Fake News Spreaders Detection: Sometimes Attention Is Not All You Need
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,…
McRock at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Multi-Channel CNN, Hybrid LSTM, DistilBERT and XLNet
In this paper we propose four deep learning models for the task of detecting and classifying Patronizing and Condescending Language (PCL) using a corpus of over 13,000 annotated paragraphs in English. The task, hosted at SemEval-2022, consists of two different subtasks. The Subtask 1 is a binary classification problem. Namely, given a paragraph, a system must predict whether or not it contains any form of PCL. The Subtask 2 is a multi-label classification task. Given a paragraph, a system must identify which PCL categories express the condescension. A paragraph might contain one or more categories of PCL. To face with the first subtask we propose a multi-channel Convolutional Neural Network…
COURAGE at CheckThat! 2022: Harmful Tweet Detection using Graph Neural Networks and ELECTRA
In this paper we propose a deep learning model based on graph machine learning (i.e. Graph Attention Convolution) and a pretrained transformer language model (i.e. ELECTRA). Our model was developed to detect harmful tweets about COVID-19 and was used to tackle subtask 1C (harmful tweet detection) at the CheckThat!Lab shared task organized as part of CLEF 2022. In this binary classification task, our proposed model reaches a binary F1 score (positive class label, i.e. harmful tweet) of 0.28 on the test set. We demonstrate that our approach outperforms the official baseline by 8% and describe our model as well as the experimental setup and results in detail. We also refer to limitations of th…
Detection of Hate Speech Spreaders using Convolutional Neural Networks
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…
WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach
The huge number of modern social network users has made the web a fertile ground for the growth and development of a plethora of recommender systems. To date, recommending a new user profile X to a given user U that could be interested in creating a relationship with X has been tackled using techniques based on content analysis, existing friendship relationships and other pieces of information coming from different social networks or websites. In this paper we propose a recommending architecture - called WhoSNext (WSN) - tested on Twitter and which aim is promoting the creation of new relationships among users. As recent researches show, this is an interesting recommendation problem: for a …