Computational Social Linguistics for Telugu Cultural Preservation: Novel Algorithms for Chandassu Metrical Pattern Recognition

TL;DR

This work addresses preserving Telugu chandassu by introducing a computational social linguistics framework that integrates traditional prosody with automated pattern recognition. The modular pipeline combines AksharamTokenizer, LaghuvuGuruvu Generator, and PadyaBhedam Checker to produce the Chandassu Score $C$, defined as $C = \frac{1}{n} \sum_{i=1}^{n} c_i$, and evaluates a 4,651‑padyam dataset. It reports a Chandassu Score of $91.73\%$, with high per-dimension performance ($c_{na} \approx 99.43\%$, $c_{np} \approx 99.93\%$, $c_{gk} \approx 93.82\%$, $c_{pr} \approx 94.54\%$, $c_{yt} \approx 78.69\%$), demonstrating robust metrical analysis and pattern recognition. The framework advances cultural preservation and digital humanities, enabling real-time metrical validation and scalable study of traditional Telugu poetry, with future extensions to sandhi and satakam-level evaluation.

Abstract

This research presents a computational social science approach to preserving Telugu Chandassu, the metrical poetry tradition representing centuries of collective cultural intelligence. We develop the first comprehensive digital framework for analyzing Telugu prosodic patterns, bridging traditional community knowledge with modern computational methods. Our social computing approach involves collaborative dataset creation of 4,651 annotated padyams, expert-validated linguistic patterns, and culturally-informed algorithmic design. The framework includes AksharamTokenizer for prosody-aware tokenization, LaghuvuGuruvu Generator for classifying light and heavy syllables, and PadyaBhedam Checker for automated pattern recognition. Our algorithm achieves 91.73% accuracy on the proposed Chandassu Score, with evaluation metrics reflecting traditional literary standards. This work demonstrates how computational social science can preserve endangered cultural knowledge systems while enabling new forms of collective intelligence around literary heritage. The methodology offers insights for community-centered approaches to cultural preservation, supporting broader initiatives in digital humanities and socially-aware computing systems.

Figures (4)

• Figure 1: Distribution of padyams across satakams in Chandassu dataset
• Figure 2: Proposed computational framework architecture for Telugu metrical poetry analysis
• Figure 3: Performance evaluation across prosodic classes
• Figure 4: Performance evaluation across padyam types