Media bias has been extensively studied by both social and computational sciences. However, current work still has a large reliance on human input and subjective assessment to label biases. This is especially true for cable news research. To address these issues, we develop an unsupervised machine learning method to characterize the bias of cable news programs without any human input. This method relies on the analysis of what topics are mentioned through Named Entity Recognition and how those topics are discussed through Stance Analysis in order to cluster programs with similar biases together. Applying our method to 2020 cable news transcripts, we find that program clusters are consistent over time and roughly correspond to the cable news network of the program. This method reveals the potential for future tools to objectively assess media bias and characterize unfamiliar media environments.
I present here an experimental system for identifying and annotating metaphor in corpora. It is designed to plug in to Metacorps, an experimental web app for annotating metaphor. As Metacorps users annotate metaphors, the system will use user annotations as training data. When the system is confident, it will suggest an identification and an annotation. Once approved by the user, this becomes more training data. This naturally allows for transfer learning, where the system can, with some known degree of reliability, classify one class of metaphor after only being trained on another class of me…
University of California, Santa Barbara（加州大学圣巴巴拉分校）；IBM Research（IBM研究院）
With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge. While previous datasets haveconcentrated on question answering (QA) forformal text like news and Wikipedia, wepresent the first large-scale dataset for QA oversocial media data. To ensure that the tweetswe collected are useful, we only gather tweetsused by journalists to write news articles. Wethen ask human annotators to write questionsand answers upon the…
Named Entity Recognition (NER) for Myanmar Language is essential to Myanmar natural language processing research work. In this work, NER for Myanmar language is treated as a sequence tagging problem and the effectiveness of deep neural networks on NER for Myanmar language has been investigated. Experiments are performed by applying deep neural network architectures on syllable level Myanmar contexts. Very first manually annotated NER corpus for Myanmar language is also constructed and proposed. In developing our in-house NER corpus, sentences from online news website and also sentences support…
University of Information Technology（信息技术大学）；VNU-HCM（胡志明市国家大学）
For the last two years, from 2020 to 2021, COVID-19 has broken disease prevention measures in many countries, including Vietnam, and negatively impacted various aspects of human life and the social community. Besides, the misleading information in the community and fake news about the pandemic are also serious situations. Therefore, we present the first Vietnamese community-based question answering dataset for developing question answering systems for COVID-19 called UIT-ViCoV19QA. The dataset comprises 4,500 question-answer pairs collected from trusted medical sources, with at least one answe…
University of Dayton（代顿大学）；Knoesis Research Center（Knoesis研究中心）
The ever-growing datasets published on Linked Open Data mainly contain encyclopedic information. However, there is a lack of quality structured and semantically annotated datasets extracted from unstructured real-time sources. In this paper, we present principles for developing a knowledge graph of interlinked events using the case study of news headlines published on Twitter which is a real-time and eventful source of fresh information. We represent the essential pipeline containing the required tasks ranging from choosing background data model, event annotation (i.e., event recognition and c…
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Pennsylvania State University（宾夕法尼亚州立大学）；Hong Kong Baptist University（香港浸会大学）
The COVID-19 pandemic poses a great threat to global public health. Meanwhile, there is massive misinformation associated with the pandemic which advocates unfounded or unscientific claims. Even major social media and news outlets have made an extra effort in debunking COVID-19 misinformation, most of the fact-checking information is in English, whereas some unmoderated COVID-19 misinformation is still circulating in other languages, threatening the health of less-informed people in immigrant communities and developing countries. In this paper, we make the first attempt to detect COVID-19 misi…
This is a paper for exploring various different models aiming at developing fake news detection models and we had used certain machine learning algorithms and we had used pretrained algorithms such as TFIDF and CV and W2V as features for processing textual data.
RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation
National University of Science and Technology POLITEHNICA Bucharest（布加勒斯特理工国立科学技术大学）；National Institute for Research and Development in Informatics - ICI Bucharest（布加勒斯特国家信息学研究与发展研究所）；Paris 1 Panthéon-Sorbonne University（巴黎第一大学先贤祠-索邦大学）；University of Bucharest（布加勒斯特大学）
Virginia Tech（弗吉尼亚理工大学）；NEC Labs America（NEC美国实验室）
Text summarization has been intensively studied in many languages, and some languages have reached advanced stages. Yet, Arabic Text Summarization (ATS) is still in its developing stages. Existing ATS datasets are either small or lack diversity. We build, LANS, a large-scale and diverse dataset for Arabic Text Summarization task. LANS offers 8.4 million articles and their summaries extracted from newspapers websites metadata between 1999 and 2019. The high-quality and diverse summaries are written by journalists from 22 major Arab newspapers, and include an eclectic mix of at least more than 7…
Unified Large Language Models for Misinformation Detection in Low-Resource Linguistic Settings
College of Science and Engineering（科学与工程学院）；Department of Computer Science（计算机科学系）；University of Hertfordshire（赫特福德郡大学）；Applied College, King Khalid University（国王·卡利德大学应用学院）；Faculty of Resilience（韧性学院）；Faculty of Computer Science（计算机科学系）
UTP University of Science and Technology（UTP科技大学）；National Technical University of Athens（雅典国立技术大学）；Universidad de Burgos（布尔戈斯大学）；Wrocław University of Science and Technology（弗罗茨瓦夫科技大学）
Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It …
Qatar Computing Research Institute（卡塔尔计算研究所）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）；Indiana University, Bloomington（印第安纳大学伯明顿分校）
Framing is an indispensable narrative device for news media because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames …
Recent advances in natural language processing have enabled automation of a wide range of tasks, including machine translation, named entity recognition, and sentiment analysis. Automated summarization of documents, or groups of documents, however, has remained elusive, with many efforts limited to extraction of keywords, key phrases, or key sentences. Accurate abstractive summarization has yet to be achieved due to the inherent difficulty of the problem, and limited availability of training data. In this paper, we propose a topic-centric unsupervised multi-document summarization framework to …
University College Dublin（都柏林大学学院）；Insight Centre for Data Analytics（洞察数据分析中心）
Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models. This paper proposes a simple but effective data augmentation method, which leverages the idea of pseudo-labelling to select samples from noisy distant supervision annotation datasets. The result shows that the proposed method improves the accuracy of biased news detection models.
Although a great deal of attention has been paid to how conspiracy theories circulate on social media and their factual counterpart conspiracies, there has been little computational work done on describing their narrative structures. We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media, and actual conspiracies reported in the news media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy…
Natural Language processing (NLP) represents the task of automatic handling of natural human language by machines.There is large spectrum of possible applications of NLP which help in automating tasks like translating text from one language to other, retrieving and summarizing data from very huge repositories, spam email filtering, identifying fake news in digital media, find sentiment and feedback of people, find political opinions and views of people on various government policies, provide effective medical assistance based on past history records of patient etc. Hindi is the official langua…
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Leiden University（莱顿大学）；Pontifícia Universidade Católica de Minas Gerais（米纳斯吉拉斯天主教大学）；Harvard University（哈佛大学）
WhatsApp is, as of 2018, a significant component of the global information and communication infrastructure, especially in developing countries. However, probably due to its strong end-to-end encryption, WhatsApp became an attractive place for the dissemination of misinformation, extremism and other forms of undesirable behavior. In this paper, we investigate the public perception of WhatsApp through the lens of media. We analyze two large datasets of news and show the kind of content that is being associated with WhatsApp in different regions of the world and over time. Our analyses include t…
With a growing interest in modeling inherent subjectivity in natural language, we present a linguistically-motivated process to understand and analyze the writing style of individuals from three perspectives: lexical, syntactic, and semantic. We discuss the stylistically expressive elements within each of these levels and use existing methods to quantify the linguistic intuitions related to some of these elements. We show that such a multi-level analysis is useful for developing a well-knit understanding of style - which is independent of the natural language task at hand, and also demonstrate…
University of Warwick（华威大学）；University of Sheffield（谢菲尔德大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；Alan Turing Institute（艾伦·图灵研究所）
Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e. pieces of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how natural language processing and data mining techniques may be used to find ways of determining their veracity. In this survey we introduce and discuss two types of rumours that circulate on social media; long-standing ru…
