This paper describes the participation of team QUST in the SemEval2023 task3. The monolingual models are first evaluated with the under-sampling of the majority classes in the early stage of the task. Then, the pre-trained multilingual model is fine-tuned with a combination of the class weights and the sample weights. Two different fine-tuning strategies, the task-agnostic and the task-dependent, are further investigated. All experiments are conducted under the 10-fold cross-validation, the multilingual approaches are superior to the monolingual ones. The submitted system achieves the second best in Italian and Spanish (zero-shot) in subtask-1.

This paper describes the participation of team “bertha-von-suttner” in the SemEval2019 task 4 Hyperpartisan News Detection task. Our system uses sentence representations from averaged word embeddings generated from the pre-trained ELMo model with Convolutional Neural Networks and Batch Normalization for predicting hyperpartisan news. The final predictions were generated from the averaged predictions of an ensemble of models. With this architecture, our system ranked in first place, based on accuracy, the official scoring metric.

News media typically present biased accounts of news stories, and different publications present different angles on the same event. In this research, we investigate how different publications differ in their approach to stories about climate change, by examining the sentiment and topics presented. To understand these attitudes, we find sentiment targets by combining Latent Dirichlet Allocation (LDA) with SentiWordNet, a general sentiment lexicon. Using LDA, we generate topics containing keywords which represent the sentiment targets, and then annotate the data using SentiWordNet before regrouping the articles based on topic similarity. Preliminary analysis identifies clearly different attitudes on the same issue presented in different news sources. Ongoing work is investigating how systematic these attitudes are between different publications, and how these may change over time.