Hyperpartisan News Detection Research Articles

Politically-oriented information inference from text

The inference of politically-oriented information from text data is a popular research topic in Natural Language Processing (NLP) at both text- and author-level. In recent years, studies of this kind have been implemented with the aid of text representations ranging from simple count-based models (e.g., bag-of-words) to sequence-based models built from transformers (e.g., BERT). Despite considerable success, however, we may still ask whether results may be improved further by combining these models with additional text representations. To shed light on this issue, the present work describes a series of experiments to compare a number of strategies for political bias and ideology inference from text data using sequence-based BERT models, syntax-and semantics-driven features, and examines which of these representations (or their combinations) improve overall model accuracy. Results suggest that one particular strategy - namely, the combination of BERT language models with syntactic dependencies - significantly outperforms well-known count- and sequence-based text classifiers alike. In particular, the combined model has been found to improve accuracy across all tasks under consideration, outperforming the SemEval hyperpartisan news detection top-performing system by up to 6%, and outperforming the use of BERT alone by up to 21%, making a potentially strong case for the use of heterogeneous text representations in the present tasks.

Multi-Stage Prompt Tuning for Political Perspective Detection in Low-Resource Settings

Political perspective detection in news media—identifying political bias in news articles—is an essential but challenging low-resource task. Prompt-based learning (i.e., discrete prompting and prompt tuning) achieves promising results in low-resource scenarios by adapting a pre-trained model to handle new tasks. However, these approaches suffer performance degradation when the target task involves a textual domain (e.g., a political domain) different from the pre-training task (e.g., masked language modeling on a general corpus). In this paper, we develop a novel multi-stage prompt tuning framework for political perspective detection. Our method involves two sequential stages: a domain- and task-specific prompt tuning stage. In the first stage, we tune the domain-specific prompts based on a masked political phrase prediction (MP3) task to adjust the language model to the political domain. In the second task-specific prompt tuning stage, we only tune task-specific prompts with a frozen language model and domain-specific prompts for downstream tasks. The experimental results demonstrate that our method significantly outperforms fine-tuning (i.e., model tuning) methods and state-of-the-art prompt tuning methods on the SemEval-2019 Task 4: Hyperpartisan News Detection and AllSides datasets.

Detection of hyperpartisan news articles using natural language processing technique

Yellow journalism has increased the spread of hyperpartisan news on the internet. It is very difficult for online news article readers to distinguish hyperpartisan news articles from mainstream news articles. There is a need for an automated model that can detect hyperpartisan news on the internet and tag them as hyperpartisan so that it is very easy for readers to avoid that news. A hyperpartisan news detection article was developed by using three different natural language processing techniques named BERT, ELMo, and Word2vec. This research used the bi-article dataset published at SEMEVAL-2019. The ELMo word embeddings which are trained on a Random forest classifier has got an accuracy of 0.88, which is much better than other state of art models. The BERT and Word2vec models have got the same accuracy of 0.83. This research tried different sentence input lengths to BERT and proved that BERT can extract context from local words. Evidenced from the described ML models, this study will assist the governments, news’ readers, and other political stakeholders to detect any hyperpartisan news, and also helps policy to track, and regulate, misinformation about the political parties and their leaders.