arXiv subjects

Ananya Malik

Publications and source records attributed to Ananya Malik.

4 recordsLinked to original sources

arXiv2026

"He gets to be the fun parent": Understanding and Supporting Burnt-Out Mothers in Online Communities

Maternal burnout is a psychological phenomena with documented harms to both mother and child, requiring prompt attention. Mothers experiencing burnout might choose to turn to online anonymous platforms, such as Reddit, to share their experience, due to feelings of shame and stigmatization of mental health issues. In this work, we study how mothers use Reddit to discuss their experiences of burnout. We first identify posts written by burnt out mothers by manually annotating Reddit posts and training machine learning models on them. Focusing on posts made by this population (N = 3,244), we then investigate the issues brought up by mothers, such as the need for help, career advice, and co-parenting issues. Additionally, we investigate how the Reddit community responds to these posts through the analysis of comments. We find that commenters frequently share personal lived experiences with the poster, and provide emotional support. Finally, considering co-parenting could be a mitigating factor for parental burnout, we explore co-pareting patterns experienced by burnt out mothers, finding evidence of lack of support for and unequal expectations from mothers.

cs.SI↗

arXiv2025

Are LLMs Empathetic to All? Investigating the Influence of Multi-Demographic Personas on a Model's Empathy

Large Language Models' (LLMs) ability to converse naturally is empowered by their ability to empathetically understand and respond to their users. However, emotional experiences are shaped by demographic and cultural contexts. This raises an important question: Can LLMs demonstrate equitable empathy across diverse user groups? We propose a framework to investigate how LLMs' cognitive and affective empathy vary across user personas defined by intersecting demographic attributes. Our study introduces a novel intersectional analysis spanning 315 unique personas, constructed from combinations of age, culture, and gender, across four LLMs. Results show that attributes profoundly shape a model's empathetic responses. Interestingly, we see that adding multiple attributes at once can attenuate and reverse expected empathy patterns. We show that they broadly reflect real-world empathetic trends, with notable misalignments for certain groups, such as those from Confucian culture. We complement our quantitative findings with qualitative insights to uncover model behaviour patterns across different demographic groups. Our findings highlight the importance of designing empathy-aware LLMs that account for demographic diversity to promote more inclusive and equitable model behaviour.

cs.CL↗

arXiv2025

Who Speaks Matters: Analysing the Influence of the Speaker's Ethnicity on Hate Classification

Large Language Models (LLMs) offer a lucrative promise for scalable content moderation, including hate speech detection. However, they are also known to be brittle and biased against marginalised communities and dialects. This requires their applications to high-stakes tasks like hate speech detection to be critically scrutinized. In this work, we investigate the robustness of hate speech classification using LLMs particularly when explicit and implicit markers of the speaker's ethnicity are injected into the input. For explicit markers, we inject a phrase that mentions the speaker's linguistic identity. For the implicit markers, we inject dialectal features. By analysing how frequently model outputs flip in the presence of these markers, we reveal varying degrees of brittleness across 3 LLMs and 1 LM and 5 linguistic identities. We find that the presence of implicit dialect markers in inputs causes model outputs to flip more than the presence of explicit markers. Further, the percentage of flips varies across ethnicities. Finally, we find that larger models are more robust. Our findings indicate the need for exercising caution in deploying LLMs for high-stakes tasks like hate speech detection.

cs.CL↗

arXiv2023

Evaluating Large Language Models through Gender and Racial Stereotypes

Language Models have ushered a new age of AI gaining traction within the NLP community as well as amongst the general population. AI's ability to make predictions, generations and its applications in sensitive decision-making scenarios, makes it even more important to study these models for possible biases that may exist and that can be exaggerated. We conduct a quality comparative study and establish a framework to evaluate language models under the premise of two kinds of biases: gender and race, in a professional setting. We find out that while gender bias has reduced immensely in newer models, as compared to older ones, racial bias still exists.

cs.CL↗