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.

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.

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.

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.

Can accurate demographic information about people who use prescription medications nonmedically be derived from Twitter?

Traditional substance use (SU) surveillance methods, such as surveys, incur substantial lags. Due to the continuously evolving trends in SU, insights obtained via such methods are often outdated. Social media-based sources have been proposed for obtaining timely insights, but methods leveraging such data cannot typically provide fine-grained statistics about subpopulations, unlike traditional approaches.

Automatic Detection of Twitter Users Who Express Chronic Stress Experiences via Supervised Machine Learning and Natural Language Processing.

Americans bear a high chronic stress burden, particularly during the COVID-19 pandemic. Although social media have many strengths to complement the weaknesses of conventional stress measures, including surveys, they have been rarely utilized to detect individuals self-reporting chronic stress. Thus, this study aimed to develop and evaluate an automatic system on Twitter to identify users who have self-reported chronic stress experiences.

The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges.

The COVID-19 pandemic is the most devastating public health crisis in at least a century and has affected the lives of billions of people worldwide in unprecedented ways. Compared to pandemics of this scale in the past, societies are now equipped with advanced technologies that can mitigate the impacts of pandemics if utilized appropriately. However, opportunities are currently not fully utilized, particularly at the intersection of data science and health.

Comparison of Pretraining Models and Strategies for Health-Related Social Media Text Classification.

Pretrained contextual language models proposed in the recent past have been reported to achieve state-of-the-art performances in many natural language processing (NLP) tasks, including those involving health-related social media data. We sought to evaluate the effectiveness of different pretrained transformer-based models for social media-based health-related text classification tasks. An additional objective was to explore and propose effective pretraining strategies to improve machine learning performance on such datasets and tasks.

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.

A comparison of few-shot and traditional named entity recognition models for medical text.

Many research problems involving medical texts have limited amounts of annotated data available (., expressions of rare diseases). Traditional supervised machine learning algorithms, particularly those based on deep neural networks, require large volumes of annotated data, and they underperform when only small amounts of labeled data are available.

Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use.

Background: The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study these aspects unobtrusively and at scale.

Methods: We applied a machine learning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users.

A Light-Weight Text Summarization System for Fast Access to Medical Evidence.

As the volume of published medical research continues to grow rapidly, staying up-to-date with the best-available research evidence regarding specific topics is becoming an increasingly challenging problem for medical experts and researchers. The current COVID19 pandemic is a good example of a topic on which research evidence is rapidly evolving. Automatic query-focused text summarization approaches may help researchers to swiftly review research evidence by presenting salient and query-relevant information from newly-published articles in a condensed manner.

Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts.

The capabilities of natural language processing (NLP) methods have expanded significantly in recent years, and progress has been particularly driven by advances in data science and machine learning. However, NLP is still largely underused in patient-oriented clinical research and care (POCRC). A key reason behind this is that clinical NLP methods are typically developed, optimized, and evaluated with narrowly focused data sets and tasks (eg, those for the detection of specific symptoms in free texts).

Automatic gender detection in Twitter profiles for health-related cohort studies.

Objective: Biomedical research involving social media data is gradually moving from population-level to targeted, cohort-level data analysis. Though crucial for biomedical studies, social media user's demographic information (eg, gender) is often not explicitly known from profiles. Here, we present an automatic gender classification system for social media and we illustrate how gender information can be incorporated into a social media-based health-related study.

Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid.

Background: The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers' perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter.

Objective: This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example.

Text classification models for the automatic detection of nonmedical prescription medication use from social media.

Background: Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.

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.