AI-based Twitter framework for assessing the involvement of government schemes in electoral campaigns

Abstract

The government schemes (also known as programs and plans) or social welfare policies can be defined as the set of assistance and aids provided by the country’s governance body. These schemes focus on the improved well-being of needful citizens. Some researchers have shown that introducing such policies and schemes has had an electoral impact in democratic countries. These earlier studies relied upon the post-poll and public survey data to reach conclusions. However, this data source has limitations and has to be collected manually, which makes it time-consuming and costly. The readily available internet inculcates the sharing of opinions freely on social media, facilitating government–citizen interactions. These interactions may show fluctuations in frequency and intensity on social media with the success and failure of some government schemes. Thus, this research proposes utilizing the Twitter data related to the government welfare schemes during the election duration to uncover the spatial and temporal relationships between the tweets’ information diffusion pattern and political elections. To start with, we perform tweet classification to identify the target communities or groups and multiple user-engagements by employing deep learning-based pre-trained language representation (LR) models. The scarcity of labeled data limits the application of the supervised classification models on real-time data. Thus, we propose Mod-EDA, a text augmentation method to upscale the labeled data for reduced overfitting. Going further, we propose two modules, where the classified tweets are studied to investigate the scheme tweets’ information diffusion pattern in correspondence to the election duration in terms of the voting phase and the electing parties, respectively. The proposed framework is evaluated for a case study of the 2019 Indian general elections. This study depicts that the voting phases and election duration trigger high government schemes related tweet generation. However, it is not affected by the location of the voting phase. The generation of complaints and negative tweets in one voting phase is covered with the positive news in subsequent voting phases. It is also seen that there is a strong influence of the ruling party on the scheme-related Twitter data generation.