The metabolism and growth of web forums.
Ontology highlight
ABSTRACT:
SUBMITTER: Wu L
PROVIDER: S-EPMC4130495 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
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Project description:BackgroundWhile traditional signal detection methods in pharmacovigilance are based on spontaneous reports, the use of social media is emerging. The potential strength of Web-based data relies on their volume and real-time availability, allowing early detection of signals of disproportionate reporting (SDRs).ObjectiveThis study aimed (1) to assess the consistency of SDRs detected from patients' medical forums in France compared with those detected from the traditional reporting systems and (2) to assess the ability of SDRs in identifying earlier than the traditional reporting systems.MethodsMessages posted on patients' forums between 2005 and 2015 were used. We retained 8 disproportionality definitions. Comparison of SDRs from the forums with SDRs detected in VigiBase was done by describing the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, receiver operating characteristics curve, and the area under the curve (AUC). The time difference in months between the detection dates of SDRs from the forums and VigiBase was provided.ResultsThe comparison analysis showed that the sensitivity ranged from 29% to 50.6%, the specificity from 86.1% to 95.5%, the PPV from 51.2% to 75.4%, the NPV from 68.5% to 91.6%, and the accuracy from 68% to 87.7%. The AUC reached 0.85 when using the metric empirical Bayes geometric mean. Up to 38% (12/32) of the SDRs were detected earlier in the forums than that in VigiBase.ConclusionsThe specificity, PPV, and NPV were high. The overall performance was good, showing that data from medical forums may be a valuable source for signal detection. In total, up to 38% (12/32) of the SDRs could have been detected earlier, thus, ensuring the increased safety of patients. Further enhancements are needed to investigate the reliability and validation of patients' medical forums worldwide, the extension of this analysis to all possible drugs or at least to a wider selection of drugs, as well as to further assess performance against established signals.
| S-EPMC6280030 | biostudies-literature
Project description:BackgroundTo mitigate safety concerns, regulatory agencies must make informed decisions regarding drug usage and adverse drug events (ADEs). The primary pharmacovigilance data stem from spontaneous reports by health care professionals. However, underreporting poses a notable challenge within the current system. Explorations into alternative sources, including electronic patient records and social media, have been undertaken. Nevertheless, social media's potential remains largely untapped in real-world scenarios.ObjectiveThe challenge faced by regulatory agencies in using social media is primarily attributed to the absence of suitable tools to support decision makers. An effective tool should enable access to information via a graphical user interface, presenting data in a user-friendly manner rather than in their raw form. This interface should offer various visualization options, empowering users to choose representations that best convey the data and facilitate informed decision-making. Thus, this study aims to assess the potential of integrating social media into pharmacovigilance and enhancing decision-making with this novel data source. To achieve this, our objective was to develop and assess a pipeline that processes data from the extraction of web forum posts to the generation of indicators and alerts within a visual and interactive environment. The goal was to create a user-friendly tool that enables regulatory authorities to make better-informed decisions effectively.MethodsTo enhance pharmacovigilance efforts, we have devised a pipeline comprising 4 distinct modules, each independently editable, aimed at efficiently analyzing health-related French web forums. These modules were (1) web forums' posts extraction, (2) web forums' posts annotation, (3) statistics and signal detection algorithm, and (4) a graphical user interface (GUI). We showcase the efficacy of the GUI through an illustrative case study involving the introduction of the new formula of Levothyrox in France. This event led to a surge in reports to the French regulatory authority.ResultsBetween January 1, 2017, and February 28, 2021, a total of 2,081,296 posts were extracted from 23 French web forums. These posts contained 437,192 normalized drug-ADE couples, annotated with the Anatomical Therapeutic Chemical (ATC) Classification and Medical Dictionary for Regulatory Activities (MedDRA). The analysis of the Levothyrox new formula revealed a notable pattern. In August 2017, there was a sharp increase in posts related to this medication on social media platforms, which coincided with a substantial uptick in reports submitted by patients to the national regulatory authority during the same period.ConclusionsWe demonstrated that conducting quantitative analysis using the GUI is straightforward and requires no coding. The results aligned with prior research and also offered potential insights into drug-related matters. Our hypothesis received partial confirmation because the final users were not involved in the evaluation process. Further studies, concentrating on ergonomics and the impact on professionals within regulatory agencies, are imperative for future research endeavors. We emphasized the versatility of our approach and the seamless interoperability between different modules over the performance of individual modules. Specifically, the annotation module was integrated early in the development process and could undergo substantial enhancement by leveraging contemporary techniques rooted in the Transformers architecture. Our pipeline holds potential applications in health surveillance by regulatory agencies or pharmaceutical companies, aiding in the identification of safety concerns. Moreover, it could be used by research teams for retrospective analysis of events.
| S-EPMC11220433 | biostudies-literature
Project description:BackgroundThere is a general agreement on the importance of health-related quality of life (HRQoL). This type of information is becoming increasingly important for the value assessment of health technology assessment agencies in evaluating the benefits of new health technologies, including medicines. However, HRQoL data are often limited, and additional sources that provide this type of information may be helpful.ObjectiveWe aim to identify the HRQoL topics important to patients with melanoma based on web-based discussions on public social media forums.MethodsWe identified 3 public web-based forums from the United States and the United Kingdom, namely the Melanoma Patient Information Page, the Melanoma International Forum, and MacMillan. Their posts were randomly selected and coded using qualitative methods until saturation was reached.ResultsOf the posts assessed, 36.7% (150/409) of posts on Melanoma International Forum, 45.1% (198/439) on MacMillan, and 35.4% (128/362) on Melanoma Patient Information Page focused on HRQoL. The 2 themes most frequently mentioned were mental health and (un)certainty. The themes were constructed based on underlying and more detailed codes. Codes related to fear, worry and anxiety, uncertainty, and unfavorable effects were the most-often discussed ones.ConclusionsWeb-based forums are a valuable source for identifying relevant HRQoL aspects in patients with a given disease. These aspects could be cross-referenced with existing tools and they might improve the content validity of patient-reported outcome measures, including HRQoL questionnaires. In addition, web-based forums may provide health technology assessment agencies with a more holistic understanding of the external aspects affecting patient HRQoL. These aspects might support the value assessment of new health technologies and could therefore help inform topic prioritization as well as the scoping phase before any value assessment.
| S-EPMC8693198 | biostudies-literature
Project description:Due to the high stigma surrounding the Human Immunodeficiency Virus (HIV), people living with HIV (PLWH) often reach out peers over the Internet for emotional and social support. The purpose of this study was to assess the characteristics of PLWH who use HIV internet forums. A cross-sectional study was conducted using an online survey investigating demographic characteristics of PLWH, level of satisfaction of the HIV Internet forums, time living with HIV, forum users' anxiety levels, self-reported adherence to antiretroviral treatment (ART), and reasons for missing pills (n = 222). Logistic regression models were constructed to compare the use of general HIV forums with social networking sites, general HIV forums with group emails, and social networking sites with group emails. Two hundred and twenty-two patients responded to the survey. Social networking sites were used by recently diagnosed PLWH who were on antiretroviral treatment (ART) > 1 year. Young patients (≤ 40 years) and those diagnosed < 1 year before, tended to use social networking sites, while older patients (> 40 years), those diagnosed > 5 years, and from low- and middle-income countries, were more likely to use emailing lists. There was no significant difference between PLWH's adherence to treatment and anxiety levels and the usage of different Internet forums. PLWH's Internet resource choice varied depending on the availability of Internet and illness duration. Different segments of the population could be reached via social networking sites versus group emails to provide HIV information.
| S-EPMC5345957 | biostudies-literature
Project description:BackgroundMajor depressive disorder is a common mental disorder affecting 5% of adults worldwide. Early contact with health care services is critical for achieving accurate diagnosis and improving patient outcomes. Key symptoms of major depressive disorder (depression hereafter) such as cognitive distortions are observed in verbal communication, which can also manifest in the structure of written language. Thus, the automatic analysis of text outputs may provide opportunities for early intervention in settings where written communication is rich and regular, such as social media and web-based forums.ObjectiveThe objective of this study was 2-fold. We sought to gauge the effectiveness of different machine learning approaches to identify users of the mass web-based forum Reddit, who eventually disclose a diagnosis of depression. We then aimed to determine whether the time between a forum post and a depression diagnosis date was a relevant factor in performing this detection.MethodsA total of 2 Reddit data sets containing posts belonging to users with and without a history of depression diagnosis were obtained. The intersection of these data sets provided users with an estimated date of depression diagnosis. This derived data set was used as an input for several machine learning classifiers, including transformer-based language models (LMs).ResultsBidirectional Encoder Representations from Transformers (BERT) and MentalBERT transformer-based LMs proved the most effective in distinguishing forum users with a known depression diagnosis from those without. They each obtained a mean F1-score of 0.64 across the experimental setups used for binary classification. The results also suggested that the final 12 to 16 weeks (about 3-4 months) of posts before a depressed user's estimated diagnosis date are the most indicative of their illness, with data before that period not helping the models detect more accurately. Furthermore, in the 4- to 8-week period before the user's estimated diagnosis date, their posts exhibited more negative sentiment than any other 4-week period in their post history.ConclusionsTransformer-based LMs may be used on data from web-based social media forums to identify users at risk for psychiatric conditions such as depression. Language features picked up by these classifiers might predate depression onset by weeks to months, enabling proactive mental health care interventions to support those at risk for this condition.
| S-EPMC7614849 | biostudies-literature
Project description:Histone acetylation is sensitive to the availability of acetyl-CoA. However, the extent to which metabolic alterations in cancer cells impact tumor histone acetylation has been unclear. Here, we discuss our recent findings that oncogenic AKT1 activation regulates histone acetylation levels in tumors through regulation of acetyl-CoA metabolism.
| S-EPMC4904988 | biostudies-other
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:BackgroundUpper and lower blepharoplasty are among the most common procedures in aesthetic surgery and are often emotionally laden due to the subjective nature of outcomes and implications with beauty and self-identity. This article capitalizes on the increasing wealth of patient-provided health information online and is the first to analyze the emotions surrounding blepharoplasty discussions in an open internet health forum, MedHelp.MethodsWe used Python to scrape MedHelp for threads that contained "blepharoplasty" and then used IBM Watson Natural Language Understanding to perform sentiment analyses, calculating a general sentiment score (-1 to +1) as well as emotion scores for anger, sadness, joy, fear, and disgust (0 to 1) for posts and keywords contained within the posts. Keywords were then manually grouped into five distinct clinical categories: symptoms, doctor, treatment, medication, and body.ResultsWe collected 52 threads containing "blepharoplasty," yielding 154 posts and 1365 keywords. The average sentiment score was negative among all posts (-0.15) and keywords (-0.30). Among all posts and keywords, sadness had the highest score and disgust had the lowest score.ConclusionsFear and sadness are the predominant emotions for blepharoplasty patients online, and the most negative symptoms cited are not ones that surgeons typically expect.
| S-EPMC9038503 | biostudies-literature
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:BackgroundIn 2016, 44,965 people in the United States died by suicide. It is common to see people with suicidal ideation seek help or leave suicide notes on social media before attempting suicide. Many prefer to express their feelings with longer passages on forums such as Reddit and blogs. Because these expressive posts follow regular language patterns, potential suicide attempts can be prevented by detecting suicidal posts as they are written.ObjectiveThis study aims to build a classifier that differentiates suicidal and nonsuicidal forum posts via text mining methods applied on post titles and bodies.MethodsA total of 508,398 Reddit posts longer than 100 characters and posted between 2008 and 2016 on SuicideWatch, Depression, Anxiety, and ShowerThoughts subreddits were downloaded from the publicly available Reddit dataset. Of these, 10,785 posts were randomly selected and 785 were manually annotated as suicidal or nonsuicidal. Features were extracted using term frequency-inverse document frequency, linguistic inquiry and word count, and sentiment analysis on post titles and bodies. Logistic regression, random forest, and support vector machine (SVM) classification algorithms were applied on resulting corpus and prediction performance is evaluated.ResultsThe logistic regression and SVM classifiers correctly identified suicidality of posts with 80% to 92% accuracy and F1 score, respectively, depending on different data compositions closely followed by random forest, compared to baseline ZeroR algorithm achieving 50% accuracy and 66% F1 score.ConclusionsThis study demonstrated that it is possible to detect people with suicidal ideation on online forums with high accuracy. The logistic regression classifier in this study can potentially be embedded on blogs and forums to make the decision to offer real-time online counseling in case a suicidal post is being written.
| S-EPMC6035349 | biostudies-literature