We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets. Our approach, named CWUTM (Topic model based on co-occurrence word networks for unbalanced short text datasets), Our approach addresses the challenge of sparse and unbalanced short text topics by mitigating the effects of incidental word co-occurrence. This allows our model to prioritize the identification of scarce topics (Low-frequency topics). Unlike previous methods, CWUTM leverages co-occurrence word networks to capture the topic distribution of each word, and we enhanced the sensitivity in identifying scarce topics by redefining the calculation of node activity and normalizing the representation of both scarce and abundant topics to some extent. Moreover, CWUTM adopts Gibbs sampling, similar to LDA, making it easily adaptable to various application scenarios. Our extensive experimental validation on unbalanced short-text datasets demonstrates the superiority of CWUTM compared to baseline approaches in discovering scarce topics. According to the experimental results the proposed model is effective in early and accurate detection of emerging topics or unexpected events on social platforms.

Recent research has shown that graph degeneracy algorithms, which decompose a network into a hierarchy of nested subgraphs of decreasing size and increasing density, are very effective at detecting the good spreaders in a network. However, it is also known that degeneracy-based decompositions of a graph are unstable to small perturbations of the network structure. In Machine Learning, the performance of unstable classification and regression methods, such as fully-grown decision trees, can be greatly improved by using Perturb and Combine (P&C) strategies such as bagging (bootstrap aggregating). Therefore, we propose a P&C procedure for networks that (1) creates many perturbed versions of a given graph, (2) applies a node scoring function separately to each graph (such as a degeneracy-based one), and (3) combines the results. We conduct real-world experiments on the tasks of identifying influential spreaders in large social networks, and influential words (keywords) in small word co-occurrence networks. We use the k-core, generalized k-core, and PageRank algorithms as our vertex scoring functions. In each case, using the aggregated scores brings significant improvements compared to using the scores computed on the original graphs. Finally, a bias-variance analysis suggests that our P&C procedure works mainly by reducing bias, and that therefore, it should be capable of improving the performance of all vertex scoring functions, not only unstable ones.

Collective classification models attempt to improve classification performance by taking into account the class labels of related instances. However, they tend not to learn patterns of interactions between classes and/or make the assumption that instances of the same class link to each other (assortativity assumption). Blockmodels provide a solution to these issues, being capable of modelling assortative and disassortative interactions, and learning the pattern of interactions in the form of a summary network. The Supervised Blockmodel provides good classification performance using link structure alone, whilst simultaneously providing an interpretable summary of network interactions to allow a better understanding of the data. This work explores three variants of supervised blockmodels of varying complexity and tests them on four structurally different real world networks.