News sources undergo the process of selecting newsworthy information when covering a certain topic. The process inevitably exhibits selection biases, i.e. news sources' typical patterns of choosing what information to include in news coverage, due to their agenda differences. To understand the magnitude and implications of selection biases, one must first discover (1) on what topics do sources typically have diverging definitions of "newsworthy" information, and (2) do the content selection patterns correlate with certain attributes of the news sources, e.g. ideological leaning, etc.
The goal of the paper is to investigate and discuss the challenges of building scalable NLP systems for discovering patterns of media selection biases directly from news content in massive-scale news corpora, without relying on labeled data. To facilitate research in this domain, we propose and study a conceptual framework, where we compare how sources typically mention certain controversial entities, and use such as indicators for the sources' content selection preferences. We empirically show the capabilities of the framework through a case study on NELA-2020, a corpus of 1.8M news articles in English from 519 news sources worldwide. We demonstrate an unsupervised representation learning method to capture the selection preferences for how sources typically mention controversial entities. Our experiments show that that distributional divergence of such representations, when studied collectively across entities and news sources, serve as good indicators for an individual source's ideological leaning. We hope our findings will provide insights for future research on media selection biases.
In this work, we focus on the problem of distinguishing a human written news article from a news article that is created by manipulating entities in a human written news article (e.g., replacing entities with factually incorrect entities). Such manipulated articles can mislead the reader by posing as a human written news article. We propose a neural network based detector that detects manipulated news articles by reasoning about the facts mentioned in the article. Our proposed detector exploits factual knowledge via graph convolutional neural network along with the textual information in the n…
University of Warwick（华威大学）；Alan Turing Institute（艾伦·图灵研究所）；Filament AI（Filament AI公司）；Bar-Ilan University（巴伊兰大学）；Aberystwyth University（阿伯里斯特威斯大学）
Cross-document co-reference resolution (CDCR) is the task of identifying and linking mentions to entities and concepts across many text documents. Current state-of-the-art models for this task assume that all documents are of the same type (e.g. news articles) or fall under the same theme. However, it is also desirable to perform CDCR across different domains (type or theme). A particular use case we focus on in this paper is the resolution of entities mentioned across scientific work and newspaper articles that discuss them. Identifying the same entities and corresponding concepts in both sci…
We present Namesakes, a dataset of ambiguously named entities obtained from English-language Wikipedia and news articles. It consists of 58862 mentions of 4148 unique entities and their namesakes: 1000 mentions from news, 28843 from Wikipedia articles about the entity, and 29019 Wikipedia backlink mentions. Namesakes should be helpful in establishing challenging benchmarks for the task of named entity linking (NEL).
Singapore University of Technology and Design（新加坡科技设计大学）
Blame games tend to follow major disruptions, be they financial crises, natural disasters or terrorist attacks. To study how the blame game evolves and shapes the dominant crisis narratives is of great significance, as sense-making processes can affect regulatory outcomes, social hierarchies, and cultural norms. However, it takes tremendous time and efforts for social scientists to manually examine each relevant news article and extract the blame ties (A blames B). In this study, we define a new task, Blame Tie Extraction, and construct a new dataset related to the United States financial cris…
University of Konstanz（康斯坦茨大学）；Heidelberg Academy of Sciences and Humanities（海德堡科学院与人文学院）；University of Zurich（苏黎世大学）；University of Wuppertal（伍珀塔尔大学）
Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news as an essential information source in individual and societal decision making. This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles. Investigating characteristics of sentiment in news and contrasting them to …
University of Michigan（密歇根大学）；Northeastern University（东北大学）
Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles o…
University of Houston（休斯顿大学）；Universidad Nacional de Colombia（哥伦比亚国立大学）
Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Rando…
L3S Research Center（L3S研究中心）；Leibniz Universität Hannover（莱布尼茨汉诺威大学）
Long-running, high-impact events such as the Boston Marathon bombing often develop through many stages and involve a large number of entities in their unfolding. Timeline summarization of an event by key sentences eases story digestion, but does not distinguish between what a user remembers and what she might want to re-check. In this work, we present a novel approach for timeline summarization of high-impact events, which uses entities instead of sentences for summarizing the event at each individual point in time. Such entity summaries can serve as both (1) important memory cues in a retrosp…
Northeastern University（东北大学）；ByteDance AI Lab（字节跳动人工智能实验室）；Alibaba Group（阿里巴巴集团）
Automatic identification of mentioned entities in social media posts facilitates quick digestion of trending topics and popular opinions. Nonetheless, this remains a challenging task due to limited context and diverse name variations. In this paper, we study the problem of entity linking for Chinese news comments given mentions' spans. We hypothesize that comments often refer to entities in the corresponding news article, as well as topics involving the entities. We therefore propose a novel model, XREF, that leverages attention mechanisms to (1) pinpoint relevant context within comments, and …
Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge…
How to Understand Named Entities: Using Common Sense for News Captioning
School of Electrical and Information Engineering, Tianjin University（天津大学电气与信息工程学院）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）
University of California, Davis（加州大学戴维斯分校）；University of California, Berkeley（加州大学伯克利分校）；Columbia University（哥伦比亚大学）
News Image Captioning requires describing an image by leveraging additional context from a news article. Previous works only coarsely leverage the article to extract the necessary context, which makes it challenging for models to identify relevant events and named entities. In our paper, we first demonstrate that by combining more fine-grained context that captures the key named entities (obtained via an oracle) and the global context that summarizes the news, we can dramatically improve the model's ability to generate accurate news captions. This begs the question, how to automatically extrac…
University of Science and Technology of China（中国科学技术大学）；Microsoft Research Asia（微软亚洲研究院）；Tsinghua University（清华大学）；Microsoft Corp.（微软公司）
News articles usually contain knowledge entities such as celebrities or organizations. Important entities in articles carry key messages and help to understand the content in a more direct way. An industrial news recommender system contains various key applications, such as personalized recommendation, item-to-item recommendation, news category classification, news popularity prediction and local news detection. We find that incorporating knowledge entities for better document understanding benefits these applications consistently. However, existing document understanding models either represe…
The goal of this paper is to summarize methodologies used in extracting entities and topics from a database of criminal records and from a database of newspapers. Statistical models had successfully been used in studying the topics of roughly 300,000 New York Times articles. In addition, these models had also been used to successfully analyze entities related to people, organizations, and places (D Newman, 2006). Additionally, analytical approaches, especially in hotspot mapping, were used in some researches with an aim to predict crime locations and circumstances in the future, and those appr…
Detecting important events in high volume news streams is an important task for a variety of purposes.The volume and rate of online news increases the need for automated event detection methods thatcan operate in real time. In this paper we develop a network-based approach that makes the workingassumption that important news events always involve named entities (such as persons, locationsand organizations) that are linked in news articles. Our approach uses natural language processingtechniques to detect these entities in a stream of news articles and then creates a time-stamped seriesof netwo…
The foundation for the research of summarization in the Czech language was laid by the work of Straka et al. (2018). They published the SumeCzech, a large Czech news-based summarization dataset, and proposed several baseline approaches. However, it is clear from the achieved results that there is a large space for improvement. In our work, we focus on the impact of named entities on the summarization of Czech news articles. First, we annotate SumeCzech with named entities. We propose a new metric ROUGE_NE that measures the overlap of named entities between the true and generated summaries, and…
Clustering web documents has numerous applications, such as aggregating news articles into meaningful events, detecting trends and hot topics on the Web, preserving diversity in search results, etc. At the same time, the importance of named entities and, in particular, the ability to recognize them and to solve the associated co-reference resolution problem are widely recognized as key enabling factors when mining, aggregating and comparing content on the Web. Instead of considering these two problems separately, we propose in this paper a method that tackles jointly the problem of clustering …
Understanding who blames or supports whom in news text is a critical research question in computational social science. Traditional methods and datasets for sentiment analysis are, however, not suitable for the domain of political text as they do not consider the direction of sentiments expressed between entities. In this paper, we propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document, which we call directed sentiment extraction. From a million-scale news corpus, we construct a dataset of news sentences where sentiment rel…
Center of Excellence on Soft Computing and Intelligent Information Processing（软计算与智能信息处理卓越中心）；Ferdowsi University of Mashhad（马什哈德菲尔多西大学）
News agencies publish news on their websites all over the world. Moreover, creating novel corpuses is necessary to bring natural processing to new domains. Textual processing of online news is challenging in terms of the strategy of collecting data, the complex structure of news websites, and selecting or designing suitable algorithms for processing these types of data. Despite the previous works which focus on creating corpuses for Iran news in Persian, in this paper, we introduce a new corpus for English news of a national news agency. ISNA-Set is a new dataset of English news of Iranian Stu…
Commodity News contains a wealth of information such as sum-mary of the recent commodity price movement and notable events that led tothe movement. Through event extraction, useful information extracted fromcommodity news is extremely useful in mining for causal relation betweenevents and commodity price movement, which can be used for commodity priceprediction. To facilitate the future research, we introduce a new dataset withthe following information identified and annotated: (i) entities (both nomi-nal and named), (ii) events (trigger words and argument roles), (iii) eventmetadata: modality…
This paper discusses the development of trainable statistical models for extracting content from television and radio news broadcasts. In particular we concentrate on statistical finite state models for identifying proper names and other named entities in broadcast speech. Two models are presented: the first represents name class information as a word attribute; the second represents both word-word and class-class transitions explicitly. A common n-gram based formulation is used for both models. The task of named entity identification is characterized by relatively sparse training data and iss…
Wright State University（莱特州立大学）；University of South Carolina（南卡罗来纳大学）；Arizona State University（亚利桑那州立大学）；Mahidol University（玛希隆大学）
With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that …
A Novel Method for News Article Event-Based Embedding
Department of Information Systems Engineering, Ben-Gurion University of the Negev（信息系统工程系，贝纳维德大学）；The Ohio State University（俄亥俄州立大学）；National Bureau of Economic Research（国家经济研究局）
We are presenting a text analysis tool set that allows analysts in various fields to sieve through large collections of multilingual news items quickly and to find information that is of relevance to them. For a given document collection, the tool set automatically clusters the texts into groups of similar articles, extracts names of places, people and organisations, lists the user-defined specialist terms found, links clusters and entities, and generates hyperlinks. Through its daily news analysis operating on thousands of articles per day, the tool also learns relationships between people an…
In this paper, we describe a method for automatic creation of a knowledge source for text generation using information extraction over the Internet. We present a prototype system called PROFILE which uses a client-server architecture to extract noun-phrase descriptions of entities such as people, places, and organizations. The system serves two purposes: as an information extraction tool, it allows users to search for textual descriptions of entities; as a utility to generate functional descriptions (FD), it is used in a functional-unification based generation system. We present an evaluation …
News headline generation is an essential problem of text summarization because it is constrained, well-defined, and is still hard to solve. Models with a limited vocabulary can not solve it well, as new named entities can appear regularly in the news and these entities often should be in the headline. News articles in morphologically rich languages such as Russian require model modifications due to a large number of possible word forms. This study aims to validate that models with a possibility of copying words from the original article performs better than models without such an option. The p…
The goal in the NER task is to classify proper nouns of a text into classes such as person, location, and organization. This is an important preprocessing step in many NLP tasks such as question-answering and summarization. Although many research studies have been conducted in this area in English and the state-of-the-art NER systems have reached performances of higher than 90 percent in terms of F1 measure, there are very few research studies for this task in Persian. One of the main important causes of this may be the lack of a standard Persian NER dataset to train and test NER systems. In t…
We investigate the overlap of topics of online news articles from a variety of sources. To do this, we provide a platform for studying the news by measuring this overlap and scoring news stories according to the degree of attention in near-real time. This can enable multiple studies, including identifying topics that receive the most attention from news organizations and identifying slow news days versus major news days. Our application, StoryGraph, periodically (10-minute intervals) extracts the first five news articles from the RSS feeds of 17 US news media organizations across the partisans…
We extend the concept of Named Entities to Named Events - commonly occurring events such as battles and earthquakes. We propose a method for finding specific passages in news articles that contain information about such events and report our preliminary evaluation results. Collecting "Gold Standard" data presents many problems, both practical and conceptual. We present a method for obtaining such data using the Amazon Mechanical Turk service.
Lomonosov Moscow State University（莫斯科罗蒙诺索夫国立大学）；Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）
Texts can convey several types of inter-related information concerning opinions and attitudes. Such information includes the author's attitude towards mentioned entities, attitudes of the entities towards each other, positive and negative effects on the entities in the described situations. In this paper, we described the lexicon RuSentiFrames for Russian, where predicate words and expressions are collected and linked to so-called sentiment frames conveying several types of presupposed information on attitudes and effects. We applied the created frames in the task of extracting attitudes from …
International Institute of Information Technology, Bangalore（班加罗尔国际信息技术学院）；Indraprastha Institute of Information Technology, Delhi（德里印德拉普拉斯塔信息技术学院）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
Online news and social media have been the de facto mediums to disseminate information globally from the beginning of the last decade. However, bias in content and purpose of intentions are not regulated, and managing bias is the responsibility of content consumers. In this regard, understanding the stances and biases of news sources towards specific entities becomes important. To address this problem, we use pretrained language models, which have been shown to bring about good results with no task-specific training or few-shot training. In this work, we approach the problem of characterizing …
EAMA : Entity-Aware Multimodal Alignment Based Approach for News Image Captioning
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）
Being a global pandemic, the COVID-19 outbreak received global media attention. In this study, we analyze news publications from CNN and The Guardian - two of the world's most influential media organizations. The dataset includes more than 36,000 articles, analyzed using the clinical and biomedical Natural Language Processing (NLP) models from the Spark NLP for Healthcare library, which enables a deeper analysis of medical concepts than previously achieved. The analysis covers key entities and phrases, observed biases, and change over time in news coverage by correlating mined medical symptoms…
The coverage of different stakeholders mentioned in the news articles significantly impacts the slant or polarity detection of the concerned news publishers. For instance, the pro-government media outlets would give more coverage to the government stakeholders to increase their accessibility to the news audiences. In contrast, the anti-government news agencies would focus more on the views of the opponent stakeholders to inform the readers about the shortcomings of government policies. In this paper, we address the problem of stakeholder extraction from news articles and thereby determine the …
Leibniz Information Centre for Science and Technology (TIB)（莱布尼茨科学技术信息中心（TIB））；Leibniz Universität Hannover（莱布尼茨汉诺威大学）；L3S Research Center, Leibniz Universität Hannover（莱布尼茨汉诺威大学L3S研究中心）
The World Wide Web has become a popular source for gathering information and news. Multimodal information, e.g., enriching text with photos, is typically used to convey the news more effectively or to attract attention. Photo content can range from decorative, depict additional important information, or can even contain misleading information. Therefore, automatic approaches to quantify cross-modal consistency of entity representation can support human assessors to evaluate the overall multimodal message, for instance, with regard to bias or sentiment. In some cases such measures could give hi…
Pagination - the process of determining where to break an article across pages in a multi-article layout is a common layout challenge for most commercially printed newspapers and magazines. To date, no one has created an algorithm that determines a minimal pagination break point based on the content of the article. Existing approaches for automatic multi-article layout focus exclusively on maximizing content (number of articles) and optimizing aesthetic presentation (e.g., spacing between articles). However, disregarding the semantic information within the article can lead to overly aggressive…
