Emerging Trends of Self-Harm Using Sodium Nitrite in an Online Suicide Community: Observational Study Using Natural Language Processing Analysis.

Background: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, "Sanctioned Suicide," which is a primary source of information on the use and procurement of SN. Objective: This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN. Methods: We collected all publicly available from the site's inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN. Results: Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere. Conclusions: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.

Self-reported Xylazine Experiences: A Mixed-methods Study of Reddit Subscribers.

OBJECTIVES: Xylazine is an α 2 -agonist increasingly prevalent in the illicit drug supply. Our objectives were to curate information about xylazine through social media from people who use drugs (PWUDs). Specifically, we sought to answer the following: (1) What are the demographics of Reddit subscribers reporting exposure to xylazine? (2) Is xylazine a desired additive? And (3) what adverse effects of xylazine are PWUDs experiencing? METHODS: Natural language processing (NLP) was used to identify mentions of "xylazine" from posts by Reddit subscribers who also posted on drug-related subreddits. Posts were qualitatively evaluated for xylazine-related themes. A survey was developed to gather additional information about the Reddit subscribers. This survey was posted on subreddits that were identified by NLP to contain xylazine-related discussions from March 2022 to October 2022. RESULTS: Seventy-six posts were extracted via NLP from 765,616 posts by 16,131 Reddit subscribers (January 2018 to August 2021). People on Reddit described xylazine as an unwanted adulterant in their opioid supply. Sixty-one participants completed the survey. Of those who disclosed their location, 25 of 50 participants (50%) reported locations in the Northeastern United States. The most common route of xylazine use was intranasal use (57%). Thirty-one of 59 (53%) reported experiencing xylazine withdrawal. Frequent adverse events reported were prolonged sedation (81%) and increased skin wounds (43%). CONCLUSIONS: Among respondents on these Reddit forums, xylazine seems to be an unwanted adulterant. People who use drugs may be experiencing adverse effects such as prolonged sedation and xylazine withdrawal. This seemed to be more common in the Northeast.

An aspect-level sentiment analysis dataset for therapies on Twitter.

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.

A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.

Background: Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications. Methods: We collected Twitter data for four medications-fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall® (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication. Results: The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities. Conclusions: NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.

Generalizable Natural Language Processing Framework for Migraine Reporting from Social Media.

Migraine is a highly prevalent and disabling neurological disorder. However, information about migraine management in real-world settings is limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by those with migraine; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts, and used them for training and evaluating supervised machine learning methods. Our best system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

Automatic Detection of Intimate Partner Violence Victims from Social Media for Proactive Delivery of Support.

Social media platforms are increasingly being used by intimate partner violence (IPV) victims to share experiences and seek support. If such information is automatically curated, it may be possible to conduct social media based surveillance and even design interventions over such platforms. In this paper, we describe the development of a supervised classification system that automatically characterizes IPV-related posts on the social network Reddit. We collected data from four IPV-related subreddits and manually annotated the data to indicate whether a post is a self-report of IPV or not. Using the annotated data (N=289), we trained, evaluated, and compared supervised machine learning systems. A transformer-based classifier, RoBERTa, obtained the best classification performance with overall accuracy of 78% and IPV-self-report class 𝐹1 -score of 0.67. Post-classification error analyses revealed that misclassifications often occur for posts that are very long or are non-first-person reports of IPV. Despite the relatively small annotated data, our classification methods obtained promising results, indicating that it may be possible to detect and, hence, provide support to IPV victims over Reddit.

The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection.

Background: Social media has served as a lucrative platform for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the US Food and Drug Administration (FDA). While social media continues to serve as the primary platform for the promotion of such fraudulent products, it also presents the opportunity to identify these products early by using effective social media mining methods. Objective: Our objectives were to (1) create a data set of fraudulent COVID-19 products that can be used for future research and (2) propose a method using data from Twitter for automatically detecting heavily promoted COVID-19 products early. Methods: We created a data set from FDA-issued warnings during the early months of the COVID-19 pandemic. We used natural language processing and time-series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. We also performed a brief manual analysis of chatter associated with 2 products to characterize their contents. Results: FDA warning issue dates ranged from March 6, 2020, to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19 and December 31, 2020, which are all publicly available, our unsupervised approach detected 34 out of 44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6 (13.6%) within a week following the corresponding FDA letters. Content analysis revealed misinformation, information, political, and conspiracy theories to be prominent topics. Conclusions: Our proposed method is simple, effective, easy to deploy, and does not require high-performance computing machinery unlike deep neural network-based methods. The method can be easily extended to other types of signal detection from social media data. The data set may be used for future research and the development of more advanced methods.

An analysis of cannabinoid hyperemesis syndrome Reddit posts and themes.

INTRODUCTION: Reddit hosts a large active community of members dedicated to the discussion of cannabinoid hyperemesis syndrome. We sought to describe common themes discussed and the most frequently mentioned triggers and therapies for cannabinoid hyperemesis syndrome exacerbations in the Reddit online community. METHODS: Data collected from six subreddits were filtered using natural language processing to curate posts referencing cannabinoid hyperemesis syndrome. Based on a manual review of posts, common themes were identified. A machine learning model was trained using the manually categorized data to automatically classify the themes for the rest of the posts so that their distributions could be quantified. RESULTS: From August 2018 to November 2022, 2683 unique posts were collected. Thematic analysis resulted in five overall themes: cannabinoid hyperemesis syndrome-related science; symptom timing; cannabinoid hyperemesis syndrome treatment and prevention; cannabinoid hyperemesis syndrome diagnosis and education; and health impacts. Additionally, 447 trigger and 664 therapy-related posts were identified. The most commonly mentioned triggers for cannabinoid hyperemesis syndrome episodes included: food and drink (n = 62), cannabinoids (n = 45), mental health (e.g., stress, anxiety) (n = 27), and alcohol (n = 22). Most commonly mentioned cannabinoid hyperemesis syndrome therapies included: hot water/bathing (n = 62), hydration (n = 60), antiemetics (n = 42), food and drink (n = 38), gastrointestinal medications (n = 38), behavioral therapies (e.g., meditation, yoga) (n = 35), and capsaicin (n = 29). DISCUSSION: Reddit posts for cannabinoid hyperemesis syndrome provide a valuable source of community discussion and individual reports of people experiencing cannabinoid hyperemesis syndrome. Mental health and alcohol were frequently reported triggers within the posts but are not often identified in the literature. While many of the therapies mentioned are well documented, behavioral responses such as meditation and yoga have not been explored by the scientific literature. CONCLUSIONS: Knowledge shared via online social media platforms contains detailed information on self-reported cannabinoid hyperemesis syndrome disease and management experiences, which could serve as valuable data for the development of treatment strategies. Further longitudinal studies in patients with cannabinoid hyperemesis syndrome are needed to corroborate these findings.

Characteristics of Intimate Partner Violence and Survivor's Needs During the COVID-19 Pandemic: Insights From Subreddits Related to Intimate Partner Violence.

Intimate partner violence (IPV) increased during the COVID-19 pandemic. Collecting actionable IPV-related data from conventional sources (e.g., medical records) was challenging during the pandemic, generating a need to obtain relevant data from non-conventional sources, such as social media. Social media, like Reddit, is a preferred medium of communication for IPV survivors to share their experiences and seek support with protected anonymity. Nevertheless, the scope of available IPV-related data on social media is rarely documented. Thus, we examined the availability of IPV-related information on Reddit and the characteristics of the reported IPV during the pandemic. Using natural language processing, we collected publicly available Reddit data from four IPV-related subreddits between January 1, 2020 and March 31, 2021. Of 4,000 collected posts, we randomly sampled 300 posts for analysis. Three individuals on the research team independently coded the data and resolved the coding discrepancies through discussions. We adopted quantitative content analysis and calculated the frequency of the identified codes. 36% of the posts (n = 108) constituted self-reported IPV by survivors, of which 40% regarded current/ongoing IPV, and 14% contained help-seeking messages. A majority of the survivors' posts reflected psychological aggression, followed by physical violence. Notably, 61.4% of the psychological aggression involved expressive aggression, followed by gaslighting (54.3%) and coercive control (44.3%). Survivors' top three needs during the pandemic were hearing similar experiences, legal advice, and validating their feelings/reactions/thoughts/actions. Albeit limited, data from bystanders (survivors' friends, family, or neighbors) were also available. Rich data reflecting IPV survivors' lived experiences were available on Reddit. Such information will be useful for IPV surveillance, prevention, and intervention.

Self-reported Xylazine Experiences: A Mixed Methods Study of Reddit Subscribers.

Objectives: Xylazine is an alpha-2 agonist increasingly prevalent in the illicit drug supply. Our objectives were to curate information about xylazine through social media from People Who Use Drugs (PWUDs). Specifically, we sought to answer the following: 1) what are the demographics of Reddit subscribers reporting exposure to xylazine? 2) is xylazine a desired additive? and 3) what adverse effects of xylazine are PWUDs experiencing? Methods: Natural Language Processing (NLP) was used to identify mentions of "xylazine" from posts by Reddit subscribers who also posted on drug-related subreddits. Posts were qualitatively evaluated for xylazine-related themes. A survey was developed to gather additional information about the Reddit subscribers. This survey was posted on subreddits that were identified by NLP to contain xylazine-related discussions from March 2022 to October 2022. Results: 76 posts mentioning xylazine were extracted via NLP from 765,616 posts by 16,131 Reddit subscribers (January 2018 to August 2021). People on Reddit described xylazine as an unwanted adulterant in their opioid supply. 61 participants completed the survey. Of those that disclosed their location, 25/50 (50%) participants reported locations in the Northeastern United States. The most common eoute of xylazine use was intranasal use (57%). 31/59 (53%) reported experiencing xylazine withdrawal. Frequent adverse events reported were prolonged sedation (81%) and increased skin wounds (43%). Conclusions: Among respondents on these Reddit forums, xylazine appears to be an unwanted adulterant. PWUDs may be experiencing adverse effects such as prolonged sedation and xylazine withdrawal. This appeared to be more common in the Northeast.

#ChronicPain: Automated Building of a Chronic Pain Cohort from Twitter Using Machine Learning.

Background: Due to the high burden of chronic pain, and the detrimental public health consequences of its treatment with opioids, there is a high-priority need to identify effective alternative therapies. Social media is a potentially valuable resource for knowledge about self-reported therapies by chronic pain sufferers. Methods: We attempted to (a) verify the presence of large-scale chronic pain-related chatter on Twitter, (b) develop natural language processing and machine learning methods for automatically detecting self-disclosures, (c) collect longitudinal data posted by them, and (d) semiautomatically analyze the types of chronic pain-related information reported by them. We collected data using chronic pain-related hashtags and keywords and manually annotated 4,998 posts to indicate if they were self-reports of chronic pain experiences. We trained and evaluated several state-of-the-art supervised text classification models and deployed the best-performing classifier. We collected all publicly available posts from detected cohort members and conducted manual and natural language processing-driven descriptive analyses. Results: Interannotator agreement for the binary annotation was 0.82 (Cohen's kappa). The RoBERTa model performed best (F1 score: 0.84; 95% confidence interval: 0.80 to 0.89), and we used this model to classify all collected unlabeled posts. We discovered 22,795 self-reported chronic pain sufferers and collected over 3 million of their past posts. Further analyses revealed information about, but not limited to, alternative treatments, patient sentiments about treatments, side effects, and self-management strategies. Conclusion: Our social media based approach will result in an automatically growing large cohort over time, and the data can be leveraged to identify effective opioid-alternative therapies for diverse chronic pain types.

Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance.

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944; p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

Natural language model for automatic identification of Intimate Partner Violence reports from Twitter.

Intimate partner violence (IPV) is a preventable public health problem that affects millions of people worldwide. Approximately one in four women are estimated to be or have been victims of severe violence at some point in their lives, irrespective of age, ethnicity, and economic status. Victims often report IPV experiences on social media, and automatic detection of such reports via machine learning may enable improved surveillance and targeted distribution of support and/or interventions for those in need. However, no artificial intelligence systems for automatic detection currently exists, and we attempted to address this research gap. We collected posts from Twitter using a list of IPV-related keywords, manually reviewed subsets of retrieved posts, and prepared annotation guidelines to categorize tweets into IPV-report or non-IPV-report. We annotated 6,348 tweets in total, with the inter-annotator agreement (IAA) of 0.86 (Cohen's kappa) among 1,834 double-annotated tweets. The class distribution in the annotated dataset was highly imbalanced, with only 668 posts (~11%) labeled as IPV-report. We then developed an effective natural language processing model to identify IPV-reporting tweets automatically. The developed model achieved classification F1-scores of 0.76 for the IPV-report class and 0.97 for the non-IPV-report class. We conducted post-classification analyses to determine the causes of system errors and to ensure that the system did not exhibit biases in its decision making, particularly with respect to race and gender. Our automatic model can be an essential component for a proactive social media-based intervention and support framework, while also aiding population-level surveillance and large-scale cohort studies.

Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource.

OBJECTIVE: To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, and create a symptom lexicon for future research. MATERIALS AND METHODS: We retrieved tweets using COVID-19-related keywords, and performed semiautomatic filtering to curate self-reports of positive-tested users. We extracted COVID-19-related symptoms mentioned by the users, mapped them to standard concept IDs in the Unified Medical Language System, and compared the distributions to those reported in early studies from clinical settings. RESULTS: We identified 203 positive-tested users who reported 1002 symptoms using 668 unique expressions. The most frequently-reported symptoms were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), and dyspnea (36.3%) amongst users who reported at least 1 symptom. Mild symptoms, such as anosmia (28.7%) and ageusia (28.1%), were frequently reported on Twitter, but not in clinical studies. CONCLUSION: The spectrum of COVID-19 symptoms identified from Twitter may complement those identified in clinical settings.