Articles

AbstractIn this paper, we present an approach for classifying news articles as biased (i.e., hyperpartisan) or unbiased, based on a convolutional neural network. We experiment with various embedding methods (pretrained and trained on the training dataset) and variations of the convolutional neural network architecture and compare the results. When evaluating …...

AbstractThis paper describes the approach of team Kit Kittredge to SemEval-2019 Task 4: Hyperpartisan News Detection. The goal was binary classification of news articles into the categories of “biased” or “unbiased”. We had two software submissions: one a simple bag-of-words model, and the second an LSTM (Long Short Term Memory) …...

AbstractIn this paper we describe our participation in the SemEval 2019 shared task on hyperpartisan news detection. We present the system that we submitted for final evaluation and the three approaches that we used: sentiment, bias-laden words and filtered n-gram features. Our submitted model is a Linear SVM that solely …...

AbstractWe describe the system submitted by the Jack Ryder team to SemEval-2019 Task 4 on Hyperpartisan News Detection. The task asked participants to predict whether a given article is hyperpartisan, i.e., extreme-left or extreme-right. We proposed an approach based on BERT with fine-tuning, which was ranked 7th out 28 teams …...

AbstractThis paper describes our system for SemEval-2019 Task 4: Hyperpartisan News Detection (Kiesel et al., 2019). We use pretrained BERT (Devlin et al., 2018) architecture and investigate the effect of different fine tuning regimes on the final classification task. We show that additional pretraining on news domain improves the performance …...

AbstractThis report describes the starting point for a simple rule based hypothesis testing excercise on identifying hyperpartisan news items carried out by the Harry Friberg team from Gavagai. We used manually crafted metatopics, topics which often appear in hyperpartisan texts as rant conduits, together with tonality analysis to identify general …...

AbstractIn this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is heavily biased towards one side of the political spectrum. Our system receives an article in its raw, textual form, analyzes …...

AbstractThis paper reports an experiment carried out to investigate the relevance of several syntactic, stylistic and pragmatic features on the task of distinguishing between mainstream and partisan news articles. The results of the evaluation of different feature sets and the extent to which various feature categories could affect the performance …...

AbstractThis paper describes our submission to task 4 in SemEval 2019, i.e., hyperpartisan news detection. Our model aims at detecting hyperpartisan news by incorporating the style-based features and the content-based features. We extract a broad number of feature sets and use as our learning algorithms the GBDT and the n-gram …...

AbstractThis paper describes our system for detecting hyperpartisan news articles, which was submitted for the shared task in SemEval 2019 on Hyperpartisan News Detection. We developed a Support Vector Machine (SVM) model that uses TF-IDF of tokens, Language Inquiry and Word Count (LIWC) features, and structural features such as number …...

AbstractThis paper describes the Rouletabille participation to the Hyperpartisan News Detection task. We propose the use of different text classification methods for this task. Preliminary experiments using a similar collection used in (Potthast et al., 2018) show that neural-based classification methods reach state-of-the art results. Our final submission is composed …...

AbstractWhile fake news detection received quite a bit of attention in recent years, hyperpartisan news detection is still an underresearched topic. This paper presents our work towards building a classification system for hyperpartisan news detection in the context of the SemEval2019 shared task 4. We experiment with two different approaches …...

AbstractThis paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperpartisan argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. …...

AbstractWe use the 600 hand-labelled articles from SemEval Task 4 to hand-tune a classifier with 3000 features for the Hyperpartisan News Detection task. Our final system uses features based on bag-of-words (BoW), analysis of the article title, language complexity, and simple sentiment analysis in a naive Bayes classifier. We trained …...

AbstractWe use various natural processing and machine learning methods to perform the Hyperpartisan News Detection task. In particular, some of the features we look at are bag-of-words features, the title’s length, number of capitalized words in the title, and the sentiment of the sentences and the title. By adding these …...

AbstractThis paper describes our submission to the SemEval 2019 Hyperpartisan News Detection task. Our system aims for a linguistics-based document classification from a minimal set of interpretable features, while maintaining good performance. To this goal, we follow a feature-based approach and perform several experiments with different machine learning classifiers. Additionally, …...

AbstractThis paper describes our system (Fermi) for Task 4: Hyper-partisan News detection of SemEval-2019. We use simple text classification algorithms by transforming the input features to a reduced feature set. We aim to find the right number of features useful for efficient classification and explore multiple training models to evaluate …...

AbstractThis paper describes the Pioquinto Manterola Hyperpartisan News Detector, which participated in SemEval-2019 Task 4. Hyperpartisan news is highly polarized and takes a very biased or one–sided view of a particular story. We developed two variants of our system, the more successful was a Logistic Regression classifier based on unigram …...

AbstractWe investigate the recently developed Bidi- rectional Encoder Representations from Transformers (BERT) model (Devlin et al. 2018) for the hyperpartisan news detection task. Using a subset of hand-labeled articles from SemEval as a validation set, we test the performance of different parameters for BERT models. We find that accuracy from …...

AbstractIn this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019. Motivated by the late arrival of Doris Martin, we test a previously developed document classification system which consists of a combination of clustering features implemented on top of some simple shallow local features. …...