Publications

* indicates equal contribution

2024

1. Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization

   Rochana* Chaturvedi , Sugat* Chaturvedi , and Elena Zheleva

   In ACM Web Conference (WWW ’24) (Forthcoming) , 2024

   Abs LINK

   While exposure to diverse viewpoints may reduce polarization, it can also have a backfire effect and exacerbate polarization when the discussion is adversarial. Here, we examine whether intergroup interactions around important events affect polarization between majority and minority groups in social networks. We compile data on the religious identity of nearly 700,000 Indian Twitter users engaging in COVID-19-related discourse during 2020. We introduce a new measure for an individualś group conformity based on contextualized embeddings of tweet text, which helps us assess polarization between religious groups. We then use a meta-learning framework to examine heterogeneous treatment effects of intergroup interactions on an individualś group conformity in the light of communal, political, and socio-economic events. We find that for political and social events, intergroup interactions reduce polarization. This decline is weaker for individuals at the extreme who already exhibit high conformity to their group. In contrast, during communal events, intergroup interactions can increase group conformity. Finally, we decompose the differential effects across religious groups in terms of emotions and topics of discussion. The results show that the dynamics of religious polarization are sensitive to the context and have important implications for understanding the role of intergroup interactions.

2. It’s All in the Name: A Character Based Approach to Infer Religion

   Rochana* Chaturvedi , and Sugat* Chaturvedi

   Political Analysis, 2024

   Abs LINK

   Large-scale microdata on group identity are critical for studies on identity politics and violence but remain largely unavailable for developing countries. We use personal names to infer religion in South Asia—where religion is a salient social division, and yet, disaggregated data on it are scarce. Existing work predicts religion using a dictionary-based method, and therefore, cannot classify unseen names. We provide character-based machine learning models that can classify unseen names too with high accuracy. Our models are also much faster, and hence, scalable to large datasets. We explain the classification decisions of one of our models using the layer-wise relevance propagation technique. The character patterns learned by the classifier are rooted in the linguistic origins of names. We apply these to infer the religion of electoral candidates using historical data on Indian elections and observe a trend of declining Muslim representation. Our approach can be used to detect identity groups across the world for whom the underlying names might have different linguistic roots.

2023

1. Sequential Representation of Sparse Heterogeneous Data for Diabetes Risk Prediction

   Rochana Chaturvedi , Mudassir Rashid , Brian T. Layden , and 3 more authors

   In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) , Dec 2023

   Abs LINK

   Type 2 diabetes (T2D) is a major public health problem, and opportunistic screening to detect T2D at an early stage can help initiate interventions that delay or prevent the disease and its complications. In this study, we use electronic health records (EHR) and concepts extracted from clinical notes to predict future T2D risk. Our deep neural network-based model captures the temporal sequence of patient visits. We use explainable AI algorithms to assess the model decisions and observe alignment with the domain knowledge of clinical experts.

2020

1. Divide and conquer: From complexity to simplicity for lay summarization

   Rochana Chaturvedi ,  Saachi* , Jaspreet Singh* Dhani , and 7 more authors

   In Proceedings of the first workshop on scholarly document processing , Dec 2020

   Abs LINK

   We describe our approach for the 1st Computational Linguistics Lay Summary Shared Task CL-LaySumm20. The task is to produce non-technical summaries of scholarly documents. The summary should be within easy grasp of a layman who may not be well versed with the domain of the research article. We propose a two step divide-and-conquer approach. First, we judiciously select segments of the documents that are not overly pedantic and are likely to be of interest to the laity, and over-extract sentences from each segment using an unsupervised network based method. Next, we perform abstractive summarization on these extractions and systematically merge the abstractions. We run ablation studies to establish that each step in our pipeline is critical for improvement in the quality of lay summary. Our approach leverages state-of-the-art pre-trained deep neural network based models as zero-shot learners to achieve high scores on the task.