CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums
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ABSTRACT:
SUBMITTER: Lauw H
PROVIDER: S-EPMC7206158 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
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Project description:ObjectivesThe research sought to determine if the health advice provided in online discussion forms aimed at parents of young children is accurate and in agreement with evidence found in evidence-based resources and to discover whether or not these forums are an avenue for misinformation.MethodsTo determine which online forums to use, Google was searched using five common childhood ailments. Forums that appeared five or more times in the first five pages of the Google search for each question were considered. Of these forums, those that met the inclusion criteria were used. Data from a six-month time period was collected and categorized from the discussion forums to analyze the advice being provided about common childhood ailments. Evidence-based resources were used to analyze the accuracy of the advice provided.ResultsTwo discussion forums were chosen for analysis. Seventy-four questions from one and 131 questions from the other were health related. Data were not analyzed together. Of the health-related questions on the 2 forums, 65.5% and 51.8%, respectively, provided some type of advice. Of the advice provided, 54.1% and 47.2%, respectively, agreed with the evidence provided in evidence-based resources. A further 16.2% and 6.3% was refuted or was somewhat refuted by the evidence found in evidence-based resources.ConclusionWhile roughly half of the health-related advice provided in online discussion forums aimed at parents of young children is accurate, only a small portion of the advice is incorrect; therefore, these sources are not a major concern for the spread of misinformation.
| S-EPMC6148614 | biostudies-literature
Project description:Increasingly, individuals experiencing loneliness are seeking support on online forums-some of which focus specifically on discussions around loneliness (loneliness forums); loneliness may influence how these individuals communicate in other online forums not focused on loneliness (non-loneliness forums). In order to provide effective and appropriate online interventions around loneliness, it is important to understand how users who publish posts in a loneliness forum communicate in the loneliness forum and non-loneliness forums they belong to. In this paper, using language features, the following analyses are conducted: (1) Posts published on an online loneliness forum on Reddit, /r/Lonely are compared to posts (published by the same users and around the same time period) on two Reddit online forums i.e. an advice seeking forum, /r/AskReddit and a forum focused on discussions around depression (depression forum), /r/depression. (2) Interventions related to loneliness may vary depending on if an individual is lonely and depressed or lonely but not depressed; language use differences in posts published in /r/Lonely by the following set of users are identified: (a) users who post in both /r/Lonely and a depression forum and (b) users who post in /r/Lonely but not in the depression forum. The findings from this work gain new insights, for example: (i) /r/Lonely users tend to seek advice/ask questions related to relationships in the advice seeking forum, /r/AskReddit and (ii) users who are members of the loneliness forum but not the depression forum tend to publish posts (on the loneliness forum) on topic themes related to work/job, however, those who are members of the loneliness and depression forums tend to use more words associated with anger, negation, death, and post on topic themes related to affection relative to relationships in their loneliness forum posts. Some of the findings from this work also align with prior work e.g. users who express loneliness in online forums tend to make more reference to self. These findings aid in gaining insights into how users communicate on these forums and their support needs, thereby informing loneliness interventions.
| S-EPMC8460046 | biostudies-literature
Project description:OBJECTIVE:The development of cynicism in medicine, defined as a decline in empathy and emotional neutralisation during medical training, is a significant concern for medical educators. We sought to use online medical student discussion groups to provide insight into how cynicism in medicine is perceived, the consequences of cynicism on medical trainee development and potential links between the hidden curriculum and cynicism. SETTING:Online analysis of discussion topics in Premed101 (Canadian) and Student Doctor Network (American) forums. PARTICIPANTS:511 posts from seven discussion topics were analysed using NVivo 11. Participants in the forums included medical students, residents and practising physicians. METHODS:Inductive content analysis was used to develop a data-driven coding scheme that evolved throughout the analysis. Measures were taken to ensure the trustworthiness of findings, including duplicate independent coding of a sub-sample of posts and the maintenance of an audit trail. RESULTS:Medical students, residents and practising physicians participating in the discussion forums engaged in discourse about cynicism and highlighted themes of the hidden curriculum resulting in cynicism. These included the progression of cynicism over the course of medical training as a coping mechanism; the development of challenging work environments due to factors such as limited support, hierarchical demands and long work hours; and the challenge of initiating change due to the tolerance of unprofessionalism and the highly stressful nature of medicine. CONCLUSION:Our unique study of North American medical discussion posts demonstrates that cynicism develops progressively and is compounded by conflicts between the hidden and formal curriculum. Online discussion groups are a novel resource to provide insight into the culture of medical training.
| S-EPMC6196850 | biostudies-other
Project description:INTRODUCTION: The world wide web opens up new opportunities to interconnect electronic and classroom teaching and to promote active student participation. In this project article we describe the use of internet discussion forums as part of a student-centred teaching concept of pharmacology and discuss its advantages and disadvantages based on evaluation data and current literature. METHODS AND RESULTS: Final year medical students at the Technische Universität München (Munich, Germany) with the elective pharmacology moderated an internet forum that allowed all students to discuss pharmacology-related questions. Evaluation results of forum participants and elective students demonstrated a learning benefit of internet forums in pharmacology teaching. CONCLUSION: Internet discussion forums offer an easy-to-implement and effective way to actively engage students and increase the learning benefit of electronic and classroom teaching in pharmacology.
| S-EPMC3589684 | biostudies-other
Project description: Not available
| S-EPMC5298573 | biostudies-literature
Project description:Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models.
| S-EPMC7924670 | biostudies-literature
Project description:Recently, many users prefer online shopping to purchase items from the web. Shopping websites allow customers to submit comments and provide their feedback for the purchased products. Opinion mining and sentiment analysis are used to analyze products' comments to help sellers and purchasers decide to buy products or not. However, the nature of online comments affects the performance of the opinion mining process because they may contain negation words or unrelated aspects to the product. To address these problems, a semantic-based aspect level opinion mining (SALOM) model is proposed. The SALOM extracts the product aspects based on the semantic similarity and classifies the comments. The proposed model considers the negation words and other types of product aspects such as aspects' synonyms, hyponyms, and hypernyms to improve the accuracy of classification. Three different datasets are used to evaluate the proposed SALOM. The experimental results are promising in terms of Precision, Recall, and F-measure. The performance reaches 94.8% precision, 93% recall, and 92.6% f-measure.
| S-EPMC8237320 | biostudies-literature
Project description:ObjectivesAs part of a wider study, our aim was to elicit perspectives of people with congenital heart disease (CHD) and/or their parents/carers about their experiences of healthcare and what is important to them when receiving care.Design and settingA qualitative study involving a series of closed, asynchronous, online discussion forums underpinned by an interpretivist framework and set up and moderated by three patient charities via their Facebook pages.ParticipantsPeople with CHD and parents/carers of people with CHD from the UK.ResultsFive forums were run for 12-24 weeks across the three charities, and 343 participants signed up to the forums. Four linked themes related to processes of care were identified following thematic analysis of the transcripts: relationships and communication; access and coordination; experience of discrete episodes of care and psychological support. These impacted how care was experienced and, for some patients, outcomes of CHD and its treatment as well as broader health outcomes. In addition, context relating to stages of the patient journey was described, together with patient-related factors such as patients' knowledge and expertise in their own condition.ConclusionsPeople with CHD and their parents/carers want individualised, person-centred care delivered within an appropriately resourced, multidisciplinary service. Although examples of excellent care were provided it is evident that, from the perspective of patients and parents/carers, some National Health Service Standards for people with CHD were not being met.
| S-EPMC11218017 | biostudies-literature
Project description:The dataset described is an aspect-level sentiment analysis dataset for therapies, including medication, behavioral and other therapies, created by leveraging user-generated text from Twitter. The dataset was constructed by collecting Twitter posts using keywords associated with the therapies (often referred to as treatments). Subsequently, subsets of the collected posts were manually reviewed, and annotation guidelines were developed to categorize the posts as positive, negative, or neutral. The dataset contains a total of 5364 posts mentioning 32 therapies. These posts are further categorized manually into 998 (18.6%) positive, 619 (11.5%) negatives, and 3747 (69.9%) neutral sentiments. The inter-annotation agreement for the dataset was evaluated using Cohen's Kappa score, achieving an 0.82 score. The potential use of this dataset lies in the development of automatic systems that can detect users' sentiments toward therapies based on their posts. While there are other sentiment analysis datasets available, this is the first that encodes sentiments associated with specific therapies. Researchers and developers can utilize this dataset to train sentiment analysis models, natural language processing algorithms, or machine learning systems to accurately identify and analyze the sentiments expressed by consumers on social media platforms like Twitter.
| S-EPMC10558704 | biostudies-literature
Project description:The dataset contains full-page screenshots of homepages of commercial banking (N = 1033), online shopping (N = 1064), and university (N = 1059) websites, as well as the raw and aggregated user ratings of webpage design prototypicality, visual aesthetics, perceived usability and trustworthiness, and user demographic information. Design prototypicality was measured with three items, including typicality, exemplar goodness, and family resemblance, whereas the other design dimensions were measured with a single item each. Amazon Mechanical Turk crowdworkers (N = 3319 rating sessions) provided their demographic data and rated the homepages online. The demographic data have been anonymized, with generated unique participant IDs replacing MTurk crowdworker IDs. The screenshots are identified with generated IDs to provide partial anonymization for the websites, limiting their potential misuse outside design-related or user experience-related academic research. The raw rating data contain all collected ratings, whereas the aggregated data contain the per-webpage, per-dimension ratings derived solely from the ratings of study-compliant crowdworkers. The non-compliance among crowdworkers was detected based on several indicators, including rate-rerate consistency, seen-unseen webpage recognition, free-form feedback analyses, demographic data analyses, and other indicators. Future research could utilize the dataset either in user studies that require full-page webpages as stimuli, e.g., studies on the determinants of first impression, user preference, and user experience, or in computational research on web design, including computational aesthetics, as this type of research requires a large number of user-rated webpages, which this dataset provides.
| S-EPMC10823051 | biostudies-literature