# The Use of Artificial Intelligence in eParticipation: Mapping Current Research
## 5 Discussion, Implications, and Conclusions
### Theoretical and Practical Implications  
The analysis of the studies provides some interesting insights into the application of AI in eParticipation. The first set of results concerns the use of AI in these studies. Specifically, classification and supervised learning AI methods and algorithms were applied in most research papers. In terms of applications, chatbots seem to be increasingly used in eParticipation research while the application of Robotics in one study is notable.  
A second set of results concerns eParticipation. Here, it is suggested that the primary source of citizens' feedback on governments' policies comes from social media. In addition, the anticipated outcomes of eParticipation activities usually lack clarity on how the proposed approach or technology will bring them to fruition. Moreover, the evaluation of eParticipation activity results is seldom addressed in the literature. This observation is not confined solely to highly technical articles but encompasses a significant portion of the literature. Conversely, there are only general claims about enhancing the efficiency and effectiveness of public administration or governance without providing details. Furthermore, more than half of the articles do not explicitly mention any eParticipation effect.  
The analysis further revealed some important outcomes regarding policymaking. Several articles highlight the need to use citizens' opinions to create policies. For example, ref. [67] used available techniques for performing opinion mining in the analysis of citizens' feelings about public policies. In addition, ref. [49] documents the early developments  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Evaluation** & **Paper ID** \\ \hline F-measure & [44; 45; 47; 48; 57; 58; 60; 64; 66; 73; 77; 80; 82; 86] \\ \hline Precision & [43; 45; 57; 60; 62; 66; 67; 73; 76; 77; 78; 80; 82; 86] \\ \hline Accuracy & [43; 44; 46; 62; 63; 69; 70; 73; 76; 78; 80; 82] \\ \hline Recall & [45; 52; 57; 60; 62; 66; 73; 77; 78; 80; 82; 86] \\ \hline \hline \end{tabular}
\end{table}
Table 8: AI Evaluation metrics.  
of a decision support system that uses political discussions on social media to provide useful information regarding citizens' opinions to policymakers. Furthermore, ref. [57] analyzes ePetitions using named entity recognition and topic modeling with latent Dirichlet allocation. The results revealed that the techniques proposed can be utilized for gauging public sentiment. [PERSON] and [PERSON] in [62] present and test a framework for analyzing people's tweets regarding a major agricultural policy. [PERSON] and [PERSON] in [54] propose the use of neural networks for the analysis of online discussions and to match people who are for or against a particular policy. In [60], it is proposed that the use of a decision support system by citizens could help policymakers to identify solutions to citizens' problems. Finally, ref. [55] argues that gamification can improve the currently low-quality online deliberations and focuses on how policymakers can put citizens' input to use before generating and enacting public policies.  
As a result, this study can be useful to academics and practitioners. Academics can use the results of the analysis in order to better understand which areas have already been investigated and what the current results are. Similarly, they may easily identify areas where further research is needed. Practitioners can use the analysis results to better understand which AI methods have already been tested and with what results and conditions. Policymakers can better understand the relationships between AI-enhanced participation and the current legal framework, particularly in the European Union, where GDPR is in force while the new AI Act is also in effect.