Abstract. Trending topics is the most popular term list in the different web services, such as Twitter and Google. The changes in people's interest in a specific trending topic are reflected in the changes of its popularity rank (up, down, and unchanged). This paper proposes a temporal modelling framework for predicting rank change of trending topics, and delivers the real-time prediction service with only historical rank data. Historical rank data show that almost 70% of trending topics tend to disappear and reappear later. We handled those missing values, using deletion, dummy variable, mean substitution, and expectation maximization. On the other hand, it is necessary to select the optimal window size for the historical rank data. An optimal window size is selected based on the minimum length of topic disappearance in the same topic but with a different context. We examined our approach with four different machine-learning techniques using the twitter trending topics dataset, which is collected for 2 years. As an application, we implemented a trends prediction service, called TrendsForecast, applying our prediction model for Twitter trending topics in 10 different countries.
Abstract. Online trending topics represent the most popular topics among users in certain online community, such as a country community. Trending topics in one community are different from others since the users in the community may discuss different topics from other communities. Surprisingly, almost 90% of trending topics are diffused among multiple online communities, so it shows peoples interests in a certain community can be shared to others in another community. The aim of this research is to predict the scale of trending topic diffusion among different online communities. The scale of diffusion represents the number of online communities that a trending topic diffuses. We proposed a diffusion scale prediction model for trending topics with the following four features, including community innovation feature, context feature, topic feature, and rank feature. We examined the proposed model with four different machine learning in predicting the scale of diffusion in Twitter Trending Topics among 8 English-speaking countries. Our model achieved the highest prediction accuracy (80.80%) with C4.5 decision tree.
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