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- W2023102811 abstract "During specific real-world events, some users of microblogging platforms could provide exclusive information about those events. The identification of such prominent users depends on several factors such as the freshness and the relevance of their shared information. This work proposes a probabilistic model for the identification of prominent users in microblogs during specific events. The model is based on learning and classifying user behavior over time using Mixture of Gaussians Hidden Markov Models. A user is characterized by a temporal sequence of feature vectors describing his activities. The features computed at each time-stamp are designed to reflect both the on- and off-topic activities of users, and they are computationally feasible in real-time." @default.
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- W2023102811 date "2015-10-17" @default.
- W2023102811 modified "2023-09-27" @default.
- W2023102811 title "Identification of Microblogs Prominent Users during Events by Learning Temporal Sequences of Features" @default.
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- W2023102811 doi "https://doi.org/10.1145/2806416.2806612" @default.
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