Scholarlink

Computational social science research on political tweets is a general study that examines political communication dynamics on social media during the April 2024 Indian elections, focusing on user perceptions of political tweets. A primary dataset of scraped tweets underwent preprocessing, including tokenisation, lemmatisation, and stop-word removal. Sentiment analysis using the NLTK package revealed that 51.75% of tweets were positive, 41.23% negative, and the rest expressed neutral sentiments with emotional undertones like fear, surprise, and sadness. A comprehensive evaluation used classical, ensemble-based, and quantum-inspired clustering and classification methodologies. Classical methods like KMeans showed moderate results (accuracy: 32.46%, modularity: 0.42, silhouette score: 0.35), improving significantly when paired with a Support Vector Classifier and a Radial Basis Function kernel (accuracy: 85.09%, modularity: 0.46). Ensemble methods improved clustering quality, with Bagging Ensemble achieving a modularity of 0.58, silhouette score of 0.47, and an F1 score of 58.22%. Quantum-inspired approaches showed notable performance, with Quantum K-Means using dynamic centroids initialisation achieving the highest modularity (0.57), silhouette score (0.48), and Calinski-Harabasz Index (545.12). Quantum-Enhanced DBSCAN delivered the best accuracy (70.02%) and F1 score (68.01%). Community detection algorithms like Louvain and Newman-Girvan further provided insights into thematic coherence. This study underscores the potential of advanced clustering methodologies for effectively analysing political discourse on social media.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Tweeting the Vote: Influence Propagation and Community Analysis on Political Tweets with a Quantum Clustering Approach

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Elizabeth Leah George,
  • Subashini Parthasarathy

摘要

Computational social science research on political tweets is a general study that examines political communication dynamics on social media during the April 2024 Indian elections, focusing on user perceptions of political tweets. A primary dataset of scraped tweets underwent preprocessing, including tokenisation, lemmatisation, and stop-word removal. Sentiment analysis using the NLTK package revealed that 51.75% of tweets were positive, 41.23% negative, and the rest expressed neutral sentiments with emotional undertones like fear, surprise, and sadness. A comprehensive evaluation used classical, ensemble-based, and quantum-inspired clustering and classification methodologies. Classical methods like KMeans showed moderate results (accuracy: 32.46%, modularity: 0.42, silhouette score: 0.35), improving significantly when paired with a Support Vector Classifier and a Radial Basis Function kernel (accuracy: 85.09%, modularity: 0.46). Ensemble methods improved clustering quality, with Bagging Ensemble achieving a modularity of 0.58, silhouette score of 0.47, and an F1 score of 58.22%. Quantum-inspired approaches showed notable performance, with Quantum K-Means using dynamic centroids initialisation achieving the highest modularity (0.57), silhouette score (0.48), and Calinski-Harabasz Index (545.12). Quantum-Enhanced DBSCAN delivered the best accuracy (70.02%) and F1 score (68.01%). Community detection algorithms like Louvain and Newman-Girvan further provided insights into thematic coherence. This study underscores the potential of advanced clustering methodologies for effectively analysing political discourse on social media.