Critical Social Media Analysis: Problematising Online Policy Representations of the Impact of Imprisonment on Families

Drawing on the Foucauldian policy analysis framework developed by Bacchi (2009) and building on insights distilled from a study of discourses on the microblogging SNS, Twitter, this paper makes three novel contributions. It unravels how the impact of imprisonment on families is represented in or produced through policy discourses and other governance practices. It also demonstrates how SNS affordances enable affected families to resist and challenge the discourses and proffer alternatives strategies that can inform a transformational problematization model. The paper makes a third contribution by demonstrating how a methodologically innovative triangulation of computational and social science methods can be used to study the contributions of hard-to-reach populations such as the families of people in prison.

Breath of Change: Evaluating Asthma Information on TikTok and Introducing the Video Health Information Credibility Score

Introduction Asthma's global prevalence underscores the need for accessible health information dissemination, especially in the digital age. TikTok, known for its wide reach and diverse content, presents both opportunities and challenges in health information dissemination. This study aims to characterize the quality and reach of asthma-related content on TikTok and introduces the Video Health Information Credibility Score (VHICS) as a novel tool for quality assessment. Materials and methods We used a systematic methodology to analyze the top 100 TikTok videos by the number of likes tagged with #asthma. Data were collected in June 2023 and January 2024 to allow for temporal trend analysis. Videos were evaluated based on engagement metrics (views, likes, comments, shares, and favorites) and quality using the DISCERN instrument. Results Our analysis showed that physician-generated content accounted for a significant proportion of asthma-related videos, with varying levels of engagement. The DISCERN scores, with a range of 1 (lowest) to 5 highest), provided insights into content quality, revealing trends in user engagement and information reliability over time. Temporal analysis indicated changes in content creation and audience interaction. Discussion The study highlights the evolving landscape of digital health communication on TikTok. The introduction of VHICS added depth to the quality assessment of future directions, indicating the necessity for accurate and reliable health information on social media. The findings suggest an imperative for healthcare professionals to address misinformation and leverage digital platforms for patient education effectively. Conclusions TikTok is a significant medium for health information dissemination, with substantial potential for impact in patient education. The introduction of VHICS can enrich the analysis of video content, offering a robust tool for assessing the quality of health information on social media. This study underscores the importance of credible, clear, and audience-relevant health communication in the digital era.

Navigating Digital Dermatology: An Analysis of Acne-Related Content on TikTok

Background With TikTok's rising popularity as a hub for health information dissemination, the quality and nature of such content require assessment. This study investigates the popularity and quality of the top 100 most-liked videos tagged with "#acne" on TikTok. This study aims to examine the engagement and quality of acne-related content on TikTok, assess contributions from diverse sources, including physicians and non-physicians, and guide healthcare professionals in leveraging this platform for public health education. Methodology A cross-sectional analysis of the top 100 most-liked videos tagged with "#acne" on TikTok as of June 7, 2023, was conducted. Parameters assessed included the profession of the creator, gender, specialty, content type, and other observable characteristics. The quality was measured using the DISCERN tool. Results Of the dataset, 38 videos were by physicians and 29 by non-physicians. Physician-created content had higher mean views, likes, comments, shares, and favorites than non-physician-created content. Videos by dermatologists and non-dermatologists received similar engagement. Videos sharing personal experiences achieved the highest DISCERN score. Overall, DISCERN scores were uniformly low across all categories. Conclusions Physicians, especially dermatologists, are trusted sources of acne-related information on TikTok. The study underscores the need for professionals to provide reliable, evidence-based information on such platforms, guiding effective health communication in the digital age.

Social Bots' Role in the COVID-19 Pandemic Discussion on Twitter

Social bots have already infiltrated social media platforms, such as Twitter, Facebook, and so on. Exploring the role of social bots in discussions of the COVID-19 pandemic, as well as comparing the behavioral differences between social bots and humans, is an important foundation for studying public health opinion dissemination. We collected data on Twitter and used Botometer to classify users into social bots and humans. Machine learning methods were used to analyze the characteristics of topic semantics, sentiment attributes, dissemination intentions, and interaction patterns of humans and social bots. The results show that 22% of these accounts were social bots, while 78% were humans, and there are significant differences in the behavioral characteristics between them. Social bots are more concerned with the topics of public health news than humans are with individual health and daily lives. More than 85% of bots' tweets are liked, and they have a large number of followers and friends, which means they have influence on internet users' perceptions about disease transmission and public health. In addition, social bots, located mainly in Europe and America countries, create an "authoritative" image by posting a lot of news, which in turn gains more attention and has a significant effect on humans. The findings contribute to understanding the behavioral patterns of new technologies such as social bots and their role in the dissemination of public health information.

Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.

Exploring public opinion about telehealth during COVID-19 by social media analytics

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users' opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.

Social Bots' Involvement in the COVID-19 Vaccine Discussions on Twitter

During the COVID-19 pandemic, social media served as an important channel for the public to obtain health information and disseminate opinions when offline communication was severely hindered. Yet the emergence of social bots influencing social media conversations about public health threats will require researchers and practitioners to develop new communication strategies considering their influence. So far, little is known as to what extent social bots have been involved in COVID-19 vaccine-related discussions and debates on social media. This work selected a period of nearly 9 months after the approval of the first COVID-19 vaccines to detect social bots and performed high-frequency word analysis for both social bot-generated and human-generated tweets, thus working out the extent to which social bots participated in the discussion on the COVID-19 vaccine on Twitter and their participation features. Then, a textual analysis was performed on the content of tweets. The findings revealed that 8.87% of the users were social bots, with 11% of tweets in the corpus. Besides, social bots remained active over three periods. High-frequency words in the discussions of social bots and human users on vaccine topics were similar within the three peaks of discourse.

Sentiments Evoked by WHO Public Health Posts During the COVID-19 Pandemic: A Neural Network-Based Machine Learning Analysis

Introduction

The World Health Organization (WHO) is a specialized agency of the United Nations responsible for international public health. Established on April 7, 1948, it has since played a pivotal role in several public health achievements and has had considerable success. But never since the establishment of the WHO has it faced a pandemic of such a huge scale. The spread of the coronavirus and the inability of the WHO to contain it has raised many questions about its efficiency and role. The present study explores the range of emotions and sentiments evoked by public health information posts of WHO over the course of the pandemic.

Methods

This study uses Bidirectional Encoder Representations from Transformers (BERT), which is a neural network-based technique for natural language processing. Three timeframes of five months each, starting from March 2020, were defined. A total of six posts, two posts from each timeframe, were then analysed. Comments were classified as positive, neutral and negative. The broader positive and negative classes were further subclassified into two classes each. Natural language processing was further applied to obtain results.

Results

The general trend of the sentiments over the period of pandemic showed a significant and dominant proportion of negative comments that overshadowed the neutral, positive and irrelevant comments over all timeframes. Specifically, the negative sentiments peaked during the second timeframe. The negativity was directed more towards the WHO, governments and people not complying with coronavirus disease 2019-appropriate norms. Positive comments were mostly expressed towards health workers.

Conclusion

An unusually high proportion of negative sentiment was observed in response to relatively innocuous public health posts. This may be a result of heightened anxiety, questionable credibility of the sources of information and geopolitical power play maligning the image of the WHO.

Create Solidarity Networks: Dialogs in Reddit to Overcome Depression and Suicidal Ideation among Males

The emerging scientific literature examines masculinity and gender roles as risk factors for suicide ideation or suicide in young adults and adolescents. In this vein, recent studies show that certain traditional masculine norms are related to poorer mental health-related outcomes, which influences suicide and suicide ideation. This study contributes with new understandings about the associations between masculinity and suicidal ideation among males through Reddit debates in English. The posts with more interactions referring to masculinity in the topics gender and education have been selected on Reddit, emphasizing transformative personal experiences potentially helping avoid suicide ideation. Through the analysis of Reddit posts, it is shown how users can generate spaces to express the diverse ways to live with masculinity. The discussions on Reddit in the different areas selected demonstrate the existence of proposals on how to overcome fears and facilitate relaxation of norms regarding self-reliance to encourage help-seeking when feeling depressed and therefore at greater risk of suicide ideation. The results highlight the potential importance of platforms such as Reddit to create solidarity networks, showing multiple ways of being a man and demystifying dominant masculinity by sharing different experiences.

Citizen Debates in Social Networks about Didactic Resources for Mathematics

Citizens are increasingly turning to social media to open up debates on issues of utmost importance, such as health or education. When analyzing citizens’ social media interactions on COVID-19, research has underlined the importance of sharing and spreading information based on scientific evidence rather than on fake news. However, whether and how citizens’ interactions in the field of education, particularly in mathematics, are based on scientific evidence remains underexplored. To contribute to filling this gap, this article presents an analysis of citizen debates in social networks about didactic resources for mathematics. Through social media analytics, 136,964 posts were extracted from Reddit, Instagram, Twitter and Facebook, of which 1755 were analyzed. Results show that out of the 213 posts of citizen debates on didactic resources for mathematics, only two contained scientific evidence and eight claimed to contain scientific evidence. These findings highlight the importance of promoting actions to encourage citizen debates around didactic resources for mathematics based on scientific evidence.

Iktishaf+: A Big Data Tool with Automatic Labeling for Road Traffic Social Sensing and Event Detection Using Distributed Machine Learning

Digital societies could be characterized by their increasing desire to express themselves and interact with others. This is being realized through digital platforms such as social media that have increasingly become convenient and inexpensive sensors compared to physical sensors in many sectors of smart societies. One such major sector is road transportation, which is the backbone of modern economies and costs globally 1.25 million deaths and 50 million human injuries annually. The cutting-edge on big data-enabled social media analytics for transportation-related studies is limited. This paper brings a range of technologies together to detect road traffic-related events using big data and distributed machine learning. The most specific contribution of this research is an automatic labelling method for machine learning-based traffic-related event detection from Twitter data in the Arabic language. The proposed method has been implemented in a software tool called Iktishaf+ (an Arabic word meaning discovery) that is able to detect traffic events automatically from tweets in the Arabic language using distributed machine learning over Apache Spark. The tool is built using nine components and a range of technologies including Apache Spark, Parquet, and MongoDB. Iktishaf+ uses a light stemmer for the Arabic language developed by us. We also use in this work a location extractor developed by us that allows us to extract and visualize spatio-temporal information about the detected events. The specific data used in this work comprises 33.5 million tweets collected from Saudi Arabia using the Twitter API. Using support vector machines, naïve Bayes, and logistic regression-based classifiers, we are able to detect and validate several real events in Saudi Arabia without prior knowledge, including a fire in Jeddah, rains in Makkah, and an accident in Riyadh. The findings show the effectiveness of Twitter media in detecting important events with no prior knowledge about them.

Navigating ‘Home Schooling’ during COVID-19: Australian public response on Twitter

COVID-19 has wreaked havoc worldwide. Schools have escaped neither the pandemic nor its consequences. Indeed, by April 2020, schools had been suspended in 189 countries, affecting 89% of learners globally. While the Australian government has implemented variously effective health and economic policies in response to COVID-19, their inability to agree with states on education policy during the pandemic caused considerable confusion and anxiety. Accordingly, this study analyses 3 weeks of Tweets during April, leading up to the beginning of term 2, during the height of Government policy incongruity. Findings confirm a wide and rapidly changing range of public responses on Twitter. Nine themes were identified in the quantitative analysis, and six of these (positive, negative, humorous, appreciation for teachers, comments aimed at Government/politicians and definitions) are expanded upon qualitatively. Over the course of 3 weeks, the public began to lose its sense of humour and negative tweets almost doubled.

#Eggs: social and online media-derived perceptions of egg-laying hen housing

Enormous quantities of data are generated through social and online media in the era of Web 2.0. Understanding consumer perceptions or demand efficiently and cost effectively remains a focus for economists, retailer/consumer sciences, and production industries. Most of the efforts to understand demand for food products rely on reports of past market performance along with survey data. Given the movement of content-generation online to lay users via social media, the potential to capture market-influencing shifts in sentiment exists in online data. This analysis presents a novel approach to studying consumer perceptions of production system attributes using eggs and laying hen housing, which have received significant attention in recent years. The housing systems cage-free and free-range had the greatest number of online hits in the searches conducted, compared with the other laying hen housing types. Less online discussion surrounded enriched cages, which were found by other methods/researchers to meet many key consumer preferences. These results, in conjunction with insights into net sentiment and words associated with different laying hen housing in online and social media, exemplify how social media listening may complement traditional methods to inform decision-makers regarding agribusiness marketing, food systems, management, and regulation. Employing web-derived data for decision-making within agrifood firms offers the opportunity for actionable insights tailored to individual businesses or products.

Public Perceptions of Veterinarians from Social and Online Media Listening

The public perception of the veterinary medicine profession is of increasing concern given the mounting challenges facing the industry, ranging from student debt loads to mental health implications arising from compassion fatigue, euthanasia, and other challenging aspects of the profession. This analysis employs social media listening and analysis to discern top themes arising from social and online media posts referencing veterinarians. Social media sentiment analysis is also employed to aid in quantifying the search results, in terms of whether they are positivity/negativity associated. From September 2017-November 2019, over 1.4 million posts and 1.7 million mentions were analyzed; the top domain in the search results was Twitter (74%). The mean net sentiment associated with the search conducted over the time period studied was 52%. The top terms revealed in the searches conducted revolved mainly around care of or concern for pet animals. The recognition of challenges facing the veterinary medicine profession were notably absent, except for the mention of suicide risks. While undeniably influenced by the search terms selected, which were directed towards client–clinic related verbiage, a relative lack of knowledge regarding veterinarians’ roles in human health, food safety/security, and society generally outside of companion animal care was recognized. Future research aimed at determining the value of veterinarians’ contributions to society and, in particular, in the scope of One Health, may aid in forming future communication and education campaigns.

Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter's Users: An Exploratory Content- and Topology-Based Analysis

Binge Drinking (BD) is a common risky behaviour that people hardly report to healthcare professionals, although it is not uncommon to find, instead, personal communications related to alcohol-related behaviors on social media. By following a data-driven approach focusing on User-Generated Content, we aimed to detect potential binge drinkers through the investigation of their language and shared topics. First, we gathered Twitter threads quoting BD and alcohol-related behaviours, by considering unequivocal keywords, identified by experts, from previous evidence on BD. Subsequently, a random sample of the gathered tweets was manually labelled, and two supervised learning classifiers were trained on both linguistic and metadata features, to classify tweets of genuine unique users with respect to media, bot, and commercial accounts. Based on this classification, we observed that approximately 55% of the 1 million alcohol-related collected tweets was automatically identified as belonging to non-genuine users. A third classifier was then trained on a subset of manually labelled tweets among those previously identified as belonging to genuine accounts, to automatically identify potential binge drinkers based only on linguistic features. On average, users classified as binge drinkers were quite similar to the standard genuine Twitter users in our sample. Nonetheless, the analysis of social media contents of genuine users reporting risky behaviours remains a promising source for informed preventive programs.

Understanding an Urban Park through Big Data

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.