Examinando por Autor "Cuzzocrea, Alfredo"

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  • Miniatura
    Ítem
    Clustering validation inference
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-08) Figuera, Pau; Cuzzocrea, Alfredo; García Bringas, Pablo
    Clustering validation is applied to evaluate the quality of classifications. This step is crucial for unsupervised machine learning. A plethora of methods exist for this purpose; however, a common drawback is that statistical inference is not possible. In this study, we construct a density function for the cluster number. For this purpose, we use smooth techniques. Then, we apply non-negative matrix factorization using the Kullback–Leibler divergence. Employing a unique linearly independent uncorrelated observational variable hypothesis, we construct a sequence by varying the dimension of the span space of the factorization only using analytical techniques. The expectation of the limit of this sequence follows a gamma probability density function. Then, identifying the dimension of the factorization of the space span with clusters, we transform the estimation of the suitable dimension of the factorization into a probabilistic estimate of the number of clusters. This approach is an internal validation method that is suitable for numerical and categorical multivariate data and independent of the clustering technique. Our main achievement is a predictive clustering validation model with graphical abilities. It provides results in terms of credibility, thus making it possible to compare results such as expert judgment on a quantitative basis.
  • Miniatura
    Ítem
    Cyberbullying detection, prevention, and analysis on social media via trustable LSTM-autoencoder networks over synthetic data: The TLA-NET approach
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-02) Cuzzocrea, Alfredo; Akter, Mst Shapna; Shahriar, Hossain; García Bringas, Pablo
    The plague of cyberbullying on social media exerts a dangerous influence on human lives. Due to the fact that online social networks continue to daily expand, the proliferation of hate speech is also growing. Consequentially, distressing content is often implicated in the onset of depression and suicide-related behaviors. In this paper, we propose an innovative framework, named as the trustable LSTM-autoencoder network (TLA NET), which is designed for the detection of cyberbullying on social media by employing synthetic data. We introduce a state-of-the-art method for the automatic production of translated data, which are aimed at tackling data availability issues. Several languages, including Hindi and Bangla, continue to face research limitations due to the absence of adequate datasets. Experimental identification of aggressive comments is carried out via datasets in Hindi, Bangla, and English. By employing TLA NET and traditional models, such as long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), the LSTM-autoencoder, Word2vec, bidirectional encoder representations from transformers (BERT), and the Generative Pre-trained Transformer 2 (GPT-2), we perform the experimental identification of aggressive comments in datasets in Hindi, Bangla, and English. In addition to this, we employ evaluation metrics that include the F1-score, accuracy, precision, and recall, to assess the performance of the models. Our model demonstrates outstanding performance across all the datasets by achieving a remarkable 99% accuracy and positioning itself as a frontrunner when compared to previous works that make use of the dataset featured in this research
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    Ítem
    An innovative framework for supporting content-based authorship identification and analysis in social media networks
    (Oxford University Press, 2024-08) Gaviria de la Puerta, José; Pastor López, Iker; Tellaeche Iglesias, Alberto; Sanz Urquijo, Borja; Sanjurjo González, Hugo; Cuzzocrea, Alfredo; Bringas García, Pablo
    Content-based authorship identification is an emerging research problem in online social media networks, due to a wide collection of issues ranging from security to privacy preservation, from radicalization to defamation detection, and so forth. Indeed, this research has attracted a relevant amount of attention from the research community during the past years. The general problem becomes harder when we consider the additional constraint of identifying the same false profile over different social media networks, under obvious considerations. Inspired by this emerging research challenge, in this paper we propose and experimentally assess an innovative framework for supporting content-based authorship identification and analysis in social media networks.