Public Opinion and it’s Influence on Technology Governance in the Age of Digital Transformation

This study examines how social public opinion affects technology governance in order to examine the ways in which digital transformation affects various government agencies. Our method breaks out the precise effects of digital transformation on different government departments and their contributions to overall governmental efficiency, in contrast to earlier research that frequently sees digital government as a single entity. We evaluate the extent of digital transformation in government departments from 2013 to 2023 using text analysis and empirical models, and the results show that these changes improve governmental efficiency. Our results also demonstrate how important it is to coordinate digital transformation initiatives in order to increase overall efficiency. This study not only presents a novel theoretical framework for comprehending digital government, but it also makes practical suggestions for allocating resources as efficiently as possible and planning digital projects that would advance government. For efficient analysis of the enormous volume of public debate on digital platforms, sophisticated Natural Language Processing (NLP) approaches are needed. In order to capture both contextual meanings and sequential dependencies in public opinion data, this study suggests a hybrid model that combines BERT and LSTM. The model uses LSTM (Long Short-Term Memory) to remember sequential patterns in sentiment evolution and BERT (Bidirectional Encoder Representations from Transformers) for deep semantic comprehension. The study sheds light on how public opinion influences technology policies and governance tactics by using this model to analyse case studies of AI ethics, data privacy, and platform laws.