DpgMedia2019: A Dutch News Dataset for Partisanship Detection

Binary and Multitask Classification Model for Dutch Anaphora Resolution: Die/Dat Prediction

The correct use of Dutch pronouns 'die' and 'dat' is a stumbling block for both native and non-native speakers of Dutch due to the multiplicity of syntactic functions and the dependency on the antecedent's gender and number. Drawing on previous research conducted on neural context-dependent dt-mistake correction models (Heyman et al. 2018), this study constructs the first neural network model for Dutch demonstrative and relative pronoun resolution that specifically focuses on the correction and part-of-speech prediction of these two pronouns. Two separate datasets are built with sentences obta…

新闻标题中超党派性的计算评估

Computational Assessment of Hyperpartisanship in News Titles

本文采用人机协同框架构建超党派新闻标题检测数据集，利用transformer模型进行检测，并通过计算分析量化了不同媒体偏见群体在标题党派性程度、主题分布及语言距离上的差异与动态变化。

Detecting Polarized Topics Using Partisanship-aware Contextualized Topic Embeddings

Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence. Early identification of polarized topics is thus an urgent matter that can help mitigate conflict. However, accurate measurement of topic-wise polarization is still an open research challenge. To address this gap, we propose Partisanship-aware Contextualized Topic Embeddings (PaCTE), a method to automatically detect polarized topics from partisan news sources. Specifically, utilizing a language model that has been finetuned on recognizing partisanship of the news articles, we represe…

A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation diffe…

Distinguishing Commercial from Editorial Content in News

How can we distinguish commercial from editorial content in news, or more specifically, differentiate between advertorials and regular news articles? An advertorial is a commercial message written and formatted as an article, making it harder for readers to recognize these as advertising, despite the use of disclaimers. In our research we aim to differentiate the two using a machine learning model, and a lexicon derived from it. This was accomplished by scraping 1.000 articles and 1.000 advertorials from four different Dutch news sources and classifying these based on textual features. With th…

365 Dots in 2019: Quantifying Attention of News Sources

We investigate the overlap of topics of online news articles from a variety of sources. To do this, we provide a platform for studying the news by measuring this overlap and scoring news stories according to the degree of attention in near-real time. This can enable multiple studies, including identifying topics that receive the most attention from news organizations and identifying slow news days versus major news days. Our application, StoryGraph, periodically (10-minute intervals) extracts the first five news articles from the RSS feeds of 17 US news media organizations across the partisans…