Machine learning
models of state-citizen communication on social networks from the perspective of digital government
DOI:
https://doi.org/10.56162/transdigital529Keywords:
machine learning, e-government, citizen participation, social networks, digital communicationAbstract
In recent years, the State has attempted to transform interactions between institutions and citizens. However, unidirectional models persist, limiting transparency and participation. This study analyzed the digital communication of the Secretariat of Security, Coexistence, and Justice of Bogota, Colombia, on TikTok, based on 495 posts and 23,434 comments. A mixed-method approach was applied, using Python programming languages to classify interactions and detect institutional responses in comments and content. The Viable Systems Model was also used to represent the entity's social media communication flow and project two scenarios based on e-government. The results showed that, although 10.9% of the comments could be considered questions, the entity did not respond to any comments. In this way, the current communication flow was projected, characterized by a unidirectional model typical of e-government 1.0. Its limitations were also contrasted with e-government 2.0and 3.0 scenarios, where machine learning and natural language processing enable real-time feedback. It was concluded that the integration of e-Government 3.0 can overcome biases and advance communication that is more adaptive, participatory, and focused on citizen needs.
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