1 code implementation • LREC 2022 • Muskan Garg, Seema Wazarkar, Muskaan Singh, Ondřej Bojar
With the development of multimodal systems and natural language generation techniques, the resurgence of multimodal datasets has attracted significant research interests, which aims to provide new information to enrich the representation of textual data.
no code implementations • 12 Jan 2024 • Muskan Garg, MSVPJ Sathvik, Amrit Chadha, Shaina Raza, Sunghwan Sohn
The social NLP research community witness a recent surge in the computational advancements of mental health analysis to build responsible AI models for a complex interplay between language use and self-perception.
no code implementations • 21 Nov 2023 • MSVPJ Sathvik, Surjodeep Sarkar, Chandni Saxena, Sunghwan Sohn, Muskan Garg
Mental health professionals and clinicians have observed the upsurge of mental disorders due to Interpersonal Risk Factors (IRFs).
no code implementations • 25 Aug 2023 • Muskan Garg
During the current mental health crisis, the importance of identifying potential indicators of mental issues from social media content has surged.
no code implementations • 3 Aug 2023 • Shaina Raza, Muskan Garg, Deepak John Reji, Syed Raza Bashir, Chen Ding
Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data.
no code implementations • 8 Jun 2023 • Muskan Garg, Manas Gaur, Raxit Goswami, Sunghwan Sohn
Low self-esteem and interpersonal needs (i. e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts.
no code implementations • 6 Jun 2023 • Chandreen Liyanage, Muskan Garg, Vijay Mago, Sunghwan Sohn
Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) manifested in self-narrated text.
1 code implementation • 30 May 2023 • Muskan Garg, Amirmohammad Shahbandegan, Amrit Chadha, Vijay Mago
With a surge in identifying suicidal risk and its severity in social media posts, we argue that a more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare.
no code implementations • 30 May 2023 • Muskan Garg, Chandni Saxena, Debabrata Samanta, Bonnie J. Dorr
Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP).
no code implementations • 25 Apr 2023 • Surjodeep Sarkar, Manas Gaur, L. Chen, Muskan Garg, Biplav Srivastava, Bhaktee Dongaonkar
Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually.
no code implementations • 8 Apr 2023 • Muskan Garg
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.
no code implementations • 26 Jan 2023 • Muskan Garg, Chandni Saxena, Usman Naseem, Bonnie J Dorr
To bridge this gap, we posit two significant dimensions: (1) Causal analysis to illustrate a cause and effect relationship in the user generated text; (2) Perception mining to infer psychological perspectives of social effects on online users intentions.
no code implementations • 6 Jan 2023 • Simranjeet Kaur, Ritika Bhardwaj, Aastha Jain, Muskan Garg, Chandni Saxena
With recent developments in digitization of clinical psychology, NLP research community has revolutionized the field of mental health detection on social media.
1 code implementation • 16 Oct 2022 • Chandni Saxena, Muskan Garg, Gunjan Ansari
In this task, we fine tune the classifiers and find explanations for multi-class causal categorization of mental illness on social media with LIME and Integrated Gradient (IG) methods.
no code implementations • 27 Aug 2022 • Muskan Garg
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.
no code implementations • 27 Aug 2022 • Muskan Garg, Naveen Aggarwal
In this research work, analysis of isolated digit recognition in the presence of different bit rates and at different noise levels has been performed.
1 code implementation • LREC 2022 • Muskan Garg, Chandni Saxena, Veena Krishnan, Ruchi Joshi, Sriparna Saha, Vijay Mago, Bonnie J Dorr
We introduce a new dataset for Causal Analysis of Mental health issues in Social media posts (CAMS).
no code implementations • ICON 2021 • Gunjan Ansari, Muskan Garg, Chandni Saxena
To handle this issue, we have studied the effect of data augmentation techniques on domain specific user generated text for mental health classification.
no code implementations • 4 Oct 2021 • Muskan Garg
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.