ABSTRACT This study introduces and evaluates the robustness of different volumetric, sentiment, and social network approaches to predict the elections in three Asian countries – Malaysia, India, and Pakistan from Twitter posts. We find that predictive power of social media performs well for India and Pakistan but is not effective for Malaysia. Overall, we find that it is useful to consider the recency of Twitter posts while using it to predict a real outcome, such as an election result. Sentiment information mined using machine learning models was the most accurate predictor of election outcomes. Social network information is stable despite sudden surges in political discussions, for e.g. around elections-related news events. Methods combining sentiment and volume information, or sentiment and social network information, are effective at predicting smaller vote shares, for e.g. vote shares in the case of independent candidates and regional parties. We conclude with a detailed discussion on the caveats of social media analysis for predicting real-world outcomes and recommendations for future work.

中文翻译：

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从社交媒体预测选举：三个国家、三种方法的比较研究

摘要 本研究介绍并评估了不同的容量、情绪和社交网络方法的稳健性，以根据 Twitter 帖子预测三个亚洲国家——马来西亚、印度和巴基斯坦的选举。我们发现社交媒体的预测能力对印度和巴基斯坦表现良好，但对马来西亚无效。总的来说，我们发现在使用 Twitter 帖子预测真实结果（例如选举结果）时考虑 Twitter 帖子的新近度很有用。使用机器学习模型挖掘的情绪信息是选举结果最准确的预测指标。尽管政治讨论突然激增，例如与选举相关的新闻事件，但社交网络信息是稳定的。结合情感和音量信息，或情感和社交网络信息的方法，有效地预测较小的投票份额，例如在独立候选人和地区政党的情况下的投票份额。最后，我们详细讨论了社交媒体分析在预测现实世界结果和对未来工作的建议方面的注意事项。