15 citations · 36 across the 14 of their papers we have counts for
13 papers · 1 filter
NBIAS: A Natural Language Processing Framework for Bias Identification in Text
Shaina Raza, Muskan Garg, Deepak John Reji +2
Bias in textual data can lead to skewed interpretations and outcomes when the data is used. These biases could perpetuate stereotypes, discrimination, or other forms of unfair trea…
WellXplain: Wellness Concept Extraction and Classification in Reddit Posts for Mental Health Analysis
Muskan Garg
During the current mental health crisis, the importance of identifying potential indicators of mental issues from social media content has surged. Overlooking the multifaceted natu…
LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts
Muskan Garg, Manas Gaur, Raxit Goswami +1
Low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts. Individuals se…
Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health
Chandreen Liyanage, Muskan Garg, Vijay Mago +1
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…
LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts
Muskan Garg, Chandni Saxena, Debabrata Samanta +1
Social media is a potential source of information that infers latent mental states through Natural Language Processing (NLP). While narrating real-life experiences, social media us…
An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts
Muskan Garg, Amirmohammad Shahbandegan, Amrit Chadha +1
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 clin…
Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts
Muskan Garg
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 s…
Causal Categorization of Mental Health Posts using Transformers
Simranjeet Kaur, Ritika Bhardwaj, Aastha Jain +2
With recent developments in digitization of clinical psychology, NLP research community has revolutionized the field of mental health detection on social media. Existing research i…
NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media
Muskan Garg, Chandni Saxena, Usman Naseem +1
Interactions among humans on social media often convey intentions behind their actions, yielding a psychological language resource for Mental Health Analysis (MHA) of online users.…
Explainable Causal Analysis of Mental Health on Social Media Data
Chandni Saxena, Muskan Garg, Gunjan Ansari
With recent developments in Social Computing, Natural Language Processing and Clinical Psychology, the social NLP research community addresses the challenge of automation in mental…
Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environments
Muskan Garg, Naveen Aggarwal
This research work is about recent development made in speech recognition. In this research work, analysis of isolated digit recognition in the presence of different bit rates and…
CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts
Muskan Garg, Chandni Saxena, Veena Krishnan +4
Research community has witnessed substantial growth in the detection of mental health issues and their associated reasons from analysis of social media. We introduce a new dataset…
Data Augmentation for Mental Health Classification on Social Media
Gunjan Ansari, Muskan Garg, Chandni Saxena
The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user generated text o…