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  1. AU="Garg, Muskan"
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  3. AU="Hartmann, Annette M" AU="Hartmann, Annette M"
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  1. Book ; Online ; E-Book: The climate change crisis and its impact on mental health

    Samanta, Debabrata / Garg, Muskan

    (Advances in psychology, mental health and behavioral studies book series)

    2024  

    Abstract: Climate change has far-reaching consequences beyond its environmental impact. It also significantly affects social and mental well-being, both at individual and community levels. Addressing the social and mental well-being impacts of climate change ... ...

    Author's details Debabrata Samanta, Muskan Garg
    Series title Advances in psychology, mental health and behavioral studies book series
    Abstract "Climate change has far-reaching consequences beyond its environmental impact. It also significantly affects social and mental well-being, both at individual and community levels. Addressing the social and mental well-being impacts of climate change requires a multi-faceted approach that includes both mitigation and adaptation strategies"--
    Subject code 363.7001/9
    Language English
    Size 1 Online-Ressource (xxxvii, 301 Seiten), Diagramme
    Publisher IGI Global
    Publishing place Hershey, PA
    Publishing country United States
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT030715242
    ISBN 9798369332733 ; 9798369332726
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book: Impact of Climate Change on Mental Health and Well-Being

    Samanta, Debabrata / Garg, Muskan

    2024  

    Language English
    Size 316 p.
    Publisher IGI Global
    Document type Book
    Note PDA Manuell_25
    Format 178 x 254 x 19
    ISBN 9798369321775
    Database PDA

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  3. Article ; Online: Mental disturbance impacting wellness dimensions: Resources and open research directions.

    Garg, Muskan

    Asian journal of psychiatry

    2023  Volume 92, Page(s) 103876

    Abstract: In light of the unparalleled pressure faced by the healthcare system, there arises a pressing need for innovative solutions to comprehensively assess the overall well-being of individuals affected by mental health issues. With the objective of advancing ... ...

    Abstract In light of the unparalleled pressure faced by the healthcare system, there arises a pressing need for innovative solutions to comprehensively assess the overall well-being of individuals affected by mental health issues. With the objective of advancing AI-driven mental health analysis towards fine-grained analysis, we develop and publicly release our datasets, MULTIWD and WELLXPLAIN, specifically designed to capture the impact of mental disturbances on wellness dimensions in self-narrated texts. To this end, we make two major contributions. First, our examination focuses on the identification of one or more of the six distinct wellness dimensions evident within a given text, shedding light on the significant ramifications of mental disturbance, which, in turn, can perpetuate further mental unrest. Second, we conducting an extensive analysis of the textual cues that signify the presence of various wellness dimensions. We delve into the content of the text, examining specific linguistic and contextual markers that provide indications of the wellness dimensions being discussed. Finally, we open up future research directions to facilitate advancements in the domain of AI-driven approaches for fine-grained mental health analysis. This framework aims to establish and validate new clinical categories for mental distress, bridging the gap between mental wellness and illness, in response to the higher prevalence of distress compared to illnesses.
    MeSH term(s) Humans ; Mental Health ; Mental Disorders/diagnosis ; Mental Disorders/psychology
    Language English
    Publishing date 2023-12-20
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2456678-0
    ISSN 1876-2026 ; 1876-2018
    ISSN (online) 1876-2026
    ISSN 1876-2018
    DOI 10.1016/j.ajp.2023.103876
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Mental Health Analysis in Social Media Posts: A Survey.

    Garg, Muskan

    Archives of computational methods in engineering : state of the art reviews

    2023  Volume 30, Issue 3, Page(s) 1819–1842

    Abstract: The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations ... ...

    Abstract The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations between
    Language English
    Publishing date 2023-01-03
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2276736-8
    ISSN 1886-1784 ; 1134-3060
    ISSN (online) 1886-1784
    ISSN 1134-3060
    DOI 10.1007/s11831-022-09863-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Book ; Online: Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts

    Garg, Muskan

    2023  

    Abstract: Motivated with recent advances in inferring users' mental state in social media posts, we identify and formulate the problem of finding causal indicators behind mental illness in self-reported text. In the past, we witness the presence of rule-based ... ...

    Abstract Motivated with recent advances in inferring users' mental state in social media posts, we identify and formulate the problem of finding causal indicators behind mental illness in self-reported text. In the past, we witness the presence of rule-based studies for causal explanation analysis on curated Facebook data. The investigation on transformer-based model for multi-class causal categorization in Reddit posts point to a problem of using long-text which contains as many as 4000 words. Developing end-to-end transformer-based models subject to the limitation of maximum-length in a given instance. To handle this problem, we use Longformer and deploy its encoding on transformer-based classifier. The experimental results show that Longformer achieves new state-of-the-art results on M-CAMS, a publicly available dataset with 62\% F1-score. Cause-specific analysis and ablation study prove the effectiveness of Longformer. We believe our work facilitates causal analysis of depression and suicide risk on social media data, and shows potential for application on other mental health conditions.
    Keywords Computer Science - Computation and Language ; Computer Science - Computers and Society
    Publishing date 2023-04-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article: A survey on different dimensions for graphical keyword extraction techniques: Issues and Challenges.

    Garg, Muskan

    Artificial intelligence review

    2021  Volume 54, Issue 6, Page(s) 4731–4770

    Abstract: The transmission from offline activities to online activities due to the social disorder evolved from COVID-19 pandemic lockdown has led to increase in the online economic and social activities. In this regard, the Automatic Keyword Extraction (AKE) from ...

    Abstract The transmission from offline activities to online activities due to the social disorder evolved from COVID-19 pandemic lockdown has led to increase in the online economic and social activities. In this regard, the Automatic Keyword Extraction (AKE) from textual data has become even more interesting due to its application over different domains of Natural Language Processing (NLP). It is observed that the Graphical Keyword Extraction Techniques (GKET) use Graph of Words (GoW) in literature for analysis in different dimensions. In this article, efforts have been made to study these different dimensions for GKET, namely, the GoW representation, the statistical properties of GoW, the stability of the structure of GoW, the diversity in approaches over GoW for GKET, and the ranking of nodes in GoW. To elucidate these different dimensions, a comprehensive survey of GKET is carried in different domains to make some inferences out of the existing literature. These inferences are used to lay down possible research directions for interdisciplinary studies of network science and NLP. In addition, the experimental results are analysed to compare and contrast the existing GKET over 21 different dataset, to analyse the Word Co-occurrence Networks (WCN) for 15 different languages, and to study the structure of WCN for different genres. In this article, some strong correspondences in different disciplinary approaches are identified for different dimensions, namely, GoW representation: 'Line Graphs' and 'Bigram Words Graphs'; Feature extraction and selection using eigenvalues: 'Random Walk' and 'Spectral Clustering'. Different observations over the need to integrate multiple dimensions has open new research directions in the inter-disciplinary field of network science and NLP, applicable to handle streaming data and language-independent NLP.
    Language English
    Publishing date 2021-04-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 1479828-1
    ISSN 1573-7462 ; 0269-2821
    ISSN (online) 1573-7462
    ISSN 0269-2821
    DOI 10.1007/s10462-021-10010-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: An event detection technique using social media data

    Garg, Muskan

    2022  

    Abstract: People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about ... ...

    Abstract People post information about different topics which are in their active vocabulary over social media platforms (like Twitter, Facebook, PInterest and Google+). They follow each other and it is more likely that the person who posts information about current happenings will receive better response. Manual analysis of huge amount of data on social media platforms is difficult. This has opened new research directions for automatic analysis of usercontributed social media documents. Automatic social media data analysis is difficult due to abundant information shared by users. Many researchers use Twitter data for Social Media Analysis (SMA) as the Twitter data is freely available in the public domain. One of the most this research work. Event Detection from social media data is used for different applications like traffic congestion detection, disaster and emergency management, and live news detection. Nature of the information which is shared on twitter platform is short-text, noisy, and ambiguous. Thus, event detection and extraction of event phrases from user-generated and illformed data becomes challenging. To address these challenges, events are extracted from streaming social media data in the form of keyphrases using different cognitive properties. The motivation behind this research work is to provide substantial improvements in the lexical variation of event phrases while detecting events and sub-events from twitter data. In this research work, the approach towards event detection from social media data is divided into three phases namely: Identifying sub-graphs in Microblog Word Co-occurrence Network (WCN) which provides important information about keyphrases; Identifying multiple events from social media data; and Ranking contextual information of event phrases.
    Keywords Computer Science - Social and Information Networks ; Computer Science - Information Retrieval
    Subject code 306
    Publishing date 2022-08-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article: CareD: Caregiver's Experience with Cognitive Decline in Reddit Posts.

    Garg, Muskan / Sohn, Sunghwan

    IEEE International Conference on Healthcare Informatics. IEEE International Conference on Healthcare Informatics

    2023  Volume 2023, Page(s) 581–587

    Abstract: With advancements in analysis of cognitive decline in electronic health records, the research community witnesses a recent surge in social media posting by caregivers and/or loved ones of people with cognitive decline. The major challenges in this area ... ...

    Abstract With advancements in analysis of cognitive decline in electronic health records, the research community witnesses a recent surge in social media posting by caregivers and/or loved ones of people with cognitive decline. The major challenges in this area are availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability. To this end, we construct a new dataset, Caregivers experiences with cognitive Decline (CareD), of 1005 posts with more than 194K words and 9541 sentences, highlighting discussions on people with dementia and Alzheimer's disease on Reddit. We discuss the changing trends of discussions on cognitive decline in social media and open challenges for natural language processing and social computing. We first identify the Reddit posts reflecting substantial information as candidate posts. We further formulate the annotation guidelines, handle perplexities to investigate the existence of experiences, self-reported articles and potential caregiver in candidate posts, resulting in the discovery of latent symptoms, firsthand information, and prospective source of longitudinal information about the patient, respectively.
    Language English
    Publishing date 2023-12-11
    Publishing country United States
    Document type Journal Article
    ISSN 2575-2626
    ISSN 2575-2626
    DOI 10.1109/ichi57859.2023.00104
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Quantifying the Suicidal Tendency on Social Media

    Garg, Muskan

    A Survey

    2021  

    Abstract: Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a brief period ... ...

    Abstract Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a brief period may lead to clinical depressions and the long-lasting traits of prevailing depressions can be life threatening with suicidal ideation as the possible outcome. The increasing concern towards the rise in number of suicide cases is because it is one of the leading cause of premature but preventable death. Recent studies have shown that mining social media data has helped in quantifying the suicidal tendency of users at risk. This potential manuscript elucidates the taxonomy of mental healthcare and highlights some recent attempts in examining the potential of quantifying suicidal tendency on social media data. This manuscript presents the classification of heterogeneous features from social media data and handling feature vector representation. Aiming to identify the new research directions and advances in the development of Machine Learning (ML) and Deep Learning (DL) based models, a quantitative synthesis and a qualitative review was carried out with corpus of over 77 potential research articles related to stress, depression and suicide risk from 2013 to 2021.

    Comment: Revised version
    Keywords Computer Science - Social and Information Networks ; Computer Science - Computation and Language ; Computer Science - Information Retrieval
    Subject code 300
    Publishing date 2021-10-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  10. Article: Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health.

    Liyanage, Chandreen / Garg, Muskan / Mago, Vijay / Sohn, Sunghwan

    Proceedings of the conference. Association for Computational Linguistics. Meeting

    2024  Volume 2023, Page(s) 306–312

    Abstract: Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative NLP ... ...

    Abstract Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative NLP models for data augmentation to enable further improvement in the pre-screening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through prompt-based Generative NLP models, and evaluate the ROUGE scores and syntactic/semantic similarity among
    Language English
    Publishing date 2024-01-31
    Publishing country United States
    Document type Journal Article
    ISSN 0736-587X
    ISSN 0736-587X
    DOI 10.18653/v1/2023.bionlp-1.27
    Database MEDical Literature Analysis and Retrieval System OnLINE

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