Iatrogenic effects of Reboot/ NoFap on public health: A preregistered survey study

“Reboot,” especially NoFap, promotes abstinence from masturbation and/or pornography to treat “pornography addiction,” an unrecognized diagnosis. While the intention of Reboot/NoFap is to decrease distress, qualitative studies have consistently suggested that “Reboots” paradoxically cause more distress. The distress appears to occur in response to (1) the abstinence goal, which recasts common sexual behaviors as personal “failures,” and (2) problematic and inaccurate Reboot/NoFap forum messaging regarding sexuality and addiction. This preregistered survey asked men about their experience with perceived “relapse” and NoFap forums. Participants reported that their most recent relapse was followed by feeling shameful, worthless, sad, a desire to commit suicide, and other negative emotions. A novel predictor of identifying as a pornography addict in this lower religiosity sample was higher narcissism. Participants reported that NoFap forums contained posts that were misogynist (73.7% of participants), bullying (49.1%), anti-LGBT (42.9%), antisemitic (32.0%), instructing followers to harm or kill themselves (23.5%), or threats to hurt someone else (21.1%). More engagement in NoFap online forums was associated with worse symptoms of erectile dysfunction, depression, anxiety, and more sex negativity. Results support and expand previously documented harms and problems with Reboot/NoFap claims of treating pornography addiction from qualitative research

An Automated Tool to Detect Suicidal Susceptibility from Social Media Posts

According to the World Health Organization (WHO), approximately 1.4 million individuals died by suicide in 2022. This means that one person dies by suicide every 20 seconds. Globally, suicide ranks as the 10th leading cause of death, while it ranks second for young people aged 15-29. In the year 2022, it was estimated that about 10.5 million suicide attempts occurred. The WHO suggests that alongside each completed suicide, there are many individuals who make attempts. Today, social media is a place where people share their feelings, such as happiness, sadness, anger, and love. This helps us understand how they are thinking or what they might do. This study takes advantage of this opportunity and focuses on developing an automated tool to find if someone may be thinking about harming themselves. It is developed based on the Suicidal-Electra model. We collected datasets of social media posts, processed them, and used them to train and fine-tune the model. Upon evaluating the refined model with a testing dataset, we consistently observed outstanding results. The model demonstrated an impressive accuracy rate of 93% and a commendable F1 score of 0.93. Additionally, we developed an API enabling seamless integration with third-party platforms, enhancing its potential for implementation to address the growing concern of rising suicide rates.Comment: 8 pages, 10 figures, 1 table. Submitted to Peer

Text Mining Methods for Analyzing Online Health Information and Communication

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients\u27 questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit organizations and federal agencies can also diffuse preventative information in such networks for better outcomes. Researchers in health communication can mine user generated content on social networks to understand themes and derive insights into patient experiences that may be impractical to glean through traditional surveys. The main difficulty in mining social health data is in separating the signal from the noise. Social data is characterized by informal nature of content, typos, emoticons, tonal variations (e.g. sarcasm), and ambiguities arising from polysemous words, all of which make it difficult in building automated systems for deriving insights from such sources. In this dissertation, we present four efforts to mine health related insights from user generated social data. In the first effort, we build a model to identify marketing tweets on electronic cigarettes (e-cigs) and assess different topics in marketing and non-marketing messages on e-cigs on Twitter. In our next effort, we build ensemble models to classify messages on a mental health forum for triaging posts whose authors need immediate attention from trained moderators to prevent self-harm. The third effort deals with models from our participation in a shared task on identifying tweets that discuss adverse drug reactions and those that mention medication intake. In the final task, we build a classifier that identifies whether a particular tweet about the popular Juul e-cig indicates the tweeter actually using the product. Our methods range from linear classifiers (e.g., logistic regression), classical nonlinear models (e.g., nearest neighbors), recent deep neural networks (e.g., convolutional neural networks), and ensembles of all these models in using different supervised training regimens (e.g., co-training). The focus is more on task specific system building than on building specific individual models. Overall, we demonstrate that it is possible to glean insights from social data on health related topics through natural language processing and machine learning with use-cases from substance use and mental health

Exploring the potential of online self-reported and routinely collected electronic healthcare record data in self-harm research

Background:Self-harm is a major public health concern and is a leading cause of death from injury. Reaching participants for self-harm research raises a number of challenges, however an opportunity exists in the use of both the internet for data collection and in the use of routinely collected healthcare data.Aims and objectives:The aim of this project was to explore the potential of both online and routinely collected healthcare data for self-harm research and the way in which these data sources can be brought together.Methods:This thesis represents a series of projects exploring the use of various data sources for self-harm research. The first was the development and piloting of an online platform (SHARE UK) for self-harm research. This website incorporated multiple functions: hosting questionnaires; sign-up for a research register; sign-up for linkage with routinely collected data and uploads to a media databank. Next a national survey was conducted to explore young people’s perspectives on the use of both online and healthcare data for self-harm research. Lastly a population level electronic health record cohort study analysing trends over time and contacts across healthcare services was conducted.Results:Participants engaged well with research online: 498 participants signed up to the SHARE UK platform; of whom 85% signed up for the research register. Sixty-two participants uploaded 95 items to the media databank. Alternative formats are discussed. Only 15% of participants consented for linkage with healthcare data. A total of 2,733 young people aged 10-24 who self-harm completed the national survey. Results demonstrated that the necessity for participants to give their address for linkage poses a significant barrier. Opinions around the use of Big Data, encompassing social media, marketing and health data are explored.A total of 937,697 individuals aged 10-24 provided 5,269,794 person years of data from 01.01.2003 to 20.09.2015 to the electronic health record cohort study. Self-harm incidence was highest in primary care. Males preferentially present to emergency departments. Male are less likely than females to be admitted following attendance. This difference persists in the youngest age groups and for self-poisoning. Analysis supports the importance of non-specialist services.Conclusions:This thesis has explored both online and routinely collected healthcare data and their utility for self-harm research, exploring participant views and issues via a national survey. An online platform for self-harm research was successfully piloted and issues identified. This series of projects explores possibilities for future self-harm research. The use of multiple data sources allows research to represent both those in the community and those presenting to healthcare settings, lowering many of the barriers to participating in self-harm research. The future utility of the SHARE UK platform through its collaboration with the Adolescent Mental Health Data Platform (ADP) is discussed. Results of this series of projects will be used to inform the development of this platform with lessons learnt from the pilot addressed and findings from both the national survey and the electronic health record cohort study informing and shaping future research

Active children through individual vouchers – evaluation (ACTIVE): A mixed method randomised control trial to improve the cardiovascular fitness and health of teenagers

To experience the health benefits of physical activity, it is recommended thatchildren and young people take part in at least 60 minutes of moderate to vigorousactivity on average per day across the week. In Wales, only 11% of girls and 20% ofboys are reported to meet these government recommendations with accessibility(e.g., cost and lack of local facilities) cited as the main barrier to participation. Todate, interventions have experienced short-term success. These interventions oftenplace emphasis on policymakers as the leaders, or experts on the matter in question.However, this can result in a disconnect between what is provided and what thegroup receiving the intervention value and need. The Active Children throughIndividual Vouchers – Evaluation Project (ACTIVE), funded by the British HeartFoundation (BHF), aimed to empower teenagers and tackle accessibility barriers toimprove the physical activity, cardiovascular fitness, motivation and heart health ofthose aged 13 – 14 in south Wales. This study was co-produced by teenagers from itsinception to delivery of the ACTIVE intervention and included a multi-componentintervention encompassing a voucher scheme, peer mentoring and support workerengagement. The ACTIVE RCT had a positive impact on cardiovascular fitness andblood pressure as well as perceptions of activity. The findings from observationaldata provide some key predictors of teenage health which can be used to be proactivein promoting healthy behaviours in young people and identifies some protectivefactors which can be promoted to families and first-time parents. The key messagefrom ACTIVE is that young people want to have their say in activity provision sothat they can increase their opportunities to participate in unstructured, fun and socialactivity in their local communities. To improve physical activity, more should bedone to listen to teenagers as to what they want and need

Analysis and Decision-Making with Social Media

abstract: The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to study the behavior of individuals online (content analysis) and 2) Synthesis: to build models that influence the behavior of individuals offline (incomplete action models for decision-making). A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data. The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Doctors’ attitudes toward specific medical conditions

This study uses machine learning and natural language processing tools to examine the language used by healthcare professionals on a global online forum. It contributes to an underdeveloped area of knowledge, that of physician attitudes toward their patients. Using comments left by physicians on Reddit's ”Medicine” subreddit (r/medicine), we test if the language from online discussions can reveal doctors’ attitudes toward specific medical conditions. We focus on a set of chronic conditions that usually are more stigmatized and compare them to ones well accepted by the medical community. We discovered that when comparing diseases with similar traits, doctors discussed some conditions with more negative attitudes. These results show bias does not occur only along the dimensions traditionally analyzed in the economics literature of gender and race, but also along the dimension of disease type. This is meaningful because the emotions associated with beliefs impact physicians’ decision making, prescribing behavior, and quality of care. First, we run a binomial LASSO-logistic regression to compare a range of 21 diseases against myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), depression, and the autoimmune diseases multiple sclerosis and rheumatoid arthritis. Next, we use dictionary methods to compare five more chronic diseases: Lyme disease, Ehlers-Danlos syndrome (EDS), Alzheimer's disease, osteoporosis, and lupus. The results show physicians discuss ME/CFS, depression, and Lyme disease with more negative language than the other diseases in the set. The results for ME/CFS included over four times more negative words than the results for depression

A QUALITATIVE ANALYSIS OF ONLINE SELF-HARM SUPPORT FORUMS: EXAMINING USERS’ ONLINE ACTIVITIES DURING SELF-HARM DESISTANCE PROCESSES

Non- suicidal self-injury, commonly referred to as NSSI, is defined as the damage of one’s body tissue through the practices of, but not limited to, cutting, burning, branding, bone-breaking, biting, hair pulling and head banging (Adler & Adler, 2011), without suicidal intent (Lewis & Mehrabkhani, 2016). Self-harm literature has primarily focused on persistence processes and NSSI-related online interaction in the maintenance of pro self-harm ideology and practice. Alternatively, this research will provide insight into desistance processes of non-suicidal self-injury (NSSI) and related online interactions by conducting a virtual ethnography (Hine, 2000) of open, online spaces, consistent with the symbolic interactionist perspective (Blumer, 1969) that guided this project. Specifically, the research project seeks to understand how individuals describe their experiences with maintaining and stopping self-harm in online self-harm support forums and how they use these forums during the exit phases of their self-harm. Most notably, this research project offers insight into the significance of this online activity for our traditional notions of desistance in offline contexts