Application of Automated Tools in Researching Internet Discourses: Experience of Using the Recurrent Neural Networks for Studying Discussions on Pension Reform
Abstract:The paper presents a conceptual model of a pragmatic-moral discourse as a basis for assembling a training dataset, as well as the results of an experiment of using such data by the Recurrent Neural Network (RNN) to assess how accurately it can determine the attitude of Internet discourse participants towards the pension
Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.
Electronic Participation (eParticipation) enables citizens to engage in political and decision-making processes using information and communication technologies. As in many other fields, Artificial Intelligence (AI) has recently started to dictate some of the realities of eParticipation. As a result, an increasing number of studies are investigating the use of AI in eParticipation. The aim of this paper is to map current research on the use of AI in eParticipation. Following PRISMA methodology, the authors identified 235 relevant papers in Web of Science and Scopus and selected 46 studies for review. For analysis purposes, an analysis framework was constructed that combined eParticipation elements (namely actors, activities, effects, contextual factors, and evaluation) with AI elements (namely areas, algorithms, and algorithm evaluation). The results suggest that certain eParticipation actors and activities, as well as AI areas and algorithms, have attracted significant attention from researchers. However, many more remain largely unexplored. The findings can be of value to both academics looking for unexplored research fields and practitioners looking for empirical evidence on what works and what does not.
“…Применению методов машинного обучения, в частности, искусственных нейронных сетей, в сентимент-анализе (анализе тональности текста) и компьютерной лингвистике послужили развитие информационно-коммуникационных технологий, значительное увеличение количества данных и рост вычислительных мощностей [11,12]. На их основе нам удалось провести пилотное исследование с применением машинного обучения для сентимент-анализа дискуссий граждан на такую актуальную общественно-политическую тему, как российская пенсионная реформа [13]. Цель данного исследования заключалась в разработке автоматизированного инструментария для проведения анализа интернетдискурса, в частности, проведение эксперимента по применению искусственных нейронных сетей в определении позиций граждан (сентимент-анализ) в онлайн-обсуждениях на тему повышения пенсионного возраста.…”
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