International Gynaecological Cancer Society (IGCS) 2020 Annual Global Meeting: Twitter activity analysis

2021 ◽  
pp. ijgc-2021-002781
Author(s):  
Geetu Prakash Bhandoria ◽  
Navya Nair ◽  
Sadie Esme Fleur Jones ◽  
Ane Gerda Eriksson ◽  
Heng-Cheng Hsu ◽  
...  
Keyword(s):  
Cluster Algorithm ◽  
Gynecological Cancer ◽  
Application Programming Interface ◽  
Activity Analysis ◽  
Cancer Society ◽  
Application Programming ◽  
Media Platform ◽  
Twitter Users ◽  
Twitter Activity ◽  
Programming Interface

ObjectivesTwitter is the most frequently used social media platform by healthcare practitioners, at medical conferences. This study aimed to analyze Twitter conversations during the virtual International Gynecological Cancer Society 2020 conference to understand the interactions between Twitter users related to the conference.MethodsTweets using the hashtag ‘#IGCS2020’ were searched using the Twitter Search Application Programming Interface (API) during the period 10–13 September 2020. NodeXL Pro was used to retrieve data. The Clauset-Newman-Moore cluster algorithm clustered users into different groups or ‘clusters’ based on how users interacted.ResultsThere were 2009 registrants for the virtual IGCS 2020 conference. The total number of users within the network was 168, and there were 880 edges connecting users. Five types of edges were identified as follows: ‘replies to’ (n=18), ‘mentions’ (n=221), ‘mentions in retweets’ (n=375), retweets (n=198), and tweets (n=68). The most influential account was that of the IGCS account itself (@IGCSociety). The overall network shape resembled a community where distinct groups formed within the network. Our current analyses demonstrated that less than 10% of the total members interacted on Twitter.ConclusionThis study identified the most influential Twitter users within the ‘#IGCS2020’ community. he results also confirmed the community network shape of the #IGCS2020 hashtag and found that the most frequent co-related words were ‘ovarian’ and ‘cancer’ (n=39).

Nonprobability Sampling and Twitter

2017 ◽  
Vol 36 (2) ◽  
pp. 195-211 ◽  
Author(s):  
Patrick Rafail
Keyword(s):  
Social Sciences ◽  
Application Programming Interface ◽  
Sampling Strategies ◽  
Twitter Data ◽  
Large Databases ◽  
The Social ◽  
Application Programming ◽  
Data Source ◽  
Twitter Activity ◽  
Programming Interface

Twitter data are widely used in the social sciences. The Twitter Application Programming Interface (API) allows researchers to build large databases of user activity efficiently. Despite the potential of Twitter as a data source, less attention has been paid to issues of sampling, and in particular, the implications of different sampling strategies on overall data quality. This research proposes a set of conceptual distinctions between four types of populations that emerge when analyzing Twitter data and suggests sampling strategies that facilitate more comprehensive data collection from the Twitter API. Using three applications drawn from large databases of Twitter activity, this research also compares the results from the proposed sampling strategies, which provide defensible representations of the population of activity, to those collected with more frequently used hashtag samples. The results suggest that hashtag samples misrepresent important aspects of Twitter activity and may lead researchers to erroneous conclusions.

scholarly journals Emotional Analysis of Twitter Posts During the First Phase of the COVID-19 Pandemic in Greece: Infoveillance Study

10.2196/27741 ◽  
2021 ◽  
Vol 5 (9) ◽  
pp. e27741
Author(s):  
Styliani Geronikolou ◽  
George Drosatos ◽  
George Chrousos
Keyword(s):  
Public Health ◽  
Negative Emotions ◽  
Application Programming Interface ◽  
Inclusion Criteria ◽  
Basic Emotions ◽  
Health Measures ◽  
Health Strategy ◽  
Application Programming ◽  
Twitter Users ◽  
Programming Interface

Background The effectiveness of public health measures depends upon a community’s compliance as well as on its positive or negative emotions. Objective The purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece. Methods The period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted. Results The most frequent emotion that was detected in the tweets was “surprise” at the emerging contagion, while the imposed isolation resulted mostly in “anger” (odds ratio 2.108, 95% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked “flight or fight” or “fear versus anger” response to the contagion. Conclusions The findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance.

scholarly journals Emotional Analysis of Twitter Posts During the First Phase of the COVID-19 Pandemic in Greece: Infoveillance Study (Preprint)

2021 ◽  
Author(s):  
Styliani Geronikolou ◽  
George Drosatos ◽  
George Chrousos
Keyword(s):  
Public Health ◽  
Negative Emotions ◽  
Application Programming Interface ◽  
Inclusion Criteria ◽  
Basic Emotions ◽  
Health Measures ◽  
Health Strategy ◽  
Application Programming ◽  
Twitter Users ◽  
Programming Interface

BACKGROUND The effectiveness of public health measures depends upon a community’s compliance as well as on its positive or negative emotions. OBJECTIVE The purpose of this study was to perform an analysis of the expressed emotions in English tweets by Greek Twitter users during the first phase of the COVID-19 pandemic in Greece. METHODS The period of this study was from January 25, 2020 to June 30, 2020. Data collection was performed by using appropriate search words with the filter-streaming application programming interface of Twitter. The emotional analysis of the tweets that satisfied the inclusion criteria was achieved using a deep learning approach that performs better by utilizing recurrent neural networks on sequences of characters. Emotional epidemiology tools such as the 6 basic emotions, that is, joy, sadness, disgust, fear, surprise, and anger based on the Paul Ekman classification were adopted. RESULTS The most frequent emotion that was detected in the tweets was “surprise” at the emerging contagion, while the imposed isolation resulted mostly in “anger” (odds ratio 2.108, 95% CI 0.986-4.506). Although the Greeks felt rather safe during the first phase of the COVID-19 pandemic, their positive and negative emotions reflected a masked “flight or fight” or “fear versus anger” response to the contagion. CONCLUSIONS The findings of our study show that emotional analysis emerges as a valid tool for epidemiology evaluations, design, and public health strategy and surveillance.

scholarly journals Modern tools and current trends in web-development

2021 ◽  
Vol 24 (2) ◽  
pp. 978
Author(s):  
Debani Prasad Mishra ◽  
Kshirod Kumar Rout ◽  
Surender Reddy Salkuti
Keyword(s):  
Application Programming Interface ◽  
Social Media Platform ◽  
Current Trends ◽  
Front End ◽  
Responsive Design ◽  
Application Programming ◽  
Media Platform ◽  
Web App ◽  
Cascading Style Sheets ◽  
Programming Interface

In this paper, a social media platform like LinkedIn and Facebook is made using MongoDB as a database. This paper aims to touch all the modern tools required to make an efficient web app, keeping in mind both the customer satisfaction and the ease for the developers to make their web designs, front-end and back-end. In this application, a user could make an account, add or delete details of their profile, education, and experience fields. The users could post, also comment and even like a post of other users. A monolithic architectural approach is used for simplicity in maintaining the database. Postman application programming interface (API) was used to check the working of the back-end. Git, Github, and Heroku were used to deploy the website. Node package manager (NPM) packages like bcrypt and validator are used to encrypt passwords and to validate a user during login. Media queries are used in cascading style sheets (CSS) to achieve a responsive design. Therefore, the users could view the website through a mobile phone, i-pad and also a personal computer (PC), maintaining the readability and design across all these devices.

Social Media as Mirror of Society

2017 ◽  
pp. 128-141
Author(s):  
Amir Manzoor
Keyword(s):  
Social Media ◽  
Application Programming Interface ◽  
Social Media Data ◽  
Social Media Platform ◽  
Application Programming ◽  
Media Platform ◽  
Use Of Social Media ◽  
Media Data ◽  
Programming Interface ◽  
Specific Reason

Over the last decade, social media use has gained much attention of scholarly researchers. One specific reason of this interest is the use of social media for communication; a trend that is gaining tremendous popularity. Every social media platform has developed its own set of application programming interface (API). Through these APIs, the data available on a particular social media platform can be accessed. However, the data available is limited and it is difficult to ascertain the possible conclusions that can be drawn about society on the basis of this data. This chapter explores the ways social researchers and scientists can use social media data to support their research and analysis.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

Sign in / Sign up

Export Citation Format

Share Document