News articles similarity for automatic media bias detection in Polish news portals

Baraniak, Katarzyna; Sydow, Marcin

doi:10.15439/2018f359

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…The dates of the articles span from January 2020 to January 2022. The size of the collection is larger than (or comparable to) Arabic and non-Arabic news article similarity experiments conducted based on labelled data [44][45][46][47]. [44] [45] [46] [47].…”

Section: Data Pre-processingmentioning

confidence: 99%

Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

Daoud¹

2023

AETiC

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This article tackles the problem of finding similarity between web time-sensitive news articles, which can be a challenge. This challenge was approached with a novel methodology that uses supervised learning algorithms with carefully selected features (Semantic, Lexical and Temporal features (content and contextual features)). The proposed approach considers not only the textual content, which is a well-studied approach that may yield misleading results, but also the context, community engagement, and community-deduced importance of that news article. This paper details the major procedures of title pair pre-processing, analysis of lexical units, feature engineering, and similarity measures. Thousands of web articles are being published every second, and therefore, it is essential to determine the similarity of these articles efficiently without wasting time on unnecessary text processing of the bodies. Hence, the proposed approach focuses on short contents (titles) and context. The conducted experiment showed high precision and accuracy on a Really Simple Syndication (RSS) dataset of 8000 Arabic news article pairs collected automatically from 10 different news sources. The proposed approach achieved an accuracy of 0.81. Contextual features increased the accuracy and the precision. The proposed algorithm achieved a 0.89 correlation with the evaluations of two human judges based on Pearson’s Correlation Coefficient. The results outperform the state-of-the-art systems on Arabic news articles.

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Section: Data Pre-processingmentioning

confidence: 99%

Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

Daoud¹

2023

AETiC

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“…Examples of this type of research would include Al-Gamde and Tenbrink's paper on how the Syrian civil war was covered by Iranian news agency [21] or Al-Sarraj and Lubbad's analysis on how the Palestinian/Israeli Conflict is covered by western media [22]. Sometimes, the analysis is also done to cover regional media bias by grouping articles in countries like Poland [23] or an event such as the election in Brazil [24].…”

Section: B Natural Language Processing Approachesmentioning

confidence: 99%

Data Driven Model to Investigate Political Bias in Mainstream Media

Ness¹,

Fatima

Oghaz

2023

IEEE Access

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Media bias refers to the tendency of mainstream media outlets to report news in a way that reflects their own political, social, or ideological beliefs or preferences. Such bias may obfuscate facts, manipulate public beliefs, misinform readers, narrow perspectives and viewpoints, and result in greater polarization and division. To counter this issue, this study presents a model for quantifying media bias, aimed at enabling individuals to make more informed media choices. The proposed media analysis model includes a pipeline that gathers articles from three distinct sources: mainstream media news outlets, known conservative outlets, and known liberal media outlets. The collected articles were subjected to a range of text pre-processing operations and subsequently, curated n-gram and topic lists were generated. Several classification models including BERT, logistic regression, random forest, multinomial, and long short-term memory (LSTM) were created and fine-tuned on polarized news sources and used for analyzing news articles from the mainstream media. Among the various classification models that we investigated in this study, BERT achieved overall higher accuracy across the majority of topics. The analysis of mainstream media on various topics yielded different results, with some being balanced and others leaning left or right, depending on the topic. The research also suggests the effectiveness of using highly polarized news sources for developing models to predict media bias.

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“…An application of news article similarities in the form of detecting media bias was described in [12]. The authors leveraged machine learning techniques such as Support Vector Machines, Logistic Regression and Siamese networks [13] to compute a similarity score for pairs of articles and also approached the problem of finding all similar items with a give article for a given documents corpus.…”

Section: Related Workmentioning

confidence: 99%

Finding Patient Zero and Tracking Narrative Changes in the Context of Online Disinformation Using Semantic Similarity Analysis

et al. 2023

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Disinformation in the form of news articles, also called fake news, is used by multiple actors for nefarious purposes, such as gaining political advantages. A key component for fake news detection is the ability to find similar articles in a large documents corpus, for tracking narrative changes and identifying the root source (patient zero) of a particular piece of information. This paper presents new techniques based on textual and semantic similarity that were adapted for achieving this goal on large datasets of news articles. The aim is to determine which of the implemented text similarity techniques is more suitable for this task. For text similarity, a Locality-Sensitive Hashing is applied on n-grams extracted from text to produce representations that are further indexed to facilitate the quick discovery of similar articles. The semantic textual similarity technique is based on sentence embeddings from pre-trained language models, such as BERT, and Named Entity Recognition. The proposed techniques are evaluated on a collection of Romanian articles to determine their performance in terms of quality of results and scalability. The presented techniques produce competitive results. The experimental results show that the proposed semantic textual similarity technique is better at identifying similar text documents, while the Locality-Sensitive Hashing text similarity technique outperforms it in terms of execution time and scalability. Even if they were evaluated only on Romanian texts and some of them are based on pre-trained models for the Romanian language, the methods that are the basis of these techniques allow their extension to other languages, with few to no changes, provided that there are pre-trained models for other languages as well. As for a cross-lingual setup, more changes are needed along with tests to demonstrate this capability. Based on the obtained results, one may conclude that the presented techniques are suitable to be integrated into a decentralized anti-disinformation platform for fact-checking and trust assessment.

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News articles similarity for automatic media bias detection in Polish news portals

Cited by 9 publications

References 5 publications

Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

Similarity Detection of Time-Sensitive Online News Articles Based on RSS Feeds and Contextual Data

Data Driven Model to Investigate Political Bias in Mainstream Media

Finding Patient Zero and Tracking Narrative Changes in the Context of Online Disinformation Using Semantic Similarity Analysis

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