Staying connected: Alzheimer's hashtags and opportunities for engagement and overcoming stigma

Alzheimer's disease (AD) is a terminal, neurodegenerative disease, and consequently is difficult to communicate about as it is stigmatized, and discussions are rife with misconceptions. By situating AD conversations in the sociocultural space of the opportunity model of presence during the end-of-life process, a framework developed illustrating the potential trajectory from living with illness through death and into bereavement, we examined networked discussions surrounding Alzheimer's related hashtags on Twitter (N = 132,803) between January 1st, 2010, and September 29th, 2021. Using the mixed-method approach of the Analysis of Topic Model Network (ANTMN) framework, results revealed 30 topics clustered into five distinct themes: promotion, education, action, "You aren't alone", and dementia. Results indicated that discussions surrounding World Alzheimer's Day focused on changing stigma and promoting engagement in difficult conversations. The frequency of themes over time remained relatively stable. By understanding how Twitter's online discourse may be used to overcome stigmatized topics, we can continue to tailor messages to reduce stigma and provide support for those who experience similar health issues.

Analysis of Worldwide Greenhouse and Carbon Monoxide Gas Emissions: Which Countries Exhibit a Special Pattern? A Closer Look via Twitter

There is significant global concern about the harmful effects of greenhouse gas and carbon monoxide emissions (deforestation, air pollution, global warming, etc.). The 2015 Paris Agreement on climate change aspires to reduce global warming by achieving a climate-neutral world. Research has been carried out to calculate and diminish the aforementioned emissions in waste, power industry, transport, building, in addition to other areas. The aim of this paper is to analyse the carbon and greenhouse gas emissions across countries around the globe in order to find patterns and correlate them to socio-economic indicators [gross national income (GNI), industrial production (IPI) and human development indexes (HDI)] as well as Twitter interactions regarding climate change. For this purpose, time series and socio-economic data have been downloaded from different repositories including EDGAR (Emissions Database for Global Atmospheric Research), World Bank and UNDP (United Nations Development Programme). Although classical clustering algorithms have already been used in the examination of some environmental issues, we use a non-parametric time series clustering method, which has been suggested in certain scientific literature as a more flexible approach, since any ad hoc parametric assumptions are required. The chosen socio-economic indicators have also demonstrated their relevance in pieces of research related to various fields. With respect to Twitter, which is one of the most popular social networks nowadays, significant analysis has also been performed on the basis of capturing citizens' perceptions on a multitude of matters. We found that several countries such as Brazil, India, China, Nigeria, Russia, United States, Spain, Andorra, Greece, and Qatar show differences in carbon and greenhouse gas emissions patterns. Besides, there does not seem to be a correlation between GNI, IPI and HDI as well as the above mentioned emissions ( correlation < 0.16 ) . Regarding Twitter interactions, a dissimilarity in the distribution of hashtags was detected between the aforementioned countries and the rest of the world. This research can help to identify countries in which more governmental measures are needed to reduce the type of emissions analysed in certain industrial sectors. In addition, it points out the topics related to climate change that seem to generate the most debate on Twitter for countries with an unusual pattern.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s41742-023-00510-4.

Role of Hashtags in Educating and Promoting the Field of Hand Surgery on Social Media

Purpose

This study identifies the hand surgery content being posted on Instagram, how hashtags are being used, and those posting to determine what is reaching the public.

Methods

Top hand surgery-related hashtags on Instagram from June 2020 to August 2020 were identified by searching "hand surgery" and sorting by relevance. Hashtags were quantified by number and qualitatively assessed. Posts without a clear relationship were excluded. Hashtags relevant to hand surgery were analyzed by educational merit, medical specialty, patient or nonpatient, and demographics.

Results

The top 25 hashtags contained 325,400 posts. The 3 hashtags with the highest number of posts were #carpaltunnel (64,700), #handsurgery (50,500), and #handtherapy (48,300). Most posts were educational (53.2%). Nonsurgical fields (66.5%) posted the most, followed by orthopedic (25.9%), and plastic hand surgeons (7.7%). Nonpatients (68.8%) posted more than patients. The top 3 languages of the posts were English (67.7%), Russian (9.4%), and Spanish (7.1%). However, when looking at the hashtags with more than half of the posts being made by hand surgeons, we observed that most (62.9%) of the posts were noneducational in content.

Conclusions

Instagram posts on hand surgery topics are largely posted by nonexperts and are educational in content. There is a major opportunity for hand surgeons to educate and market effectively using hand surgery-related hashtags. Given the number of hand surgeries performed annually, one would hope to see more representation by hand surgeons on social media. More active participation and provision of educational content by specialists is warranted.

Clinical relevance

There is an opportunity for hand surgeons to educate those searching upper extremity conditions and seeking out expertise in a domain where information is largely driven by nonsurgeons and the quality of information is not vetted. This study identifies the need for more hand surgeon involvement to expand knowledge and communication efforts within the specialty and with the public through the evolving world of social media.

The body image "problem" on social media: Novel directions for the field

Social media is a highly popular and interactive environment which has many benefits, but research has shown that its use is also associated with body image concerns. Leaked internal survey findings in September 2021 from Instagram provide some support for negative impacts on body image of teenage girls especially. In this short communication, we discuss the complexities of moderating social media platforms which aim to protect users from potentially harmful body image focused content. Several strategies have been employed and we specifically examine the moderation of pro-eating disorder hashtags (e.g., #thinspiration) and hiding of "Like" counts on Instagram in particular. We find that both are quick technical "fixes" that do not effectively address these long-standing and complex issues on social media. We provide suggestions for novel avenues of research, including that body image researchers are excellently positioned to propose specific suggestions for content moderation strategies that may actually be effective. Social media companies know that they have a major problem on their hands, as the leaked findings demonstrate, and so they may be more receptive than they have ever been to novel research findings from our community.

Annotated dataset of history-related tweets

In this article, we present a dataset containing history-related content obtained from social media. It contains hashtags and tweets that include these hashtags, as well as the results of third party tools applied to the tweets that include extracted entities, years, and url categories, and the categories for the history-related hashtags we used to crawl the tweets. We collected the tweets from Twitter official API using hashtag-based crawling. The crawling process had been performed from March 2016 to July 2018. During the crawling, we applied a bootstrapping approach which is an iterative process of collecting tweets using a small set of seed hashtags, and a manual inspection of newly acquired hashtags that co-occur with the seed hashtags to include those they are related to history. Finally, we collected 147 history-related hashtags and 2,370,252 tweets. We then defined 6 categories for the collected hashtags after their manual investigation. The presented dataset could be useful for further analysis on how people refer to history in Twitter, for collecting new history-related tweets, for training classifiers to detect history-related tweets, or for further investigations of the proposed hashtag categories.

Is a Picture Really Worth More than a Thousand Words? Which Instagram Post Types Elicit the Best Response for Radiology Education

Since its 2010 launch, Instagram has rapidly risen to become one of the leading social media in the world, with more than one billion monthly users. Within the field of radiology, perhaps because of the image-weighted nature of the platform, several prominent organizations host Instagram accounts, including the Radiological Society of North America, American Roentgen Ray Society, American College of Radiology, and the American Board of Radiology. We started our own Instagram account in January 2016 because of the popularity of this social media platform among medical students. Our website contains over 260,000 images and is aimed at educating physicians, technologists, and nurses around the world on radiology, with an emphasis on body computed tomography. Given our varied selection of radiology resources, we were curious as to which posts perform best on Instagram and set out how to best characterize the interest in these posts. Our results point to an approach that can aid radiology educators who use social media, particularly Instagram, in producing popular content.

#workfromhome: how multi-level marketers enact and subvert federal language policy for profit

This paper analyzes how multi-level marketing companies (MLMs), via direct selling through electronic commerce (e-commerce) and social media, enact and evade federal language policy to maximize profits. Here we describe the federal language policies that govern this type of e-commerce, and in particular, the language policies of the Federal Trade Commission, which dictate what can and cannot be communicated by MLM companies and their contractors. We then illustrate how these federal language policies are enacted, and at times subverted, for financial gain during the COVID-19 economic and health crisis which rendered many people vulnerable. We draw on the discourse analysis of public documents, MLM insider sources via the first author, and over 100,000 public Instagram posts published by MLM independent contractors collected with the third-party Instagram data extraction tool, Phantombuster. We find that MLM independent contractors, although varying widely with respect to their enactment of federal and corporate policy, frequently reference COVID-19 implicitly or explicitly, a practice prohibited by federal policy. We demonstrate that quantitative and qualitative discourse analysis of language policies and practices of MLM social media provides a productive lens for understanding both the communication challenges of and responses to the COVID-19 pandemic. This approach reveals the variable ways in which language policies are taken up and discourses recontextualized with new meanings and for new purposes across social media platforms.

#Covid-19: An exploratory investigation of hashtag usage on Twitter

BACKGROUND

The literature highlights Twitter as a vital instrument tool for health policy-makers for health communication and promotion. Furthermore, Twitter is a tool allowing us to understand the focus of people regarding a topic of interest.

OBJECTIVE

To provide health policy-makers with insights concerning key topics of interest in the Twitter community regarding Covid-19, and to support information search and health communication.

METHOD

A total of 28.5M tweets have been retrieved, of which 6.9M tweets included hashtags. The data was analyzed using data science and natural language processing libraries. Qualitative analysis was performed using thematic analysis.

RESULTS

907k different hashtags were used. Of these, only 1192 hashtags were used more than 1000 times. The qualitative analysis resulted in 13 themes. The top three themes regarding the number of hashtags used were related to Covid-19, identifying information, interventions, and geographical tagging. We explored the relationship between themes and showed how health practitioners can understand the communication in relation to specific topics expressed as hashtags (e.g., #stayhome).

CONCLUSIONS

The results provide first insights for policy-makers and health practitioners to identify relevant tweets and to choose appropriate hashtags for health communication. The results also show that only with a limited number of Tweets (10 per day) health organizations could have been among the top users.

"Vaccines for pregnant women…?! Absurd" - Mapping maternal vaccination discourse and stance on social media over six months

OBJECTIVE

To understand the predominant topics of discussion, stance and associated language used on social media platforms relating to maternal vaccines in 15 countries over a six-month period.

BACKGROUND

In 2019, the World Health Organisation prioritised vaccine hesitancy as a top ten global health threat and recognized the role of viral misinformation on social media as propagating vaccine hesitancy. Maternal vaccination offers the potential to improve maternal and child health, and to reduce the risk of severe morbidity and mortality in pregnancy. Understanding the topics of discussion, stance and language used around maternal vaccines on social media can inform public health bodies on how to combat vaccine misinformation and vaccine hesitancy.

METHODS

Social media data was extracted (Twitter, forums, blogs and comments) for six months from 15 countries (Australia, Brazil, Canada, France, Germany, India, Italy, Korea, Mexico, Panama, South Africa, Spain, United Kingdom and United States). We used stance, discourse and topic analysis to provide insight into the most frequent and weighted keywords, hashtags and themes of conversation within and across countries.

RESULTS

We exported a total of 19,192 social media posts in 16 languages obtained between 1st November 2018 and 30th April 2019. After screening all posts, 16,000 were included in analyses, while excluding retweets, 2,722 were annotated for sentiment. Main topics of discussion were the safety of the maternal influenza and pertussis vaccines. Discouraging posts were most common in Italy (44.9%), and the USA (30.8%).

CONCLUSION

The content and stance of maternal vaccination posts from November 2018 to April 2019 differed across countries, however specific topics of discussion were not limited to geographical location. These discussions included the promotion of vaccination, involvement of pregnant women in vaccine research, and the trust and transparency of institutions. Future research should examine the relationship between stance (promotional, neutral, ambiguous, discouraging) online and maternal vaccination uptake in the respective regions.

Dataset on dynamics of Coronavirus on Twitter

In this data article, we provide a dataset of 8,982,694 Twitter posts around the coronavirus health global crisis. The data were collected through the Twitter REST API search. We used the rtweet R package to download raw data. The term searched was "Coronavirus" which included the word itself and its hashtag version. We collected the data over 23 days, from January 21 to February 12, 2020. The dataset is multilingual, prevailing English, Spanish, and Portuguese. We include a new variable created from other four variables; it is called "type" of tweets, which is useful for showing the diversity of tweets and the dynamics of users on Twitter. The dataset comprises seven databases which can be analysed separately. On the other hand, they can be crossed to set other researches, among them, trends and relevance of different topics, types of tweets, the embeddedness of users and their profiles, the retweets dynamics, hashtag analysis, as well as to perform social network analysis. This dataset can attract the attention of researchers related to different fields on knowledge, such as data science, social science, network science, health informatics, tourism, infodemiology, and others.

Tracking online topics over time: understanding dynamic hashtag communities

Background

Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propose novel tools for tracing their dynamics in time-dependent data. The observations are characterized by bursty behaviors in the increases and decreases of hashtag usage. These features can be reproduced with a novel model of dynamic rankings.

Hashtag communities in time

We build temporal and weighted co-occurrence networks from hashtags. On static snapshots, we infer the community structure using customized methods. On temporal networks, we solve the bipartite matching problem of detected communities at subsequent timesteps by taking into account higher-order memory. This results in a matching protocol that is robust toward temporal fluctuations and instabilities of the static community detection. The proposed methodology is broadly applicable and its outcomes reveal the temporal behavior of online topics.

Modeling topic-dynamics

We consider the size of the communities in time as a proxy for online popularity dynamics. We find that the distributions of gains and losses, as well as the interevent times are fat-tailed indicating occasional, but large and sudden changes in the usage of hashtags. Inspired by typical website designs, we propose a stochastic model that incorporates a ranking with respect to a time-dependent prestige score. This causes occasional cascades of rank shift events and reproduces the observations with good agreement. This offers an explanation for the observed dynamics, based on characteristic elements of online media.

Public health implications of #ShoutYourAbortion

OBJECTIVES

Social media platforms such as Twitter allow members of the public to raise awareness for issues, causes and events. This study investigated the hashtag #ShoutYourAbortion that was created to combat the stigma that can be associated with the medical procedure of abortion.

STUDY DESIGN

A mixed method and case study approach was used.

METHODS

Twitter data were retrieved and analysed by social network analysis, a subset of tweets were qualitatively coded and the location of tweets was examined.

RESULTS

It was found that a sizable group of users shared tweets denouncing the hashtag and the these users formed a cluster in themselves. The study also identified two narratives: anti-abortion and pro-abortion content.

CONCLUSION

The results have implications for public health organisations and agencies interested in devising digital health campaigns. That is, when devising health campaigns, it is essential to consider the tone of the campaign and whether it is likely to provoke citizens who may have opposing views. Moreover, future campaigns could communicate information surrounding the dangers of unsafe abortions and the broad spectrum of reasons that women may seek abortion, for example, when the child and/or mothers health is at risk.

Social media processes in disasters: Implications of emergent technology use

This article seeks to extend social science scholarship on social media technology use during disruptive events. Though social media's role in times of crisis has been previously studied, much of this work tends to focus on first-responders and relief organizations. However, social media use during disasters tends to be decentralized and this organizational structure can promote different types of messages to top-down information systems. Using 142,786 geo-tagged tweets collected before and after Hurricane Sandy's US landfall as a case study, this article seeks to explore shifts in social media behavior during disruptive events and highlights that though Sandy disrupted routine life within Twitter, users responded to the disaster by employing humor, sharing photos, and checking into locations. We conclude that social media use during disruptive events is complex and understanding these nuanced behaviors is important across the social sciences.