In this paper, we investigate the problem of automatically constructing characters' social network from movies. Unlike existing approaches that use co-appearance information to measure the relationship between two characters, we argue that a method that describes the characters' interaction, rather than the co-appearance, makes more sense. We propose a new scheme that quantifies the interaction of characters by the use of film-editing cues, based on which we construct the characters' social network. Experiments on real-world data validate the effectiveness of the proposed method. In addition, we show an application of discovering characters' social clusters enabled by the automatically constructed social network.
Mixture item response theory (IRT) models include a mixture of latent subpopulations such that there are qualitative differences between subgroups but within each subpopulation the measure model based on a continuous latent variable holds. Under this modeling framework, students can be characterized by both their location on a continuous latent variable and by their latent class membership according to Students’ responses. It is important to identify anchor items for constructing a common scale between latent classes beforehand under the mixture IRT framework. Then, all model parameters across latent classes can be estimated on the common scale. In the study, we proposed Q-matrix anchored mixture Rasch model (QAMRM), including a Q-matrix and the traditional mixture Rasch model. The Q-matrix in QAMRM can use class invariant items to place all model parameter estimates from different latent classes on a common scale regardless of the ability distribution. A simulation study was conducted, and it was found that the estimated parameters of the QAMRM recovered fairly well. A real dataset from the Certificate of Proficiency in English was analyzed with the QAMRM, LCDM. It was found the QAMRM outperformed the LCDM in terms of model fit indices.
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