Search results for " Author Profiling"

showing 3 items of 3 documents

T100: A modern classic ensemble to profile irony and stereotype spreaders

2022

In this work we propose a novel ensemble model based on deep learning and non-deep learning classifiers. The proposed model was developed by our team for participating at the Profiling Irony and Stereotype Spreaders (ISSs) task hosted at PAN@CLEF2022. Our ensemble (named T100), include a Logistic Regressor (LR) that classifies an author as ISS or not (nISS) considering the predictions provided by a first stage of classifiers. All these classifiers are able to reach state-of-the-art results on several text classification tasks. These classifiers (namely, the voters) are a Convolutional Neural Network (CNN), a Support Vector Machine (SVM), a Decision Tree (DT) and a Naive Bayes (NB) classifie…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore ING-INF/03 - Telecomunicazioniirony stereotypes author profiling text classification Twitter ensemble logistic regressor
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Fake News Spreaders Detection: Sometimes Attention Is Not All You Need

2022

Guided by a corpus linguistics approach, in this article we present a comparative evaluation of State-of-the-Art (SotA) models, with a special focus on Transformers, to address the task of Fake News Spreaders (i.e., users that share Fake News) detection. First, we explore the reference multilingual dataset for the considered task, exploiting corpus linguistics techniques, such as chi-square test, keywords and Word Sketch. Second, we perform experiments on several models for Natural Language Processing. Third, we perform a comparative evaluation using the most recent Transformer-based models (RoBERTa, DistilBERT, BERT, XLNet, ELECTRA, Longformer) and other deep and non-deep SotA models (CNN,…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazionitext classificationcorpus linguisticSettore ING-INF/03 - Telecomunicazionifake newTwitterauthor profilingconvolutional neural networkdeep learningNatural Language Processing (NLP)user classificationfake news; misinformation; Natural Language Processing (NLP); transformers; Twitter; convolutional neural networks; text classification; deep learning; machine learning; user classification; author profiling; corpus linguistics; linguistic analysismachine learningtransformermisinformationlinguistic analysisInformation Systems
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Detection of Hate Speech Spreaders using Convolutional Neural Networks

2021

In this paper we describe a deep learning model based on a Convolutional Neural Network (CNN). The model was developed for the Profiling Hate Speech Spreaders (HSSs) task proposed by PAN 2021 organizers and hosted at the 2021 CLEF Conference. Our approach to the task of classifying an author as HSS or not (nHSS) takes advantage of a CNN based on a single convolutional layer. In this binary classification task, on the tests performed using a 5-fold cross validation, the proposed model reaches a maximum accuracy of 0.80 on the multilingual (i.e., English and Spanish) training set, and a minimum loss value of 0.51 on the same set. As announced by the task organizers, the trained model presente…

Hate Speech Deep Learning Author Profiling Convolutional Neural Network Word EmbeddingDeep LearningEnglishWord EmbeddingTwitterHate SpeechAuthor ProfilingConvolutional Neural NetworkSpanish
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