Blockchain technology awareness on social media: Insights from twitter analytics

2021 ◽  
pp. 100416
Author(s):  
Emna Mnif ◽  
Khaireddine Mouakhar ◽  
Anis Jarboui
Keyword(s):  
Social Media ◽  
Blockchain Technology ◽  
Twitter Analytics

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

Application of social media in the education of the general surgeon. @Cirbosque, breaking the paradigm

2020 ◽  
Vol 31 (1) ◽  
pp. 15-20 ◽  
Author(s):  
LF Cabrera Vargas ◽  
G Herrera ◽  
A Mendoza Zuchini ◽  
M Pedraza ◽  
S Sánchez ◽  
...  
Keyword(s):  
Social Media ◽  
General Surgeon ◽  
Redes Sociales ◽  
Twitter Analytics ◽  
Educación Virtual

Resumen En el campo de la medicina las redes sociales han ganado poco a poco terreno y hoy en día juegan un rol importante en el aprendizaje y la enseñanza de conocimientos que se pueden trasmitir de inmediato y de forma masiva. El objetivo de este artículo es mostrar la experiencia colombiana en el uso de las redes sociales para crear aprendizaje quirúrgico significativo, con liderazgo y tutoría global. Se llevó a cabo un estudio descriptivo retrospectivo desde la creación de nuestras redes sociales en Twitter, 22 de febrero al 22 de agosto de 2019, evaluando las siguientes variables: número de tweets académicos, número de seguidores, impresiones, visitas y menciones. Desde la creación de nuestra red social Twitter @Cirbosque para realizar educación virtual quirúrgica a través de redes sociales con el fin de generar un aprendizaje significativo en nuestros seguidores, en solo seis meses del proyecto, seguimos a 62 cuentas, hemos realizado hasta la fecha 5.025 tweets académicos, con un crecimiento del 77,1% mensual, con 2.203 seguidores, con un crecimiento del 426 seguidores mensual, 1.090.000 impresiones, con un crecimiento del 56% mensual, 13.500 mil visitas, con un crecimiento del 28,9% mensual y 2.028 menciones, con un crecimiento del 88,3% mensual. Aunque la evidencia aún es insuficiente para garantizar que la educación que se hace a través de redes sociales y de @Cirbosque sea eficiente, el impacto que ha tenido esta iniciativa en Twitter es apreciada por muchos cirujanos a nivel mundial incluyendo a grandes maestros referentes en cada uno de los temas que se han tratado, de la misma forma, la cantidad de participantes en las diversas discusiones planteadas día a día y con un incremento en todos los indicadores de impacto según Twitter Analytics, se puede deducir que el mensaje educativo está teniendo un efecto positivo y está llegando a miles de personas a nivel mundial.

scholarly journals Beyond the hashtag: describing and understanding the full impact of the #BJSConnect tweet chat May 2019

BJS Open ◽  
2020 ◽  
Vol 5 (2) ◽  
Author(s):  
G Mackenzie ◽  
R Grossman ◽  
J Mayol
Keyword(s):  
Social Media ◽  
Clinical Practice ◽  
Third Party ◽  
Internet Browser ◽  
Twitter Analytics ◽  
Social Media Tool

Abstract Background Twitter engagement between surgeons provides opportunities for international discussion of research and clinical practice. Understanding how surgical tweet chats work is important at a time when increasing reliance is being placed on virtual engagement because of the COVID-19 pandemic. Methods Individual tweets from the May 2019 #BJSConnect tweet chat were extracted using NodeXL, complemented by Twitter searches in an internet browser to identify responses that had not used the hashtag. Aggregate estimates of tweet views were obtained from a third-party social media tool (Twitonomy) and compared with official Twitter Analytics measurements. Results In total 37 Twitter accounts posted 248 tweets or replies relating to the tweet chat. A further 110 accounts disseminated the tweets via retweeting. Only 58.5 per cent of these tweets and 35 per cent of the tweeters were identified through a search for the #BJSConnect hashtag. The rest were identified by searching for replies (61), quoting tweets (20), and posts by @BJSurgery that used the hashtag but did not appear in the Twitter search (22). Studying all tweets revealed complex branching discussions that went beyond the discussed paper’s findings. Third-party estimates of potential reach of the tweet chat were greatly exaggerated. Conclusion Understanding the extent of the discussion generated by the #BJSConnect tweet chat required looking beyond the hashtag to identify replies and other responses, which was time-consuming. Estimates of reach using a third-party tool were unreliable.

Social media games: insights from Twitter analytics

2020 ◽  
Vol 16 (1) ◽  
pp. 34 ◽  
Author(s):  
Rajesh R. Pai ◽  
Sreejith Alathur
Keyword(s):  
Social Media ◽  
Twitter Analytics

Analyzing the startup ecosystem of India: a Twitter analytics perspective

2019 ◽  
Vol 17 (2) ◽  
pp. 262-281 ◽  
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.

scholarly journals Do policymakers use social media for policy design? A Twitter analytics approach

2021 ◽  
Vol 25 ◽  
Author(s):  
Ashish Kumar Rathore ◽  
Dayashankar Maurya ◽  
Amit Kumar Srivastava
Keyword(s):  
Social Media ◽  
Policy Design ◽  
Public Policies ◽  
Limited Attention ◽  
Social Media Analytics ◽  
The Public ◽  
Information Awareness ◽  
Twitter Analytics ◽  
Social Media Platforms ◽  
Use Of Social Media

Social media has been used widely for communicating information, awareness, and promote public policies by government agencies. However, limited attention has been paid to the use of social media in improving the design of public policies. This paper explores to what extent citizens' responses/opinions expressed on social media platforms contribute to policy design.  The paper analyzes discussion about the 'Ayushman Bharat' scheme on Twitter through social media analytics techniques (e.g., content analytics) and then traces the change in policy design over two years.  To validate findings from Twitter data, and assess the evolution in policy design, we conducted in-depth interviews with experts and extensive document analysis. The paper reveals that consistently similar issues were raised by the experts in the past as well as by the citizens in the current scheme. However, over the period, the policy design has not changed significantly. Therefore, despite a strong social media presence, its optimum use to improve policy effectiveness is yet to be achieved. The paper contributes by exploring the role social media can play in the public policy process and policy design in developing countries' contexts and identifies gaps in existing social media strategies of public agencies.

Diffusion of blockchain technology

2019 ◽  
Vol 32 (5) ◽  
pp. 735-757 ◽  
Author(s):  
Purva Grover ◽  
Arpan Kumar Kar ◽  
Marijn Janssen
Keyword(s):  
Social Media ◽  
Decision Process ◽  
Manufacturing Industry ◽  
Content Type ◽  
Academic Literature ◽  
Blockchain Technology ◽  
Data Source ◽  
Single Data ◽  
Almost All ◽  
Media Data

Purpose Although blockchain is often discussed, its actual diffusion seems to be varying for different industries. The purpose of this paper is to explore the blockchain technology diffusion in different industries through a combination of academic literature and social media (Twitter). Design/methodology/approach The insights derived from the academic literature and social media have been used to classify industries into five stages of the innovation-decision process, namely, knowledge, persuasion, decision, implementation and confirmation (Rogers, 1995). Findings Blockchain is found to be diffused in almost all industries, but the level of diffusion varies. The analysis highlights that manufacturing industry is at the knowledge stage. Further public administration is at persuasion stage. Subsequently, transportation, communications, electric, gas and sanitary services and trading industry had reached to the decision stage. Then, services industries have reached to implementation stage while finance, insurance and real estate industries are the innovators of blockchain technologies and have reached the confirmation stage of innovation-decision process. Practical implications Actual implementations of blockchain technology are still in its infancy stage for most of the industries. The findings suggest that specific industries are developing specific blockchain applications. Originality/value To the best of the authors’ knowledge this is the first study which is using social media data for investigating the diffusion of blockchain in industries. The results show that the combination of Twitter and academic literature analysis gives better insights into diffusion than a single data source.

scholarly journals Understanding the Emotional Intelligence Discourse on Social Media: Insights from the Analysis of Twitter

2021 ◽  
Vol 9 (4) ◽  
pp. 56
Author(s):  
Shardul Shankar ◽  
Vijayshri Tewari
Keyword(s):  
Social Media ◽  
Emotional Intelligence ◽  
Language Processing ◽  
Information Diffusion ◽  
Information Dissemination ◽  
Research And Practice ◽  
Network Analytics ◽  
Twitter Analytics ◽  
Social Media Platforms ◽  
Use Of Social Media

Social networks have created an information diffusion corpus that provides users with an environment where they can express their views, form a community, and discuss topics of similar or dissimilar interests. Even though there has been an increasingly rising demand for conducting an emotional analysis of the users on social media platforms, the field of emotional intelligence (EI) has been rather slow in exploiting the enormous potential that social media can play in the research and practice of the framework. This study, thus, tried to examine the role that the microblogging platform Twitter plays in enhancing the understanding of the EI community by building on the Twitter Analytics framework of Natural Language Processing to further develop the insights of EI research and practice. An analysis was conducted on 53,361 tweets extracted using the hashtag emotional intelligence through descriptive analytics (DA), content analytics (CA), and network analytics (NA). The findings indicated that emotional intelligence tweets are used mostly by speakers, psychologists (or other medical professionals), and business organizations, among others. They use it for information dissemination, communication with stakeholders, and hiring. These tweets carry strong positive sentiments and sparse connectedness. The findings present insights into the use of social media for understanding emotional intelligence.

scholarly journals Use of Social Media as a Platform for Education and Support for People With Diabetes During a Global Pandemic

2021 ◽  
pp. 193229682110548
Author(s):  
Rebecca L. Thomas ◽  
Victoria Alabraba ◽  
Sam Barnard ◽  
Hannah Beba ◽  
Julie Brake ◽  
...  
Keyword(s):  
Social Media ◽  
Fundamental Aspect ◽  
Audience Engagement ◽  
Global Pandemic ◽  
Twitter Analytics ◽  
Twitter Account ◽  
Media Platform ◽  
Use Of Social Media ◽  
Virtual Conference ◽  
Video Presentations

Background: Patient education is a fundamental aspect of self-management of diabetes. The aim of this study was to understand whether a social media platform is a viable method to deliver education to people with diabetes and understand if people would engage and interact with it. Methods: Education sessions were provided via 3 platforms in a variety of formats. “Tweetorials” and quizzes were delivered on the diabetes101 Twitter account, a virtual conference via Zoom and video presentations uploaded to YouTube. Audience engagement during and after the sessions were analyzed using social media metrics including impressions and engagement rate using Twitter analytics, Tweepsmap, and YouTube Studio. Results: A total of 22 “tweetorial” sessions and 5 quizzes with a total of 151 polls (both in tweetorial and quiz sessions) receiving a total of 21,269 votes took place. Overall, the 1-h tweetorial sessions gained 1,821,088 impressions with an engagement rate of 6.3%. The sessions received a total of 2,341 retweets, 2,467 replies and 10,060 likes. The quiz days included 113 polls receiving 16,069 votes. The conference covered 8 topics and was attended live by over 100 people on the day. The video presentations on YouTube have received a total of 2,916 views with a watch time of 281 h and 8,847 impressions. Conclusion: Despite the limitations of social media, it can be harnessed to provide relevant reliable information and education about diabetes allowing people the time and space to learn at their own pace.

Sign in / Sign up

Export Citation Format

Share Document