The volume of news increases everyday, triggering competition for users’ attention. Predicting which topics will become trendy has many applications in domains like marketing or politics, where it is crucial to anticipate how much interest a product or a person will attract. We propose a model for representing topic popularity behavior across time and to predict if a topic will become trendy in the future. Furthermore, we tested our proposal on a real data set from Yahoo News and analysed the performance of various classifiers for the topic popularity prediction task. Experiments confirmed the validity of the proposed model.
Prado-Romero, M. A.; Celi, A.; Stilo, Giovanni; Coto-Santiesteban, A.. (2019). TSTM: A Model to Predict Topics’ Popularity in News Providers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 96- 106). Springer. Isbn: 978-3-030-33903-6. Doi: 10.1007/978-3-030-33904-3_9. https://www.springer.com/series/558.
TSTM: A Model to Predict Topics’ Popularity in News Providers
Stilo G.Membro del Collaboration Group
;
2019
Abstract
The volume of news increases everyday, triggering competition for users’ attention. Predicting which topics will become trendy has many applications in domains like marketing or politics, where it is crucial to anticipate how much interest a product or a person will attract. We propose a model for representing topic popularity behavior across time and to predict if a topic will become trendy in the future. Furthermore, we tested our proposal on a real data set from Yahoo News and analysed the performance of various classifiers for the topic popularity prediction task. Experiments confirmed the validity of the proposed model.
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