evidence implicating its importance in Plastic Surgery, the large amount of data has made social media difficult to study. Objectives This study seeks to provide a comprehensive assessment of Plastic Surgery content throughout the world using techniques for analyzing large-scale data. Methods ‘#PlasticSurgery’ was used to search public Instagram posts. Metadata was collected from posts between December 2018 and August 2020. In addition to descriptive analysis, we created two instruments to characterize textual data: a multi-lingual dictionary of procedural hashtags and a rule-based text classification model to categorize the source of the post. Results Plastic Surgery content yielded more than 2 million posts, 369 million likes, and 6 billion views globally over the 21-month study. The United States had the most posts of 182 countries studied (26.8%, 566,206). Various other regions had substantial presence including Istanbul, Turkey, which led all cities (4.8%, 102,208). Our classification model achieved high accuracy (94.9%) and strong agreement with independent raters (κ= 0.88). Providers accounted for 40% of all posts (847,356) and included Physician (28%), Plastic Surgery (9%), Advanced-Practice-Practitioners and Nurses (1.6%), Facial Plastics (1.3%), and Oculoplastics (0.2%). Content between Plastics and non-Plastics groups demonstrated high textual similarity, and only 1.4% of posts had a verified source. Conclusions Plastic Surgery content has immense global reach in social media. Textual similarity between groups coupled with the lack of an effective verification mechanism presents challenges in discerning the source and veracity of information.

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Commentary on: Global Trends in Plastic Surgery on Social Media: Analysis of 2 Million Posts

Aesthetic Surgery Journal ◽

10.1093/asj/sjab167 ◽

2021 ◽

Author(s):

Sanam Zahedi

Keyword(s):

Social Media ◽

Plastic Surgery ◽

Media Analysis ◽

Social Media Analysis ◽

Global Trends

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Using Social Media to Analyze Public Concerns and Policy Responses to COVID-19 in Hong Kong

ACM Transactions on Management Information Systems ◽

10.1145/3460124 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1-20

Author(s):

Guanqing Liang ◽

Jingxin Zhao ◽

Helena Yan Ping Lau ◽

Cane Wing-Ki Leung

Keyword(s):

Social Media ◽

Hong Kong ◽

Large Scale ◽

Early Stage ◽

Media Analysis ◽

Classification Model ◽

Public Discussion ◽

The Public ◽

Evaluation Phase ◽

Key Issues

The outbreak of COVID-19 has caused huge economic and societal disruptions. To fight against the coronavirus, it is critical for policymakers to take swift and effective actions. In this article, we take Hong Kong as a case study, aiming to leverage social media data to support policymakers’ policy-making activities in different phases. First, in the agenda setting phase, we facilitate policymakers to identify key issues to be addressed during COVID-19. In particular, we design a novel epidemic awareness index to continuously monitor public discussion hotness of COVID-19 based on large-scale data collected from social media platforms. Then we identify the key issues by analyzing the posts and comments of the extensively discussed topics. Second, in the policy evaluation phase, we enable policymakers to conduct real-time evaluation of anti-epidemic policies. Specifically, we develop an accurate Cantonese sentiment classification model to measure the public satisfaction with anti-epidemic policies and propose a keyphrase extraction technique to further extract public opinions. To the best of our knowledge, this is the first work which conducts a large-scale social media analysis of COVID-19 in Hong Kong. The analytical results reveal some interesting findings: (1) there is a very low correlation between the number of confirmed cases and the public discussion hotness of COVID-19. The major public concern in the early stage is the shortage of anti-epidemic items. (2) The top-3 anti-epidemic measures with the greatest public satisfaction are daily press conference on COVID-19 updates, border closure, and social distancing rules.

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Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020 (Preprint)

10.2196/preprints.26655 ◽

2020 ◽

Author(s):

Daisy Massey ◽

Chenxi Huang ◽

Yuan Lu ◽

Alina Cohen ◽

Yahel Oren ◽

...

Keyword(s):

Public Health ◽

Social Media ◽

Language Processing ◽

Daily Life ◽

The United States ◽

Media Analysis ◽

Prevention Measures ◽

Cross Sectional ◽

Health Measures ◽

The Us

BACKGROUND The coronavirus disease 2019 (COVID-19) has continued to spread in the US and globally. Closely monitoring public engagement and perception of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs. OBJECTIVE To measure the public’s behaviors and perceptions regarding COVID-19 and its daily life effects during the recent 5 months of the pandemic. METHODS Natural language processing (NLP) algorithms were used to identify COVID-19 related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged, and sensitivity and specificity were both calculated to validate the classification of posts. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the US. RESULTS The final sample size included 9,065,733 posts, 70% of which were sourced from the US. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the US beginning in October. Additionally, counter to reports from March and April, discussion was more focused on daily life topics (69%), compared with COVID-19 in general (37%) and COVID-19 public health measures (20%). CONCLUSIONS There was a decline in COVID-19-related social media discussion sourced mainly from the US, even as COVID-19 cases in the US have increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures until a vaccine is widely available to the public.

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Abstract 14682: Impact of Atrial Fibrillation in Aortic Stenosis Patients: Insights From the United States Readmissions Database

Circulation ◽

10.1161/circ.142.suppl_3.14682 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Shashank Shekhar ◽

Anas M Saad ◽

Toshiaki Isogai ◽

Mohamed M Gad ◽

Keerat Ahuja ◽

...

Keyword(s):

Atrial Fibrillation ◽

Aortic Stenosis ◽

Hospital Mortality ◽

Large Scale ◽

International Classification Of Diseases ◽

The United States ◽

Large Scale Data ◽

Classification Of Diseases

Introduction: Even though atrial fibrillation (AF) is present in >30% of patients with aortic stenosis (AS), it is not typically included in the decision-making algorithm for the timing or need for aortic valve replacement (AVR), either by transcatheter (TAVR) or surgical (SAVR) approaches. Large scale data on how AF affects outcomes of AS patients remain scarce. Methods: From the Nationwide Readmissions Database (NRD), we retrospectively identified AS patients aged ≥18years, with and without AF admitted between January and June in 2016 and 2017 (to allow for a six month follow up), using the International Classification of Diseases-10 th revision codes. Multivariable logistic regression was performed to examine the predictors of in-hospital mortality during index hospitalization. In-hospital complications and 6 month in-hospital mortality during any readmission after being discharged alive were compared in patients with and without AF, for patients undergoing TAVR, SAVR or no-AVR. Results: We identified 403,089 AS patients, of which 41% had AF. Patients with AF were older (median age in years: 83 vs. 79) and were more frequently females (52% vs. 48%; p<0.001). Table summarizes outcomes of AS patients with and without AF. TAVR in patients with AF was associated with higher in-hospital mortality and follow-up mortality as compared to patients without AF. Although AF did not influence in-hospital mortality in SAVR population, follow-up mortality was also significantly higher after SAVR in patients with AF compared to patients without AF. For patients not undergoing AVR, in-hospital and follow-up mortality were higher in AF population compared to no AF and was higher than patients undergoing AVR (Table). Conclusions: AF is associated with worse outcomes in patients with AS irrespective of treatment (TAVR, SAVR or no-AVR). More studies are needed to understand the implications of AF in AS population and whether earlier treatment of AS in patients with AF can improve outcomes.

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Computational Journalism Analysis on Young Adults' Body Images and Attitudes Toward Plastic Surgery

International Journal of e-Collaboration ◽

10.4018/ijec.2021100107 ◽

2021 ◽

Vol 17 (4) ◽

pp. 89-108

Author(s):

Chutisant Kerdvibulvech ◽

Pattaragun Wanishwattana

Keyword(s):

Social Media ◽

Young Adults ◽

Plastic Surgery ◽

Online Survey ◽

Media Analysis ◽

Social Media Analysis ◽

Body Images ◽

Negative Body Image ◽

Self Perceptions ◽

The Impact

Computational journalism, especially social media analysis, is a very popular field in computational science. This study was conducted to explore and analyze the impact of the intensity of the exposure to social media on young Thai adults' body images and attitudes toward plastic surgery. The purposive sampling method was used for choosing 250 young Thai men and women aged 21 to 40 who used Facebook and/or Instagram on a regular basis. Online survey questionnaires were posted on Facebook for one month to achieve the results. It was found that young Thai adults frequently and heavily used both social media. Having appearance pressure from and repeated social comparison with idealistic media images, a considerable number of participants displayed more negative self-perceptions and engaged in appearance-changing strategies through increased appearance investment. The results showed that the more these young adults were exposed to social media, the more they were likely to develop a negative body image of themselves, which later caused their attitude toward plastic surgery to be positive.

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How advocacy affects Twitter migraine conversations: A pilot cross-sectional survey of Northeast American “migraine” landscape on Twitter from May to June 2020

Cephalalgia Reports ◽

10.1177/2515816320972085 ◽

2020 ◽

Vol 3 ◽

pp. 251581632097208

Author(s):

Pengfei Zhang ◽

Santosh Bhaskarabhatla

Keyword(s):

United States ◽

Social Media ◽

Network Analysis ◽

Network Model ◽

Large Scale ◽

The United States ◽

Closeness Centrality ◽

Control Group ◽

Annual Conference ◽

Sources Of Information

Background: Twitter is a leading microblogging platform, with over 126 million daily active users as of 2019, which allows for large-scale analysis of tweets related to migraine. June 2020 encompassed the National Migraine and Headache Awareness Month in the United States and the American Headache Society’s virtual annual conference, which offer opportunities for us to study online migraine advocacy. Objective: We aim to study the content of individual tweets about migraine, as well as study patterns of other topics that were discussed in those tweets. In addition, we aim to study the sources of information that people reference within their tweets. Thirdly, we want to study how online awareness and advocacy movements shape these conversations about migraine. Methods: We designed a Twitter robot that records all unique public tweets containing the word “migraine” from May 8th, 2020 to June 23rd, 2020, within a 400 km radius of New Brunswick, New Jersey, United States. We built two network analysis models, one for the months of May 2020 and June 2020. The model for the month of May served as a control group for the model for the month of June, the Migraine Awareness Month. Our network model was developed with the following rule: if two hashtag topics co-exist in a single tweet, they are considered nodes connected by an edge in our network model. We then determine the top 30 most important hashtags in the month of May and June through applications of degree, between-ness, and closeness centrality. We also generated highly connected subgraphs (HCS) to categorize clusters of conversations within each of our models. Finally, we tally the websites referenced by these tweets during each month and categorized these websites according to the HCS subgroups. Results: Migraine advocacy related tweets are more popular in June when compared to May as judged by degree and closeness centrality measurements. They remained unchanged when judged by between-ness centralities. The HCS algorithm categorizes the hashtags into a large single dominant conversation in both months. In each of the months, advocacy related hashtags are apart of each of the dominant conversation. There are more hashtag topics as well as more unique websites referenced in the dominant conversation in June than in May. In addition, there are many smaller subgroups of migraine-related hashtags, and in each of these subgroups, there are a maximum of two websites referenced. Conclusion: We find a network analysis approach to be fruitful in the area of migraine social media research. Migraine advocacy tweets on Twitter not only rise in popularity during migraine awareness month but also may potentially bring in more diverse sources of online references into the Twitter migraine conversation. The smaller subgroups we identified suggest that there are marginalized conversations referencing a limited number of websites, creating a possibility of an “echo chamber” phenomenon. These subgroups provide an opportunity for targeted migraine advocacy. Our study therefore highlights the success as well as potential opportunities for social media advocacy on Twitter.

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Analyzing the startup ecosystem of India: a Twitter analytics perspective

Journal of Advances in Management Research ◽

10.1108/jamr-08-2019-0164 ◽

2019 ◽

Vol 17 (2) ◽

pp. 262-281 ◽

Cited By ~ 12

Author(s):

Shiwangi Singh ◽

Akshay Chauhan ◽

Sanjay Dhir

Keyword(s):

Social Media ◽

Topic Modeling ◽

Latent Dirichlet Allocation ◽

Descriptive Analysis ◽

Relevant Information ◽

Media Analysis ◽

Social Media Analytics ◽

Content Type ◽

Twitter Analytics ◽

Bayes Algorithm

Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.

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New Approach of Measuring Human Personality Traits Using Ontology-Based Model from Social Media Data

Information ◽

10.3390/info12100413 ◽

2021 ◽

Vol 12 (10) ◽

pp. 413

Author(s):

Andry Alamsyah ◽

Nidya Dudija ◽

Sri Widiyanesti

Keyword(s):

Social Media ◽

Large Scale ◽

Public Involvement ◽

Real Life ◽

Big Five Personality ◽

Sorting Algorithm ◽

Personality Measurement ◽

Human Personality ◽

Textual Data ◽

N Gram

Human online activities leave digital traces that provide a perfect opportunity to understand their behavior better. Social media is an excellent place to spark conversations or state opinions. Thus, it generates large-scale textual data. In this paper, we harness those data to support the effort of personality measurement. Our first contribution is to develop the Big Five personality trait-based model to detect human personalities from their textual data in the Indonesian language. The model uses an ontology approach instead of the more famous machine learning model. The former better captures the meaning and intention of phrases and words in the domain of human personality. The legacy and more thorough ways to assess nature are by doing interviews or by giving questionnaires. Still, there are many real-life applications where we need to possess an alternative method, which is cheaper and faster than the legacy methodology to select individuals based on their personality. The second contribution is to support the model implementation by building a personality measurement platform. We use two distinct features for the model: an n-gram sorting algorithm to parse the textual data and a crowdsourcing mechanism that facilitates public involvement contributing to the ontology corpus addition and filtering.

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