Predicting Collective Phase Transitions through Digital Affective Footprints: An NLP-Driven Framework Integrating VAD Dynamics and Critical Slowing Down

This study bridges computational linguistics and complex systems physics to develop a novel framework for forecasting abrupt collective behavioral phase transitions through digital affective footprints on social media. Motivated by the limitations of retrospective social analyses amid rapid digital interactions, we propose analyzing language as a societal barometer via the Circumplex Model of Affect, augmented with valence, arousal, and dominance (VAD) dimensions to detect precursors like high-arousal lexicon shifts, affective density, and discursive convergence. Drawing on critical slowing down and leading indicators, the framework elucidates how emotional energy accumulation-tracked via NLP-extracted anomalies-signals tipping points preceding physical actions such as protests or market crashes. Key discussions address signal authenticity challenges (e.g., bots, algorithmic amplification, digital dramaturgy), temporal dynamics including lead-time estimation and slacktivism paradoxes, platform affordances as affective modulators, and ethical risks of preemptive surveillance like emotional privacy erosion and chilling effects. Findings affirm affective traces as precise predictors when calibrated for platform biases and entropy filters, with responsible foresight principles advocating transparency, harm mitigation, and human-in-the-loop governance. Limitations encompass sarcasm handling and causality inference, while future directions include multimodal integration and cross-platform modeling. This interdisciplinary approach transforms linguistic chaos into predictable order, enabling proactive societal stewardship without compromising agency.

کلیدواژه ها:

Phase transitions ، Affective footprints ، NLP ، VAD model ، Critical slowing down ، Digital sociology