Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTowards Quantifying Magnitude of Political Bias in News Articles Using a Novel Annotation Schema
نحو تحديد حجم التحيز السياسي في مقالات إخبارية باستخدام مخطط توضيحي جديد
257
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تحيز وسائل الإعلام هي ظاهرة سائدة موجودة في معظم أشكال الوسائط المطبوعة والإلكترونية مثل المقالات الإخبارية والمدونات أو التغريدات، وما إلى ذلك. نظرا لأن وسائل الإعلام تلعب دورا محوريا في تشكيل الرأي العام تجاه الأحداث السياسية، غالبا ما تستخدم كل من الأحزاب السياسية والإعلام في كثير من الأحيان هذه المصادركمنافذ لنشر التحيزات الخاصة بهم للجمهور.كانت هناك بعض الأبحاث حول الكشف عن التحيز السياسي في مقالات إخبارية.ومع ذلك، لا يحاول أي منه تحليل طبيعة التحيز أو تحديد حجم التحيز في نص معين.تقدم هذه الورقة تحيزا سياسيا مشروحا كوربوس بيز.POBICO-21، المشروح باستخدام مخطط مصمم خصيصا مع 10 ملصقات لالتقاط تقنيات مختلفة تستخدم لإنشاء تحيز سياسي في الأخبار.نخلق تصنيف هذه التقنيات بناء على مساهمتها في التحيز.بعد التحقق من صحة الترتيب، نقترح طرق لاستخدامها لتحديد حجم التحيز في المقالات الإخبارية السياسية.
Media bias is a predominant phenomenon present in most forms of print and electronic media such as news articles, blogs, tweets, etc. Since media plays a pivotal role in shaping public opinion towards political happenings, both political parties and media houses often use such sources as outlets to propagate their own prejudices to the public. There has been some research on detecting political bias in news articles. However, none of it attempts to analyse the nature of bias or quantify the magnitude ofthe bias in a given text. This paper presents a political bias annotated corpus viz. PoBiCo-21, which is annotated using a schema specifically designed with 10 labels to capture various techniques used to create political bias in news. We create a ranking of these techniques based on their contribution to bias. After validating the ranking, we propose methods to use it to quantify the magnitude of bias in political news articles.
References used
https://aclanthology.org/
rate research
Read More
We present a data set consisting of German news articles labeled for political bias on a five-point scale in a semi-supervised way. While earlier work on hyperpartisan news detection uses binary classification (i.e., hyperpartisan or not) and English
data, we argue for a more fine-grained classification, covering the full political spectrum (i.e., far-left, left, centre, right, far-right) and for extending research to German data. Understanding political bias helps in accurately detecting hate speech and online abuse. We experiment with different classification methods for political bias detection. Their comparatively low performance (a macro-F1 of 43 for our best setup, compared to a macro-F1 of 79 for the binary classification task) underlines the need for more (balanced) data annotated in a fine-grained way.
The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g
. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.
With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident. Previous works have demonstrated that KGs are prone to various social biases, a
nd have proposed multiple methods for debiasing them. However, in such studies, the focus has been on debiasing techniques, while the relations to be debiased are specified manually by the user. As manual specification is itself susceptible to human cognitive bias, there is a need for a system capable of quantifying and exposing biases, that can support more informed decisions on what to debias. To address this gap in the literature, we describe a framework for identifying biases present in knowledge graph embeddings, based on numerical bias metrics. We illustrate the framework with three different bias measures on the task of profession prediction, and it can be flexibly extended to further bias definitions and applications. The relations flagged as biased can then be handed to decision makers for judgement upon subsequent debiasing.
Most existing methods for automatic fact-checking start with a precompiled list of claims to verify. We investigate the understudied problem of determining what statements in news articles are worthy to fact-check. We annotate the argument structure
of 95 news articles in the climate change domain that are fact-checked by climate scientists at climatefeedback.org. We release the first multi-layer annotated corpus for both argumentative discourse structure (argument types and relations) and for fact-checked statements in news articles. We discuss the connection between argument structure and check-worthy statements and develop several baseline models for detecting check-worthy statements in the climate change domain. Our preliminary results show that using information about argumentative discourse structure shows slight but statistically significant improvement over a baseline of local discourse structure.
Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fake news datasets depend on a specific time period. Consequently, the
detection models trained on such a dataset have difficulty detecting novel fake news generated by political changes and social changes; they may possibly result in biased output from the input, including specific person names and organizational names. We refer to this problem as Diachronic Bias because it is caused by the creation date of news in each dataset. In this study, we confirm the bias, especially proper nouns including person names, from the deviation of phrase appearances in each dataset. Based on these findings, we propose masking methods using Wikidata to mitigate the influence of person names and validate whether they make fake news detection models robust through experiments with in-domain and out-of-domain data.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
