Information Diffusion Model in Twitter: A Systematic Literature Review

Information diffusion, information spread, and influencers are important concepts in many studies on social media, especially Twitter analytics. However, literature overviews on the information diffusion of Twitter analytics are sparse, especially on the use of continuous time Markov chain (CTMC). This paper examines the following topics: (1) the purposes of studies about information diffusion on Twitter, (2) the methods adopted to model information diffusion on Twitter, (3) the metrics applied, and (4) measures used to determine influencer rankings. We employed a systematic literature review (SLR) to explore the studies related to information diffusion on Twitter extracted from four digital libraries. In this paper, a two-stage analysis was conducted. First, we implemented a bibliometric analysis using VOSviewer and R-bibliometrix software. This approach was applied to select 204 papers after conducting a duplication check and assessing the inclusion–exclusion criteria. At this stage, we mapped the authors’ collaborative networks/collaborators and the evolution of research themes. Second, we analyzed the gap in research themes on the application of CTMC information diffusion on Twitter. Further filtering criteria were applied, and 34 papers were analyzed to identify the research objectives, methods, metrics, and measures used by each researcher. Nonhomogeneous CTMC has never been used in Twitter information diffusion modeling. This finding motivates us to further study nonhomogeneous CTMC as a modeling approach for Twitter information diffusion.

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

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.

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

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.

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

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.

Tapping public sentiments on Twitter for tourism insights: a study of famous Indian heritage sites

PurposeTwitter is the most widely used platform with an open network; hence, tourists often resort to Twitter to share their travel experiences, satisfaction/dissatisfaction and other opinions. This study is divided into two sections, first to provide a framework for understanding public sentiments through Twitter for tourism insights, second to provide real-time insights of three Indian heritage sites i.e., the Taj Mahal, Red Fort and Golden Temple by extracting 5,000 tweets each (n = 15,000) using Twitter API. Results are interpreted using NRC emotion lexicon and data visualisation using R.Design/methodology/approachThis study attempts to understand the public sentiment on three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple using a step-by-step approach, hence proposing a framework using Twitter analytics. Extensive use of various packages of R programming from the libraries has been done for various purposes such as extraction, processing and analysing the data from Twitter. A total of 15,000 tweets from January 2015 to January 2021 were collected of the three sites using different key words. An exploratory design and data visualisation technique has been used to interpret results.FindingsAfter data processing, 12,409 sentiments are extracted. Amongst the three tourists' spots, the greatest number of positive sentiments is for the Taj Mahal and Golden temple with approximately 25% each. While the most negative sentiment can be seen for the Red Fort (17%). Amongst the positive emotions, the maximum joy sentiment (12%) can be seen in the Golden Temple and trust (21%) in the Red Fort. In terms of negative emotions, fear (13%) can be seen in the Red fort. Overall, India's heritage sites have a positive sentiment (20%), which surpasses the negative sentiment (13%). And can be said that the overall polarity is towards positive.Originality/valueThis study provides a framework on how to use Twitter for tourism insights through text mining public sentiments and provides real- time insights from famous Indian heritage sites.

The Implementation and Effectiveness of PathElective.com

The COVID-19 pandemic put most in-person pathology electives on-hold as departments adapted to changes in education and patient care. To address the subsequent void in pathology education, we created a free, virtual, modular, and high-quality pathology elective website. Website traffic from June 1, 2020, to October 1, 2020, was monitored using the built-in analyses on Squarespace. Twitter engagement was analyzed using Twitter analytics and the Symplur Social Graph Score. A voluntary satisfaction survey was sent to all PathElective users and results were analyzed. During this time, the site saw 25 467 unique visitors, over 34 988 visits, 181 302 page views, and 4449 subscriptions from 99 countries. Countries with the highest traffic are the United States (14 682), India (5210), and the Philippines (2195). PathElective’s Twitter social graph score increased from 63.59 to 89.3 with the addition of 1637 followers. Data from surveyed users (n = 177) show most to be pathology residents (41%). Most subscribers (89%) are committed to a career in pathology. The majority heard of the website via Twitter (55%). Almost half of those surveyed engaged with the PathTwitter community on Twitter and of those who participated, 99% found that interaction useful. In all survey questions surrounding satisfaction and usefulness, a large majority of the users were either satisfied or very satisfied. PathElective is a novel pathology elective that offers a unique opportunity to educate medical students and residents from around the globe and demonstrates high effectiveness and satisfaction among users.

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

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.