scholarly journals Twitter, Telepractice, and the COVID-19 Pandemic: A Social Media Content Analysis

2021 ◽  
pp. 1-11
Author(s):  
Kristen Weidner ◽  
Joneen Lowman ◽  
Anne Fleischer ◽  
Kyle Kosik ◽  
Peyton Goodbread ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Technology Implementation ◽  
Scale Up ◽  
Media Content Analysis ◽  
Data Set ◽  
Implementation Framework ◽  
Health Crisis ◽  
Wide Scale ◽  
Twitter Users

Purpose Telepractice was extensively utilized during the COVID-19 pandemic. Little is known about issues experienced during the wide-scale rollout of a service delivery model that was novel to many. Social media research is a way to unobtrusively analyze public communication, including during a health crisis. We investigated the characteristics of tweets about telepractice through the lens of an established health technology implementation framework. Results can help guide efforts to support and sustain telehealth beyond the pandemic context. Method We retrieved a historical Twitter data set containing tweets about telepractice from the early months of the pandemic. Tweets were analyzed using a concurrent mixed-methods content analysis design informed by the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Results Approximately 2,200 Twitter posts were retrieved, and 820 original tweets were analyzed qualitatively. Volume of tweets about telepractice increased in the early months of the pandemic. The largest group of Twitter users tweeting about telepractice was a group of clinical professionals. Tweet content reflected many, but not all, domains of the NASSS framework. Conclusions Twitter posting about telepractice increased during the pandemic. Although many tweets represented topics expected in technology implementation, some represented phenomena were potentially unique to speech-language pathology. Certain technology implementation topics, notably sustainability, were not found in the data. Implications for future telepractice implementation and further research are discussed.

Social network analysis of Twitter data from Pakistan during COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Syeda Hina Batool ◽  
Wasim Ahmed ◽  
Khalid Mahmood ◽  
Ashraf Sharif
Keyword(s):  
Social Media ◽  
Message Framing ◽  
Data Set ◽  
Health Crisis ◽  
Content Type ◽  
News Stories ◽  
Social Media Platforms ◽  
Twitter Users ◽  
Use Of Social Media

Purpose The use of social media has increased during the COVID-19 pandemic. Social media platforms provide opportunities to share news, ideas and personal stories. Twitter is used by citizens in Pakistan to respond and comment on emerging news stories and events. However, it is not known whether Twitter played a positive or negative role in spreading updates and preventive messages during the COVID-19 pandemic. The purpose of this study is to analyse content from Twitter during the pandemic. Design/methodology/approach NodeXL was used to retrieve data using the keyword وائرس کورونا (written in Urdu and which translates to Coronavirus). The first data set (Case Study 1) was based on 10,284 Twitter users from the end of March. The second data set (Case Study 2) was based on 10,644 Twitter users from the start of April. The theoretical lens of effective message framing was used to classify the most retweeted content on Twitter. Findings Twitter was used for personal and professional projections and included certain tweets included political motives even during the unfolding health crisis. There appeared to be very few successful attempts to use Twitter as a tool for health awareness and risk communication. The empirical findings indicate that the most retweeted messages were gain-framed and can be classified as personal, informative and political in nature. Originality/value The present study provides insights likely to be of interest to researchers, health organizations, citizens, government and politicians that are interested in making more effective use of social media for the purposes of health promotion. The authors also provide novel insights into the key topics of discussions, websites and hashtags used by Pakistani Twitter users during the COVID-19 pandemic.

Comparing Representation of Depression on Reddit and Twitter Social Media Platforms (Preprint)

2020 ◽  
Author(s):  
Ethan Kaji ◽  
Maggie Bushman
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Content Analysis ◽  
Relative Frequency ◽  
Social Activity ◽  
Social Media Platforms ◽  
Dsm Iv ◽  
Twitter Users

BACKGROUND Adolescents with depression often turn to social media to express their feelings, for support, and for educational purposes. Little is known about how Reddit, a forum-based platform, compares to Twitter, a newsfeed platform, when it comes to content surrounding depression. OBJECTIVE The purpose of this study is to identify differences between Reddit and Twitter concerning how depression is discussed and represented online. METHODS A content analysis of Reddit posts and Twitter posts, using r/depression and #depression, identified signs of depression using the DSM-IV criteria. Other youth-related topics, including School, Family, and Social Activity, and the presence of medical or promotional content were also coded for. Relative frequency of each code was then compared between platforms as well as the average DSM-IV score for each platform. RESULTS A total of 102 posts were included in this study, with 53 Reddit posts and 49 Twitter posts. Findings suggest that Reddit has more content with signs of depression with 92% than Twitter with 24%. 28.3% of Reddit posts included medical content compared to Twitter with 18.4%. 53.1% of Twitter posts had promotional content while Reddit posts didn’t contain promotional content. CONCLUSIONS Users with depression seem more willing to discuss their mental health on the subreddit r/depression than on Twitter. Twitter users also use #depression with a wider variety of topics, not all of which actually involve a case of depression.

Twitter Users’ Displays of Affect in the Global Warming Debate

2021 ◽  
pp. 004728162110078
Author(s):  
Shanna Cameron ◽  
Alexandra Russell ◽  
Luke Brake ◽  
Katherine Fredlund ◽  
Angela Morris
Keyword(s):  
Climate Change ◽  
Social Media ◽  
Global Warming ◽  
Content Analysis ◽  
Technical Communication ◽  
Research Team ◽  
Future Research ◽  
Climate Change Research ◽  
Quantitative Content ◽  
Twitter Users

This article engages with recent discussions in the field of technical communication that call for climate change research that moves beyond the believer/denier dichotomy. For this study, our research team coded 900 tweets about climate change and global warming for different emotions in order to understand how Twitter users rely on affect rhetorically. Our findings use quantitative content analysis to challenge current assumptions about writing and affect on social media, and our results indicate a number of arenas for future research on affect, global warming, and rhetoric.

scholarly journals Examining Perceived and Projected Destination Image: A Social Media Content Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3354
Author(s):  
Wei Sun ◽  
Shoulian Tang ◽  
Fang Liu
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Destination Image ◽  
Media Content ◽  
Media Content Analysis ◽  
Poor Understanding ◽  
Practical Implications

Destination image has been extensively studied in tourism and marketing, but the questions surrounding the discrepancy between the projected (perceptions from the National Tourism Organizations) and perceived destination image (perceptions from tourists) as well as how the discrepancy may influence sustainable experience remain unclear. Poor understanding of the discrepancy may cause tourist confusion and misuse of resources. The aim of this study is to empirically investigate if the perceived (by tourists) and projected (by NTOs) destination image are significantly different in both cognitive and affective aspects. Through a comprehensive social media content analysis of the NTO-generated and tourist-generated-contents (TGC), the current study identifies numerous gaps between the projected and perceived destination image, which offers some important theoretical and practical implications on destination management and marketing.

Marijuana and Glaucoma: A Social Media Content Analysis

2020 ◽  
Author(s):  
Jing (Sasha) Jia ◽  
Nikki Mehran ◽  
Robert Purgert ◽  
Qiang (Ed) Zhang ◽  
Daniel Lee ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Media Content ◽  
Media Content Analysis

scholarly journals Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253300
Author(s):  
Md Shoaib Ahmed ◽  
Tanjim Taharat Aurpa ◽  
Md Musfique Anwar
Keyword(s):  
Social Media ◽  
Public Perceptions ◽  
Intervention Strategies ◽  
User Generated Content ◽  
Time Intervals ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Significant Public Health ◽  
Trending Topics ◽  
Twitter Users

COVID-19 caused a significant public health crisis worldwide and triggered some other issues such as economic crisis, job cuts, mental anxiety, etc. This pandemic plies across the world and involves many people not only through the infection but also agitation, stress, fret, fear, repugnance, and poignancy. During this time, social media involvement and interaction increase dynamically and share one’s viewpoint and aspects under those mentioned health crises. From user-generated content on social media, we can analyze the public’s thoughts and sentiments on health status, concerns, panic, and awareness related to COVID-19, which can ultimately assist in developing health intervention strategies and design effective campaigns based on public perceptions. In this work, we scrutinize the users’ sentiment in different time intervals to assist in trending topics in Twitter on the COVID-19 tweets dataset. We also find out the sentimental clusters from the sentiment categories. With the help of comprehensive sentiment dynamics, we investigate different experimental results that exhibit different multifariousness in social media engagement and communication in the pandemic period.

Social Media Content Analysis

10.1142/10535 ◽  
2017 ◽  
Author(s):  
Kam-Fai Wong ◽  
Wei Gao ◽  
Ruifeng Xu ◽  
Wenjie Li
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Media Content ◽  
Media Content Analysis

scholarly journals Incidental Effects of Automated Retweeting: An Exploratory Network Perspective on Bot Activity During Sri Lanka’s Presidential Election in 2015

2017 ◽  
Vol 37 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Chamil Rathnayake ◽  
Wayne Buente
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Sri Lankan ◽  
Eigenvector Centrality ◽  
Data Set ◽  
Political Processes ◽  
User Expectations ◽  
Twitter Users ◽  
And Behavior

The role of automated or semiautomated social media accounts, commonly known as “bots,” in social and political processes has gained significant scholarly attention. The current body of research discusses how bots can be designed to achieve specific purposes as well as instances of unexpected negative outcomes of such use. We suggest that the interplay between social media affordances and user practices can result in incidental effects from automated agents. We examined a Twitter network data set with 1,782 nodes and 5,640 edges to demonstrate the engagement and outreach of a retweeting bot called Siripalabot that was popular among Sri Lankan Twitter users. The bot served the simple function of retweeting tweets with hashtags #SriLanka and #lk to its follower network. However, the co-use of #Sri Lanka and/or #lk with #PresPollSL, a hashtag used to discuss politics related to Sri Lanka’s presidential election in 2015, resulted in the bot incidentally amplifying the political voice of less engaged actors. The analysis demonstrated that the bot dominated the network in terms of engagement (out-degree) and the ability to connect distant clusters of actors (betweenness centrality) while more traditional actors, such as the main election candidates and news accounts, indicated more prestige (in-degree) and power (eigenvector centrality). We suggest that the study of automated agents should include designer intentions, the design and behavior of automated agents, user expectations, as well as unintended and incidental effects of interaction.

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