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  1. Article ; Online: Twitter social mobility data reveal demographic variations in social distancing practices during the COVID-19 pandemic.

    Xu, Paiheng / Broniatowski, David A / Dredze, Mark

    Scientific reports

    2024  Volume 14, Issue 1, Page(s) 1165

    Abstract: The COVID-19 pandemic demonstrated the importance of social distancing practices to stem the spread of the virus. However, compliance with public health guidelines was mixed. Understanding what factors are associated with differences in compliance can ... ...

    Abstract The COVID-19 pandemic demonstrated the importance of social distancing practices to stem the spread of the virus. However, compliance with public health guidelines was mixed. Understanding what factors are associated with differences in compliance can improve public health messaging since messages could be targeted and tailored to different population segments. We utilize Twitter data on social mobility during COVID-19 to reveal which populations practiced social distancing and what factors correlated with this practice. We analyze correlations between demographic and political affiliation with reductions in physical mobility measured by public geolocation tweets. We find significant differences in mobility reduction between these groups in the United States. We observe that males, Asian and Latinx individuals, older individuals, Democrats, and people from higher population density states exhibited larger reductions in movement. Furthermore, our study also unveils meaningful insights into the interactions between different groups. We hope these findings will provide evidence to support public health policy-making.
    MeSH term(s) Male ; Humans ; United States/epidemiology ; COVID-19/epidemiology ; COVID-19/prevention & control ; Physical Distancing ; SARS-CoV-2 ; Pandemics/prevention & control ; Social Media ; Social Mobility ; Demography
    Language English
    Publishing date 2024-01-12
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-024-51555-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Generalizing Fairness using Multi-Task Learning without Demographic Information

    Aguirre, Carlos / Dredze, Mark

    2023  

    Abstract: To ensure the fairness of machine learning systems, we can include a fairness loss during training based on demographic information associated with the training data. However, we cannot train debiased classifiers for most tasks since the relevant ... ...

    Abstract To ensure the fairness of machine learning systems, we can include a fairness loss during training based on demographic information associated with the training data. However, we cannot train debiased classifiers for most tasks since the relevant datasets lack demographic annotations. Can we utilize demographic data for a related task to improve the fairness of our target task? We demonstrate that demographic fairness objectives transfer to new tasks trained within a multi-task framework. We adapt a single-task fairness loss to a multi-task setting to exploit demographic labels from a related task in debiasing a target task. We explore different settings with missing demographic data and show how our loss can improve fairness even without in-task demographics, across various domains and tasks.
    Keywords Computer Science - Machine Learning ; Computer Science - Computers and Society
    Publishing date 2023-05-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Machine-Made Empathy? Why Medicine Still Needs Humans-Reply.

    Ayers, John W / Dredze, Mark / Smith, Davey M

    JAMA internal medicine

    2023  Volume 183, Issue 11, Page(s) 1279–1280

    MeSH term(s) Humans ; Empathy ; Medicine
    Language English
    Publishing date 2023-09-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2699338-7
    ISSN 2168-6114 ; 2168-6106
    ISSN (online) 2168-6114
    ISSN 2168-6106
    DOI 10.1001/jamainternmed.2023.4392
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: The case for social media standards on suicide.

    Hoops, Katherine / Nestadt, Paul S / Dredze, Mark

    The lancet. Psychiatry

    2023  Volume 10, Issue 9, Page(s) 662–664

    MeSH term(s) Humans ; Social Media ; Suicide ; Suicide Prevention
    Language English
    Publishing date 2023-07-12
    Publishing country England
    Document type Journal Article
    ISSN 2215-0374
    ISSN (online) 2215-0374
    DOI 10.1016/S2215-0366(23)00222-5
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  5. Article ; Online: Using Large Language Models to Support Content Analysis: A Case Study of ChatGPT for Adverse Event Detection.

    Leas, Eric C / Ayers, John W / Desai, Nimit / Dredze, Mark / Hogarth, Michael / Smith, Davey M

    Journal of medical Internet research

    2024  Volume 26, Page(s) e52499

    Abstract: This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT's performance with human annotators' in ... ...

    Abstract This study explores the potential of using large language models to assist content analysis by conducting a case study to identify adverse events (AEs) in social media posts. The case study compares ChatGPT's performance with human annotators' in detecting AEs associated with delta-8-tetrahydrocannabinol, a cannabis-derived product. Using the identical instructions given to human annotators, ChatGPT closely approximated human results, with a high degree of agreement noted: 94.4% (9436/10,000) for any AE detection (Fleiss κ=0.95) and 99.3% (9931/10,000) for serious AEs (κ=0.96). These findings suggest that ChatGPT has the potential to replicate human annotation accurately and efficiently. The study recognizes possible limitations, including concerns about the generalizability due to ChatGPT's training data, and prompts further research with different models, data sources, and content analysis tasks. The study highlights the promise of large language models for enhancing the efficiency of biomedical research.
    MeSH term(s) Humans ; Social Media/statistics & numerical data ; Dronabinol/adverse effects ; Natural Language Processing
    Chemical Substances Dronabinol (7J8897W37S)
    Language English
    Publishing date 2024-05-02
    Publishing country Canada
    Document type Journal Article
    ZDB-ID 2028830-X
    ISSN 1438-8871 ; 1438-8871
    ISSN (online) 1438-8871
    ISSN 1438-8871
    DOI 10.2196/52499
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Book ; Online: The Problem of Semantic Shift in Longitudinal Monitoring of Social Media

    Harrigian, Keith / Dredze, Mark

    A Case Study on Mental Health During the COVID-19 Pandemic

    2022  

    Abstract: Social media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of these tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have shown the ... ...

    Abstract Social media allows researchers to track societal and cultural changes over time based on language analysis tools. Many of these tools rely on statistical algorithms which need to be tuned to specific types of language. Recent studies have shown the absence of appropriate tuning, specifically in the presence of semantic shift, can hinder robustness of the underlying methods. However, little is known about the practical effect this sensitivity may have on downstream longitudinal analyses. We explore this gap in the literature through a timely case study: understanding shifts in depression during the course of the COVID-19 pandemic. We find that inclusion of only a small number of semantically-unstable features can promote significant changes in longitudinal estimates of our target outcome. At the same time, we demonstrate that a recently-introduced method for measuring semantic shift may be used to proactively identify failure points of language-based models and, in turn, improve predictive generalization.

    Comment: Accepted to the 14th International ACM Conference on Web Science in 2022 (WebSci '22)
    Keywords Computer Science - Computation and Language ; Computer Science - Computers and Society
    Publishing date 2022-06-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Then and Now

    Harrigian, Keith / Dredze, Mark

    Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses

    2022  

    Abstract: Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade. ... ...

    Abstract Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade. However, psychiatric conditions are dynamic; a prior depression diagnosis may no longer be indicative of an individual's mental health, either due to treatment or other mitigating factors. We ask: to what extent are self-disclosures of mental health diagnoses actually relevant over time? We analyze recent activity from individuals who disclosed a depression diagnosis on social media over five years ago and, in turn, acquire a new understanding of how presentations of mental health status on social media manifest longitudinally. We also provide expanded evidence for the presence of personality-related biases in datasets curated using self-disclosed diagnoses. Our findings motivate three practical recommendations for improving mental health datasets curated using self-disclosed diagnoses: 1) Annotate diagnosis dates and psychiatric comorbidities; 2) Sample control groups using propensity score matching; 3) Identify and remove spurious correlations introduced by selection bias.

    Comment: Accepted to the Eighth Workshop on Computational Linguistics and Clinical Psychology (CLPsych) at NAACL
    Keywords Computer Science - Machine Learning ; Computer Science - Computation and Language ; Computer Science - Computers and Society
    Subject code 121
    Publishing date 2022-06-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models

    Aguirre, Carlos / Sasse, Kuleen / Cachola, Isabel / Dredze, Mark

    2023  

    Abstract: Recently, work in NLP has shifted to few-shot (in-context) learning, with large language models (LLMs) performing well across a range of tasks. However, while fairness evaluations have become a standard for supervised methods, little is known about the ... ...

    Abstract Recently, work in NLP has shifted to few-shot (in-context) learning, with large language models (LLMs) performing well across a range of tasks. However, while fairness evaluations have become a standard for supervised methods, little is known about the fairness of LLMs as prediction systems. Further, common standard methods for fairness involve access to models weights or are applied during finetuning, which are not applicable in few-shot learning. Do LLMs exhibit prediction biases when used for standard NLP tasks? In this work, we explore the effect of shots, which directly affect the performance of models, on the fairness of LLMs as NLP classification systems. We consider how different shot selection strategies, both existing and new demographically sensitive methods, affect model fairness across three standard fairness datasets. We discuss how future work can include LLM fairness evaluations.
    Keywords Computer Science - Computation and Language
    Subject code 004
    Publishing date 2023-11-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling

    Mueller, Aaron / Dredze, Mark

    2021  

    Abstract: Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate ... ...

    Abstract Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models. One added benefit when using representations from multilingual models is that they facilitate zero-shot polylingual topic modeling. However, while it has been widely observed that pre-trained embeddings should be fine-tuned to a given task, it is not immediately clear what supervision should look like for an unsupervised task such as topic modeling. Thus, we propose several methods for fine-tuning encoders to improve both monolingual and zero-shot polylingual neural topic modeling. We consider fine-tuning on auxiliary tasks, constructing a new topic classification task, integrating the topic classification objective directly into topic model training, and continued pre-training. We find that fine-tuning encoder representations on topic classification and integrating the topic classification task directly into topic modeling improves topic quality, and that fine-tuning encoder representations on any task is the most important factor for facilitating cross-lingual transfer.

    Comment: Accepted to NAACL 2021
    Keywords Computer Science - Computation and Language
    Subject code 006
    Publishing date 2021-04-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Learning unsupervised contextual representations for medical synonym discovery.

    Schumacher, Elliot / Dredze, Mark

    JAMIA open

    2019  Volume 2, Issue 4, Page(s) 538–546

    Abstract: Objectives: An important component of processing medical texts is the identification of synonymous words or phrases. Synonyms can inform learned representations of patients or improve linking mentioned concepts to medical ontologies. However, medical ... ...

    Abstract Objectives: An important component of processing medical texts is the identification of synonymous words or phrases. Synonyms can inform learned representations of patients or improve linking mentioned concepts to medical ontologies. However, medical synonyms can be lexically similar ("dilated RA" and "dilated RV") or dissimilar ("cerebrovascular accident" and "stroke"); contextual information can determine if 2 strings are synonymous. Medical professionals utilize extensive variation of medical terminology, often not evidenced in structured medical resources. Therefore, the ability to discover synonyms, especially without reliance on training data, is an important component in processing training notes. The ability to discover synonyms from models trained on large amounts of unannotated data removes the need to rely on annotated pairs of similar words. Models relying solely on non-annotated data can be trained on a wider variety of texts without the cost of annotation, and thus may capture a broader variety of language.
    Materials and methods: Recent contextualized deep learning representation models, such as ELMo (Peters et al., 2019) and BERT, (Devlin et al. 2019) have shown strong improvements over previous approaches in a broad variety of tasks. We leverage these contextualized deep learning models to build representations of synonyms, which integrate the context of surrounding sentence and use character-level models to alleviate out-of-vocabulary issues. Using these models, we perform unsupervised discovery of likely synonym matches, which reduces the reliance on expensive training data.
    Results: We use the ShARe/CLEF eHealth Evaluation Lab 2013 Task 1b data to evaluate our synonym discovery method. Comparing our proposed contextualized deep learning representations to previous non-neural representations, we find that the contextualized representations show consistent improvement over non-contextualized models in all metrics.
    Conclusions: Our results show that contextualized models produce effective representations for synonym discovery. We expect that the use of these representations in other tasks would produce similar gains in performance.
    Language English
    Publishing date 2019-11-04
    Publishing country United States
    Document type Journal Article
    ISSN 2574-2531
    ISSN (online) 2574-2531
    DOI 10.1093/jamiaopen/ooz057
    Database MEDical Literature Analysis and Retrieval System OnLINE

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