Maintaining Journalistic Integrity in the Digital Age: A Comprehensive NLP Framework for Evaluating Online News Content
The Institute for Artificial Intelligence Research and Development of Serbia（塞尔维亚人工智能研究与发展研究所）；University of Belgrade（贝尔格莱德大学）；Universitas Negeri Padang（巴东国立大学）
Ljubisa Bojic, Nikola Prodanovic, Agariadne Dwinggo Samala
The rapid growth of online news platforms has led to an increased need for reliable methods to evaluate the quality and credibility of news articles. This paper proposes a comprehensive framework to analyze online news texts using natural language processing (NLP) techniques, particularly a language model specifically trained for this purpose, alongside other well-established NLP methods. The framework incorporates ten journalism standards-objectivity, balance and fairness, readability and clarity, sensationalism and clickbait, ethical considerations, public interest and value, source credibility, relevance and timeliness, factual accuracy, and attribution and transparency-to assess the quality of news articles. By establishing these standards, researchers, media organizations, and readers can better evaluate and understand the content they consume and produce. The proposed method has some limitations, such as potential difficulty in detecting subtle biases and the need for continuous updating of the language model to keep pace with evolving language patterns.
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism
School of Computing and Information Systems, The University of Melbourne（墨尔本大学计算与信息系统学院）；Institute for Advanced Algorithms Research, China（中国高等算法研究院）；Northeastern University, China（中国东北大学）；State Key Laboratory of Media Convergence Production Technology and Systems, China（中国媒体融合生产技术与系统国家重点实验室）
NewsInterview: a Dataset and a Playground to Evaluate LLMs' Ground Gap via Informational Interviews
University of California, Berkeley（加州大学伯克利分校）；University of Southern California（南加州大学）；Information Sciences Institute（信息科学研究所）；Northeastern University（东北大学）
LLM-guided headline rewriting for clickability enhancement without clickbait
Intelligent Systems, Afeka Academic College of Engineering（阿菲卡工程学院智能系统系）；School of Computer Science, Faculty of Sciences, HIT-Holon Institute of Technology（霍隆理工学院理学院计算机科学系）
Factchecking has always been a part of the journalistic process. However with newsroom budgets shrinking it is coming under increasing pressure just as the amount of false information circulating is on the rise. We therefore propose a method to increase the efficiency of the factchecking process, using the latest developments in Natural Language Processing (NLP). This method allows us to compare incoming claims to an existing corpus and return similar, factchecked, claims in a live system-allowing factcheckers to work simultaneously without duplicating their work.
On the topic of journalistic integrity, the current state of accurate, impartial news reporting has garnered much debate in context to the 2016 US Presidential Election. In pursuit of computational evaluation of news text, the statements (attributions) ascribed by media outlets to sources provide a common category of evidence on which to operate. In this paper, we develop an approach to compare partisan traits of news text attributions and apply it to characterize differences in statements ascribed to candidate, Hilary Clinton, and incumbent President, Donald Trump. In doing so, we present a m…
Soongsil University（崇实大学）；KAIST（韩国科学技术院）；Institute of Basic Science（基础科学研究院）
Quotes are critical for establishing credibility in news articles. A direct quote enclosed in quotation marks has a strong visual appeal and is a sign of a reliable citation. Unfortunately, this journalistic practice is not strictly followed, and a quote in the headline is often "contextomized." Such a quote uses words out of context in a way that alters the speaker's intention so that there is no semantically matching quote in the body text. We present QuoteCSE, a contrastive learning framework that represents the embedding of news quotes based on domain-driven positive and negative samples t…
Millions of news articles from hundreds of thousands of sources around the globe appear in news aggregators every day. Consuming such a volume of news presents an almost insurmountable challenge. For example, a reader searching on Bloomberg's system for news about the U.K. would find 10,000 articles on a typical day. Apple Inc., the world's most journalistically covered company, garners around 1,800 news articles a day. We realized that a new kind of summarization engine was needed, one that would condense large volumes of news into short, easy to absorb points. The system would filter out noi…
University of Konstanz（康斯坦茨大学）；University of Wuppertal（伍珀塔尔大学）
Event extraction from news articles is a commonly required prerequisite for various tasks, such as article summarization, article clustering, and news aggregation. Due to the lack of universally applicable and publicly available methods tailored to news datasets, many researchers redundantly implement event extraction methods for their own projects. The journalistic 5W1H questions are capable of describing the main event of an article, i.e., by answering who did what, when, where, why, and how. We provide an in-depth description of an improved version of Giveme5W1H, a system that uses syntacti…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Media seems to have become more partisan, often providing a biased coverage of news catering to the interest of specific groups. It is therefore essential to identify credible information content that provides an objective narrative of an event. News communities such as digg, reddit, or newstrust offer recommendations, reviews, quality ratings, and further insights on journalistic works. However, there is a complex interaction between different factors in such online communities: fairness and style of reporting, language clarity and objectivity, topical perspectives (like political viewpoint),…
Microsoft Cognitive Services Research Group（微软认知服务研究组）；Stanford University（斯坦福大学）
A typical journalistic convention in news articles is to deliver the most salient information in the beginning, also known as the lead bias. While this phenomenon can be exploited in generating a summary, it has a detrimental effect on teaching a model to discriminate and extract important information in general. We propose that this lead bias can be leveraged in our favor in a simple and effective way to pre-train abstractive news summarization models on large-scale unlabeled news corpora: predicting the leading sentences using the rest of an article. We collect a massive news corpus and cond…
Developing and improving computational approaches to covering news can increase journalistic output and improve the way stories are covered. In this work we approach the problem of covering crime stories in Los Angeles. We present a machine-in-the-loop system that covers individual crimes by (1) learning the prototypical coverage archetypes from classical news articles on crime to learn their structure and (2) using output from the Los Angeles Police department to generate "lede paragraphs", first structural unit of crime-articles. We introduce a probabilistic graphical model for learning arti…
taz2024full: Analysing German Newspapers for Gender Bias and Discrimination across Decades
Faculty for Computer Science and Mathematics, Hochschule München University of Applied Sciences（计算机科学与数学系，慕尼黑应用科学大学）；Department of Statistics, LMU Munich（统计学系，慕尼黑大学）；Munich Center for Machine Learning (MCML), LMU Munich（慕尼黑机器学习中心（MCML），慕尼黑大学）
Embracing Dialectic Intersubjectivity: Coordination of Different Perspectives in Content Analysis with LLM Persona Simulation
Department of Media and Information（媒体与信息系）；Michigan State University（密歇根州立大学）；College of Liberal Arts（人文学院）；Colorado State University（科罗拉多州立大学）；Department of Communication（传播系）；Department of Advertising and Public Relations（广告与公共关系系）；Department of Communication Studies（传播学系）；Texas Christian University（德克萨斯 Christian 大学）；Departments of Communication and Political Science（传播与政治学系）；University of California, Los Angeles（加州大学洛杉矶分校）
Center for Data Analytics and Biomedical Informatics（数据分析与生物信息学中心）；Temple University（天普大学）
Social scientists have shown that up to 50% if the content posted to a news article have no relation to its journalistic content. In this study we propose a classification algorithm to categorize user comments posted to a new article base don their alignment to its content. The alignment seek to match user comments to an article based on similarity off content, entities in discussion, and topic. We proposed a BERTAC, BAERT-based approach that learn jointly article-comment embeddings and infers the relevance class of comments. We introduce an ordinal classification loss that penalizes the diffe…
Enhancing Journalism with AI: A Study of Contextualized Image Captioning for News Articles using LLMs and LMMs
DFKI German Research Center for Artificial Intelligence（德国人工智能研究中心）；Applied Artificial Intelligence, Carl von Ossietzky University Oldenburg（奥尔登堡卡尔·冯·奥西茨基大学应用人工智能系）
The explosion in the amount of news and journalistic content being generated across the globe, coupled with extended and instantaneous access to information through online media, makes it difficult and time-consuming to monitor news developments and opinion formation in real time. There is an increasing need for tools that can pre-process, analyse and classify raw text to extract interpretable content; specifically, identifying topics and content-driven groupings of articles. We present here such a methodology that brings together powerful vector embeddings from Natural Language Processing wit…
Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI
Instituto Politécnico Nacional (IPN)（墨西哥国立理工学院）；Centro de Investigación en Computación (CIC)（计算机研究中心）；College of Engineering and Applied Science, University of Colorado Colorado Springs (UCCS)（科罗拉多大学科罗拉多斯普林斯分校工程与应用科学学院）
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
Penn State University（宾夕法尼亚州立大学）；Trinity College Dublin（都柏林圣三一学院）；MIT Lincoln Lab（麻省理工学院林肯实验室）；Visa Research（Visa研究）；Kempelen Institute of Intelligent Technologies（凯普勒智能技术研究所）
University of Science and Technology of China（中国科学技术大学）
Text content created by humans or language models is often stolen or misused by adversaries. Tracing text provenance can help claim the ownership of text content or identify the malicious users who distribute misleading content like machine-generated fake news. There have been some attempts to achieve this, mainly based on watermarking techniques. Specifically, traditional text watermarking methods embed watermarks by slightly altering text format like line spacing and font, which, however, are fragile to cross-media transmissions like OCR. Considering this, natural language watermarking metho…
