Forum Posts Research Articles

Exploring the influence of a massive open online course (MOOC) for preparing rehabilitation professionals to meet the needs of forcibly displaced persons

BackgroundTo assist in the global training of rehabilitation professionals to address the unique needs of forcibly displaced persons, a free massive open online course (MOOC) was developed. This study aimed to explore self-perceived knowledge following completion of courses within the MOOC, ability to implement changes in practice, and participant engagement with the MOOC content.MethodsThe Kirkpatrick Model was used to assess the effectiveness of the course with a focus on reaction, learning, and behavior. Participant interaction with the course content was explored using thematic analysis of the MOOC’s discussion forum content. Participants in the MOOC were asked to complete pre and post-surveys of self-perceived knowledge regarding the rehabilitation of displaced persons. Mean within-subject pre and post-MOOC data were compared using the Mann U Whitney test. Knowledge implementation after six months was assessed with a follow-up survey.ResultsSelf-reported knowledge following completion of the MOOC improved significantly on individual survey questions (p≤.004) as well as composite score (p < .001). Six months following completion of the course, participants noted the ability to implement multiple strategies into practice. Three themes emerged from the discussion forum postings: (1) Understanding the persons lived experience to provide effective, holistic, patient-centered care, (2) Communication to develop a sense of connection and facilitate the provision of quality care, and (3) The importance of culturally responsive practice.ConclusionThe MOOC effectively delivered the fundamental knowledge to address the complex needs of displaced individuals among a diverse group of practicing rehabilitation professionals and can play a role in preparing the rehabilitation workforce for increased population displacement, alongside specialist programs in related themes.

Automatic detection of problem-gambling signs from online texts using large language models.

Problem gambling is a major public health concern and is associated with profound psychological distress and economic problems. There are numerous gambling communities on the internet where users exchange information about games, gambling tactics, as well as gambling-related problems. Individuals exhibiting higher levels of problem gambling engage more in such communities. Online gambling communities may provide insights into problem-gambling behaviour. Using data scraped from a major German gambling discussion board, we fine-tuned a large language model, specifically a Bidirectional Encoder Representations from Transformers (BERT) model, to predict signs of problem-gambling from forum posts. Training data were generated by manual annotation and by taking into account diagnostic criteria and gambling-related cognitive distortions. Using cross-validation, our models achieved a precision of 0.95 and F1 score of 0.71, demonstrating that satisfactory classification performance can be achieved by generating high-quality training material through manual annotation based on diagnostic criteria. The current study confirms that a BERT-based model can be reliably used on small data sets and to detect signatures of problem gambling in online communication data. Such computational approaches may have potential for the detection of changes in problem-gambling prevalence among online users.

Social media as an amplifier of insider trading profits

Our study investigates the relationship between stock forum posts and insider trading profitability. Using regression analysis, we find a positive relationship between the number of posts on a firm’s stock forum and insider trading profitability, particularly for sales transactions. A difference-in-differences analysis based on the launch of the mobile app for stock forums further confirms these results. This profit-amplifying effect of stock forum posts is likely due to the pervasive noise on social media, as it weakens following the introduction of regulations aimed at cleaning up the internet’s information environment. Additionally, we observe distinctions in post tone around insider trades, with the profit-amplifying effect more pronounced in firms with high information asymmetry, substantial retail holdings, and less foreign ownership. Overall, our study highlights a potential dark side of social media in amplifying insider trading profits, emphasizing the need for regulatory measures.

Revisiting Sentiment Analysis for Software Engineering in the Era of Large Language Models

Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.

Efficient topic identification for urgent MOOC Forum posts using BERTopic and traditional topic modeling techniques

AbstractMOOC platforms provide a means of communication through forums, allowing learners to express their difficulties and challenges while studying various courses. Within these forums, some posts require urgent attention from instructors. Failing to respond promptly to these posts can contribute to higher dropout rates and lower course completion rates. While existing research primarily focuses on identifying urgent posts through various classification techniques, it has not adequately addressed the underlying reasons behind them. This research aims to delve into these reasons and assess the extent to which they vary. By understanding the root causes of urgency, instructors can effectively address these issues and provide appropriate support and solutions. BERTopic utilizes the advanced language capabilities of transformer models and represents an advanced approach in topic modeling. In this study, a comparison was conducted to evaluate the performance of BERTopic in topic modeling on MOOCs discussion forums, alongside traditional topic models such as LDA, LSI, and NMF. The experimental results revealed that the NMF and BERTopic models outperformed the other models. Specifically, the NMF model demonstrated superior performance when a lower number of topics was required, whereas the BERTopic model excelled in generating topics with higher coherence when a larger number of topics was needed.The results considering all urgent posts from the dataset were as follows: Optimal number of topics is 6 for NMF and 50 for BERTopic; coherence scores is 0.66 for NMF and 0.616 for BERTopic; and IRBO scores is 1 for both models. This highlights the BERTopic model capability to distinguish and extract diverse topics comprehensively and coherently, aiding in the identification of various reasons behind MOOC Forum posts.

Initial exploration into sarcasm and irony through machine translation

In this paper, we investigate sarcasm and irony as seen through a novel perspective of machine translation. We employ various techniques for translation, comparing both manually and automatically translated datasets of irony and sarcasm. We first clarify the definitions of irony and sarcasm and present an exhaustive field review of studies on irony both from purely linguistic as well as computational linguistic perspectives. We also propose a novel evaluation metric for the purpose of evaluating translations of figurative language, with a focus on machine-translated irony and sarcasm. The constructed English and Chinese parallel dataset includes polarized content from tweets as well as forum posts, categorized by irony types. The preferred translation model, mBART-50, is identified through a thorough experimental process. Optimal translation settings and the best-finetuned model for irony are explored, with the most effective model being finetuned on both ironic and non-ironic data. We also experimented which types of irony are best suitable for training in this specific task — short microblogging messages or longer forum posts. Moreover, we compare the capabilities of a well fine-tuned mBART to a prompt-based method using the recently popular ChatGPT model, with the conclusion that the former still outperforms the latter, although ChatGPT without any training can be considered as a “good enough” ad hoc solution in the case of a lack of data for training. Finally, we verify if the translated data – either manually, or with an MT model – can be used as training data in a task of irony detection. We believe that the presented research can be expanded into languages other than the presented here Chinese and English, which together with the ability to detect various categories of irony, could contribute to deepening the understanding of figurative language, especially irony and sarcasm.

I’m writing in Tunisian: Orthography and identity in written Tunisian Arabic

In recent years the amount of written vernacular Arabic has increased dramatically. But encoding an unstandardized language in writing is not straightforward and mechanical; rather, it is a complex process that balances practical considerations with ideological stances such as autonomy from the standard language. This study examines how writers of Tunisian Arabic (or derja) are navigating this elaboration process. Using a quantitative analysis of a 279,000-word corpus of print literary works written in Tunisian Arabic (2014–2021) and a 5.8-million-word corpus of internet forum posts (2010–2021), this paper explores how Tunisians writing in derja make orthographic choices to collectively position themselves in relation to the larger Arab world. The study finds that forum writers who have advocated for an improved status for Tunisian Arabic use more phonemic spelling forms—more closely representing Tunisian pronunciation—while those who have advocated for Standard Arabic are more conservative in their spelling choices. The authors of Tunisian Arabic novels and translations—pulled between issues of elaboration and readability—are a bit more conservative than the pro- derja group. These results show how writers in unstandardized or not-yet-standardized languages like Tunisian Arabic use orthography to express national identity stances in relation to supranational languages and identities.

Online paedophiles and the criminal justice experience: A qualitative perspective

BackgroundOnline forums provide a conduit for anonymous communication within deviant subcultures, such as online paedophiles. This helps to build virtual communities of support. ObjectiveThis study explores the experience of paedophiles in the criminal justice system as reported by those who experienced it on such a forum. By analysing and understanding the experiences disclosed by these individuals, we may be able to design more targeted prevention strategies and treatment options.Participants and Setting.Data are from one online forum discussion thread that includes 595 online forum posts by 212 unique usernames ranging over 11 years from 2012 to 2023. This forum claims to be an anonymous “therapeutic community” for paedophiles and does not allow the sharing of media (e.g., videos or images). MethodsThis study uses an inductive approach to analyse the posts and discover the perspective and concerns of users who have experienced or are faced with prosecution and punishment. ResultsSeven main themes emerged from the posts on this thread: support and praise, techniques of neutralisation, life in prison, anxiety and mental health, relationships, therapy, and difficulties post-prison. ConclusionsThe forum provided an important source of support among a virtual community of people who shared similar sexual interest in children and feel rejected and demonised by mainstream society. The thread functioned like an “echo chamber” that reinforced a positive view of users' deviant interest. Individuals who had been prosecuted for their offending were returning to the forum, which may encourage reoffending. Parole boards and probation officers may need to consider limiting or restricting access to the Internet, particularly the Tor network, when setting release conditions. Implications for treatment focus on challenging the justifications and excuses used by paedophiles.

Strategies for communicating and mitigating algorithmic control on delivery platforms

Delivery platforms use algorithmic control mechanisms to control couriers’ work. Despite studies looking at how algorithmic control manifests on different delivery platforms, there is a dearth of research exploring how delivery platforms communicate their approaches to algorithmic control, what kinds of strategies delivery workers adopt to mitigate algorithmic control, and to what extent information regarding such ‘algoactivistic approaches’ is shared among communities of delivery couriers who in many cases are in direct competition with one another. To that end, two studies are conducted. Study 1 analyses five publicly listed delivery companies’ annual reports ( n = 14), highlighting a lack of transparency on how the platforms’ algorithms function. In Study 2, a netnographic approach is used to conduct interviews ( n = 12) and a qualitative analysis of delivery workers’ online discussion forum posts ( n = 830) on the discussion forum Reddit. Four approaches to mitigating algorithmic control are found, and the way these are shared among couriers is considered.

Is AI-Generated Extremism Credible? Experimental Evidence from an Expert Survey

ABSTRACT Rapid progress in Artificial Intelligence (AI) over the past few years has unfolded important questions—and triggered anxiety—about the potential (mis)use of AI-generated content in information warfare and disinformation campaigns. A crucial question remains unanswered, however: whether—and to what extent—this technology can be harnessed in a credible way by extremists, whose language is usually characterized by highly specific subcultural registers that make them unlikely to be easily mimicked by AI models. This paper answers that question, offering the first rigorous evaluation of the credibility of synthetic (AI-generated) extremist prose. Using an expert survey experiment measuring the credibility of fake incel forum posts and ISIS magazines paragraphs generated through an original workflow for the production of synthetic extremist text, we show that these texts have high credibility scores, confusing world-leading experts. These findings, which are discussed under the broader light of the emerging literature on nefarious “dual-use” of synthetic content, not only define and evaluate the threat of extremists harnessing language models for propaganda, but they also more broadly strengthen our understanding of the role language models are due to play in hostile political communication, and ultimately resurface the debate on the human costs of technological progress.

Novel wellbeing and repair peptide use in the UK: Netnographic findings

Recently, there has been an explosion of interest in synthetic peptides hormones promoted for wellbeing enhancement purposes, including BPC-157, TB-500 and CJC-1295. With these drugs increasingly accessible through online platforms such as e-commerce sites, this research seeks to understand user experiences of taking these drugs, and the ways in which digital forum spaces facilitate the development of a shared folk pharmacology and further indigenous harm reduction.Data is drawn from a netnography of peptide-related forum posts, encompassing 493 unique threads totalling almost 15,000 posts. Thematic analysis was used to analyse the data.Findings are split into two major sections, encompassing motivations for peptide use, and online folk pharmacology. In the first of these sections, findings explored include: Peptide use for the purposes of injury rehabilitation, including post-surgery recovery; Everyday wellbeing use of peptides, including for anti-ageing, with a particular focus on older men wishing to return to gym training for aesthetic and general health purposes; And the relationship between seeking black market peptides following negative experiences with healthcare providers for wellbeing-related issues.In the second section, key themes relate to: Community knowledge exchange regarding use, including community distribution of harm reduction information; Community understandings of the limits to utility of peptides, and experiences of product not working as anticipated; And information sharing regarding product quality, and potential harms relating to ‘scam’ sellers and substituted or poor-quality product.Conclusions show that an emergent folk pharmacology relating to peptides has developed in forum spaces, which informs and encourages use. Many older men appear to be taking peptides for everyday wellbeing purposes, suggesting a need for specific focus on this population in public health work. Issues with healthcare providers appeared to lead users to accessing black market peptides, suggesting specific education for healthcare practitioners around peptides may be important to pursue.

ChatGPT for Automated Qualitative Research: Content Analysis.

Data analysis approaches such as qualitative content analysis are notoriously time and labor intensive because of the time to detect, assess, and code a large amount of data. Tools such as ChatGPT may have tremendous potential in automating at least some of the analysis. The aim of this study was to explore the utility of ChatGPT in conducting qualitative content analysis through the analysis of forum posts from people sharing their experiences on reducing their sugar consumption. Inductive and deductive content analysis were performed on 537 forum posts to detect mechanisms of behavior change. Thorough prompt engineering provided appropriate instructions for ChatGPT to execute data analysis tasks. Data identification involved extracting change mechanisms from a subset of forum posts. The precision of the extracted data was assessed through comparison with human coding. On the basis of the identified change mechanisms, coding schemes were developed with ChatGPT using data-driven (inductive) and theory-driven (deductive) content analysis approaches. The deductive approach was informed by the Theoretical Domains Framework using both an unconstrained coding scheme and a structured coding matrix. In total, 10 coding schemes were created from a subset of data and then applied to the full data set in 10 new conversations, resulting in 100 conversations each for inductive and unconstrained deductive analysis. A total of 10 further conversations coded the full data set into the structured coding matrix. Intercoder agreement was evaluated across and within coding schemes. ChatGPT output was also evaluated by the researchers to assess whether it reflected prompt instructions. The precision of detecting change mechanisms in the data subset ranged from 66% to 88%. Overall κ scores for intercoder agreement ranged from 0.72 to 0.82 across inductive coding schemes and from 0.58 to 0.73 across unconstrained coding schemes and structured coding matrix. Coding into the best-performing coding scheme resulted in category-specific κ scores ranging from 0.67 to 0.95 for the inductive approach and from 0.13 to 0.87 for the deductive approaches. ChatGPT largely followed prompt instructions in producing a description of each coding scheme, although the wording for the inductively developed coding schemes was lengthier than specified. ChatGPT appears fairly reliable in assisting with qualitative analysis. ChatGPT performed better in developing an inductive coding scheme that emerged from the data than adapting an existing framework into an unconstrained coding scheme or coding directly into a structured matrix. The potential for ChatGPT to act as a second coder also appears promising, with almost perfect agreement in at least 1 coding scheme. The findings suggest that ChatGPT could prove useful as a tool to assist in each phase of qualitative content analysis, but multiple iterations are required to determine the reliability of each stage of analysis.

HELPeR: Interface Design Decision and Evaluation.

Inequities in health information access contribute to disparities in health outcomes. Health recommender systems have emerged as a promising solution to help users find the right information. Despite their various applications, it remains understudied how these systems can aid cancer patients. In this paper, we introduce HELPeR, a recommender system designed to assist ovarian cancer patients with their information needs. The design addresses cold-start challenges, drawing input from health experts and ovarian cancer forum posts. We evaluated HELPeR with nurse practitioners in a cold-start scenario, highlighting its benefits and areas for future improvement.

Reduced speech coherence in psychosis-related social media forum posts

The extraction of linguistic markers from social media posts, which are indicative of the onset and course of mental disorders, offers great potential for mental healthcare. In the present study, we extracted over one million posts from the popular social media platform Reddit to analyze speech coherence, which reflects formal thought disorder and is a characteristic feature of schizophrenia and associated psychotic disorders. Natural language processing (NLP) models were used to perform an automated quantification of speech coherence. We could demonstrate that users who are active on forums geared towards disorders with a higher degree of psychotic symptoms tend to show a lower level of coherence. The lowest coherence scores were found in users of forums on dissociative identity disorder, schizophrenia, and bipolar disorder. In contrast, a relatively high level of coherence was detected in users of forums related to obsessive–compulsive disorder, anxiety, and depression. Users of forums on posttraumatic stress disorder, autism, and attention-deficit hyperactivity disorder exhibited medium-level coherence. Our findings provide promising first evidence for the possible utility of NLP-based coherence analyses for the early detection and prevention of psychosis on the basis of posts gathered from publicly available social media data. This opens new avenues for large-scale prevention programs aimed at high-risk populations.

Transitioning to Online Instructions and COVID-19 Response: A View from Mining Emergent College Students Discourse in Online Discussion Forum

AbstractThe COVID-19 pandemic disrupted teaching and learning activities in higher education around the world. As universities shifted to remote instruction in response to the pandemic, it is important to learn how students engaged in learning during this challenging period. In this paper, we examined the changes in learners’ social and cognitive presence in online discussion forums prior and after remote instruction. We also extracted emergent topics during the pandemic as an attempt to explore what students talked about and how they interacted with their peers. We extracted discussion forum posts between 2019 and 2020 from courses that have been offered repeatedly each term at a four-year university in the U.S. Our findings suggest that students exhibited higher social presence through increased social and affective language during remote instructions. We also identified emergent COVID-19 related discourse, which involved sharing personal experience with positive sentiments and expressing opinions on contemporary events. Our qualitative analysis further revealed that students showed rapport and empathy towrads others, and engaged in active sense making of the pandemic through engaging in critical discourse. Our study sheds lights on leveraging discussion forum to facilitate learner experiences and building classroom community in online courses. We further discussed the potential for conducting large-scale computational linguistic modeling on learner discourse and the role of artificial intelligence in deriving insights on learning behavior at scale to support remote teaching and learning.

Analyzing online forums to identify information needs and knowledge gaps in patients with left ventricular assist devices: a qualitative study.

To explore the knowledge and unmet informational needs of candidates for left ventricular assist devices (LVADs), as well as of patients, caregivers, and family members, by analyzing social media data from the MyLVAD.com website. A qualitative content analysis method was employed, systematically examining and categorizing forum posts and comments published on the MyLVAD.com website from March 2015 to February 2023. The data was collected using an automated script to retrieve threads from MyLVAD.com, focusing on genuine questions reflecting information and knowledge gaps. The study received approval from an ethics committee. The research team developed and continuously updated categorization matrices to organize information into categories and subcategories systematically. From 856 posts and comments analyzed, 435 contained questions representing informational needs, of which six main categories were identified: clothing, complications/adverse effects, LVAD pros and cons, self-care, therapy, and recent LVAD implantation. The self-care category, which includes managing the driveline site and understanding equipment functionality, was the most prominent, reflecting nearly half of the questions. Other significant areas of inquiry included complications/adverse effects and the pros and cons of LVAD. The analysis of social media data from MyLVAD.com reveals significant unmet informational needs among LVAD candidates, patients, and their support networks. Unlike traditional data, this social media-based research provides an unbiased view of patient conversations, offering valuable insights into their real-world concerns and knowledge gaps. The findings underscore the importance of tailored educational resources to address these unmet needs, potentially enhancing LVAD patient care.

The complex lived experience of schizophrenia diagnosis: a thematic analysis of online forum posts

Receiving a diagnosis of schizophrenia can be a life-changing experience with both negative and positive consequences. Previous research has identified important aspects of diagnosis, such as disagreement with the label, diagnostic shifts, stigma, and communication of the diagnosis. There is, however, relatively little research from the lived experience perspective. In this study, we investigated the lived experiences of receiving a schizophrenia diagnosis utilising a popular Reddit discussion forum. We used inductive thematic analysis to consider posts from 105 unique usernames and identified five themes. These were (i) Denial and acceptance; (ii) Stigma of diagnosis and selective disclosure; (iii) The complicated process of diagnosis; (iv) Losing, maintaining, and finding a sense of self; and (v) Receiving, finding, and providing support. Diagnosis is a complex personal experience where the same person can simultaneously accept and reject the diagnostic label. Greater attention is required to the lived experience of this diagnostic journey.

Characterizing and classifying developer forum posts with their intentions

Characterizing and classifying developer forum posts with their intentions

A content analysis of mothers' online communications of their partner's paternal postnatal depression

Research has increasingly focused on fathers' experiences of maternal postnatal depression (PND), but there is little information on mothers' experiences of paternal PND. Given that men are not routinely assessed or diagnosed with PND, the aim of this study was to understand how mothers describe their experiences of living with fathers who they presume to be experiencing paternal PND. This study used content analysis of online forum posts written by mothers on two UK parenting sites. Two themes are presented: mothers consider their partner to have PND due to his sudden behaviour changes and/or a lack of bonding with the baby; and women expect fathers to seek help when PND is suspected, with mothers feeling unsupported and in need of help. Mothers suspect paternal PND when they observe behavioural, emotional and character changes in fathers, as well as difficulties with father–infant bonding. Mothers seek advice on their situation based on a sense of helplessness. Among women more generally, fathers are expected to seek help for their mental health, usually from their GP. Conversely, women are advised to seek advice from their health visitor when they are concerned about their partner's mental health.

Towards Connectivism: Exploring Student Use of Online Learning Management Systems during the Covid-19 Pandemic

The COVID-19 pandemic has had a profound global impact on students, necessitating a shift towards fully remote e-learning via digital learning management systems (LMS). Despite this significant shift, there is a paucity of research exploring how students of varying academic performance levels engage with e-learning resources. This study addresses this gap by leveraging self-regulated learning theory and connectivism theory to analyze 129,567 activity logs from an Australian public research university. These logs encompass the online learning activities of 313 students and provide insights into their utilization of course files, discussion forums, gradebook, and online quizzes. Our analysis reveals significant differences in how students of varying academic performance levels engage with these resources. We found that higher-performing students tend to view course files, view and create forum posts, and access online quizzes more frequently, suggesting a positive correlation between active engagement with these resources and academic performance. However, the frequency of gradebook views did not significantly differ among students of different performance levels, indicating that grade tracking may not be a strong determinant of academic success. These findings underscore the importance of active engagement with online resources in academic success and provide valuable insights for educators and institutions in designing online courses and strategies to support student learning. The study also highlights the need for further research to understand the nuances of student engagement with e-learning resources. The implications of these findings extend beyond the pandemic, contributing to the broader discourse on e-learning and academic performance.