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- W2914521085 abstract "Twitter and social networks in general, participate more and more in everyday life. This is why they have become a fundamental source of information that reflects the ideas and opinions of their users. This paper shows how the most influential users, called influencers, can be decisive in defining whether a publication becomes popular or not, regardless of its content. To achieve this, we build a dataset of Spanish-writing users sampled from Twitter, along with the content generated and shared by them within a year. In a first phase, we use different algorithms to detect users who are “influencers”. In a second phase, we train a binary classifier to predict if a given tweet will be a trending publication, based on information about the activity of the influencers on the given tweet. We obtain a model with an $$F_1$$ -score close to $$79%$$ , based on the retweeting behavior of a $$10%$$ of the users dataset considered as influencers. Finally, we add two Natural Language Processing (NLP) techniques to analyze the content: Twitter-LDA topic modeling, and FastText word embeddings. While both models alone have an $$F_1$$ of less than $$50%$$ for trending prediction, FastText combined with the social model reaches an $$86.7%$$ score. We conclude that while analyzing the content can help to predict the popularity of a tweet, the influence of a user’s environment in the retweeting decision is surprisingly high." @default.
- W2914521085 created "2019-02-21" @default.
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- W2914521085 date "2019-01-01" @default.
- W2914521085 modified "2023-10-15" @default.
- W2914521085 title "Analyzing the Retweeting Behavior of Influencers to Predict Popular Tweets, with and Without Considering their Content" @default.
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- W2914521085 doi "https://doi.org/10.1007/978-3-030-11680-4_9" @default.
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