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In recent years, social media has been criticized for yielding polarization.Identifying emerging disagreements and growing polarization is important forjournalists to create alerts and provide more balanced coverage. While recentstudies have shown the existence of polarization on social media, theyprimarily focused on limited topics such as politics with a large volume ofdata collected in the long term, especially over months or years. While thesefindings are helpful, they are too late to create an alert immediately. Toaddress this gap, we develop a domain-agnostic mining method to identifypolarized topics on Twitter in a short-term period, namely 12 hours. As aresult, we find that daily Japanese news-related topics in early 2022 werepolarized by 31.6\% within a 12-hour range. We also analyzed that they tend toconstruct information diffusion networks with a relatively high average degree,and half of the tweets are created by a relatively small number of people.However, it is very costly and impractical to collect a large volume of tweetsdaily on many topics and monitor the polarization due to the limitations of theTwitter API. To make it more cost-efficient, we also develop a predictionmethod using machine learning techniques to estimate the polarization levelusing randomly collected tweets leveraging the network information. Extensiveexperiments show a significant saving in collection costs compared to baselinemethods. In particular, our approach achieves F-score of 0.85, requiring 4,000tweets, 4x savings than the baseline. To the best of our knowledge, our work isthe first to predict the polarization level of the topics with low-resourcetweets. Our findings have profound implications for the news media, allowingjournalists to detect and disseminate polarizing information quickly andefficiently.

How Many Tweets DoWe Need?: Efficient Mining of Short-Term Polarized Topics on Twitter: A Case Study From Japan