{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T16:28:57Z","timestamp":1773160137823,"version":"3.50.1"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The Russia\u2013Ukraine War has emerged as a highly contentious global issue since 2022. While China and the UK are not directly involved in the conflict, considerable attention has been drawn to their positions and perspectives on this event. In such context, conducting a comparative study on how the British and Chinese mainstream media cover the Russia\u2013Ukraine conflict can provide valuable insights into the influence of ideological differences on news framing and shed light on the respective stances of these two news agencies. Employing an interdisciplinary methodology, this study integrates corpus tools, critical discourse analysis, text mining, and emotion computation to systematically analyze news reports covering the Russia\u2013Ukraine War from Reuters and Xinhua between 2022 and 2023. Results show different patterns in the news reports from the two investigated news agencies, including the monthly publication of news articles, the occurrence of prominent entities, and the thematic emphasis. Additionally, significant variations are identified in specific dimensions of emotion and emotional intensity, indicating the divergent stances of the two news agencies on a range of significant issues.<\/jats:p>","DOI":"10.1093\/llc\/fqae015","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T00:31:58Z","timestamp":1712017918000},"page":"609-624","source":"Crossref","is-referenced-by-count":5,"title":["Mining themes, emotions, and stance in the news coverage of the Russia\u2013Ukraine War from Reuters and Xinhua"],"prefix":"10.1093","volume":"39","author":[{"given":"Zhaokun","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Foreign Languages, Shanghai Jiao Tong University , Minhang District , Shanghai, 200240, China"}]}],"member":"286","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"2024061809545224800_fqae015-B1","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1177\/1940161220966948","article-title":"\u2018Reporting Bias in Coverage of Iran Protests by Global News Agencies\u2019","volume":"27","author":"Adegbola","year":"2022","journal-title":"The International Journal of Press\/Politics"},{"key":"2024061809545224800_fqae015-B2","first-page":"346","article-title":"\u2018Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War Using Machine Learning Technique\u2019","volume":"13","author":"Al Maruf","year":"2023","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"2024061809545224800_fqae015-B3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.asoc.2023.110404","article-title":"\u2018A Deep Learning-Based Sentiment Analysis Approach (MF-CNN-BILSTM) and Topic Modeling of Tweets Related to the Ukraine-Russia Conflict\u2019","volume":"143","author":"Aslan","year":"2023","journal-title":"Applied Soft Computing"},{"key":"2024061809545224800_fqae015-B4","author":"Baccianella","year":"2010"},{"key":"2024061809545224800_fqae015-B5","doi-asserted-by":"crossref","DOI":"10.1057\/9780230285712","volume-title":"Emotion Talk across Corpora","author":"Bednarek","year":"2008"},{"key":"2024061809545224800_fqae015-B6","volume-title":"News Discourse","author":"Bednarek","year":"2012"},{"key":"2024061809545224800_fqae015-B7","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1162\/COLI_a_00278","article-title":"\u2018Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications\u2019","volume":"43","author":"Benamara","year":"2017","journal-title":"Computational Linguistics"},{"key":"2024061809545224800_fqae015-B8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01638538809544689","article-title":"\u2018Adverbial Stance Types in English\u2019","volume":"11","author":"Biber","year":"1988","journal-title":"Discourse Processes"},{"key":"2024061809545224800_fqae015-B9","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1177\/1464884919899313","article-title":"\u2018Newsworthiness and Story Prominence: How the Presence of News Factors Relates to Upfront Position and Length of News Stories\u2019","volume":"23","author":"Boukes","year":"2022","journal-title":"Journalism"},{"key":"2024061809545224800_fqae015-B10","doi-asserted-by":"crossref","first-page":"803","DOI":"10.3758\/s13428-016-0743-z","article-title":"\u2018Sentiment Analysis and Social Cognition Engine (SEANCE): An Automatic Tool for Sentiment, Social Cognition, and Social-Order Analysis\u2019","volume":"49","author":"Crossley","year":"2017","journal-title":"Behavior Research Methods"},{"key":"2024061809545224800_fqae015-B11","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1080\/1461670X.2021.1889395","article-title":"\u2018Effects of News Factors on Users\u2019 News Attention and Selective Exposure on a News Aggregator Website\u2019","volume":"22","author":"Engelmann","year":"2021","journal-title":"Journalism Studies"},{"key":"2024061809545224800_fqae015-B12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/frai.2023.1163577","article-title":"\u2018Sentiment Analysis for Measuring Hope and Fear from Reddit Posts during the 2022 Russo-Ukrainian Conflict\u2019","volume":"6","author":"Guerra","year":"2023","journal-title":"Frontiers in Artificial Intelligence"},{"key":"2024061809545224800_fqae015-B13","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1515\/jisys-2022-0001","article-title":"\u2018Deep Learning Approach to Text Analysis for Human Emotion Detection from Big Data\u2019","volume":"31","author":"Guo","year":"2022","journal-title":"Journal of Intelligent Systems"},{"key":"2024061809545224800_fqae015-B15","volume-title":"Corpus Approaches to Evaluation: Phraseology and Evaluative Language","author":"Hunston","year":"2011"},{"key":"2024061809545224800_fqae015-B16","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198238546.001.0001","volume-title":"Evaluation in Text: Authorial Distance and the Construction of Discourse","author":"Hunston","year":"2000"},{"key":"2024061809545224800_fqae015-B17","doi-asserted-by":"crossref","DOI":"10.1057\/9780230511910","volume-title":"The Language of Evaluation: Appraisal in English","author":"Martin","year":"2005"},{"key":"2024061809545224800_fqae015-B18","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s41701-023-00147-w","article-title":"\u2018Emotion in Politics in Times of War: A Corpus Pragmatics Study\u2019,","volume":"7","author":"Mestre-Mestre","year":"2023","journal-title":"Corpus Pragmatics,"},{"key":"2024061809545224800_fqae015-B19","author":"Mohammad","year":"2018"},{"key":"2024061809545224800_fqae015-B20","author":"Mohammad","year":"2023"},{"key":"2024061809545224800_fqae015-B21","author":"Mohammad","year":"2010"},{"key":"2024061809545224800_fqae015-B22","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1111\/j.1467-8640.2012.00460.x","article-title":"\u2018Crowdsourcing a Word-Emotion Association Lexicon\u2019","volume":"29","author":"Mohammad","year":"2013","journal-title":"Computational Intelligence"},{"key":"2024061809545224800_fqae015-B23","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1177\/0267323110363653","article-title":"\u2018The Value of Emotion: An Examination of Television Journalists\u2019 Notions on Emotionality\u2019","volume":"25","author":"Pantti","year":"2010","journal-title":"European Journal of Communication"},{"key":"2024061809545224800_fqae015-B24","volume-title":"Linguistic Inquiry and Word Count","author":"Pennebaker","year":"2001"},{"key":"2024061809545224800_fqae015-B25","volume-title":"The Psychology and Biology of Emotion","author":"Plutchik","year":"1994"},{"key":"2024061809545224800_fqae015-B26","doi-asserted-by":"crossref","first-page":"2022\u2019","DOI":"10.1177\/17506352231166327","article-title":"\u2018War on Frames: Text Mining of Conflict in Russian and Ukrainian News Agency Coverage on Telegram during the Russian Invasion of Ukraine in","volume":"17","author":"Ptaszek","year":"2024","journal-title":"Media, War & Conflict,"},{"key":"2024061809545224800_fqae015-B27","first-page":"1","author":"Ranganathan","year":"2016"},{"key":"2024061809545224800_fqae015-B28","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1177\/146488490100200201","article-title":"\u2018The Objectivity Norm in American Journalism\u2019","volume":"2","author":"Schudson","year":"2001","journal-title":"Journalism"},{"key":"2024061809545224800_fqae015-B29","first-page":"1083","author":"Strapparava","year":"2004"},{"key":"2024061809545224800_fqae015-B30","volume-title":"News as Discourse","author":"Van Dijk","year":"1988"},{"key":"2024061809545224800_fqae015-B31","volume-title":"News Analysis","author":"Van Dijk","year":"1988"},{"key":"2024061809545224800_fqae015-B32","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1177\/0957926593004002006","article-title":"\u2018Principles of Critical Discourse Analysis\u2019","volume":"4","author":"Van Dijk","year":"1993","journal-title":"Discourse & Society"},{"key":"2024061809545224800_fqae015-B33","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/13569310600687908","article-title":"\u2018Ideology and Discourse Analysis\u2019","volume":"11","author":"Van Dijk","year":"2006","journal-title":"Journal of Political Ideologies"},{"issue":"2","key":"2024061809545224800_fqae015-B34","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3390\/a16020069","article-title":"\u2018RUemo-The Classification Framework for Russia-Ukraine War-Related Societal Emotions on Twitter through Machine Learning\u2019","volume":"16","author":"Vyas","year":"2023","journal-title":"Algorithms"},{"key":"2024061809545224800_fqae015-B35","doi-asserted-by":"crossref","DOI":"10.1007\/s42979-023-01790-5","article-title":"\u2018Sentiment Analysis and Comprehensive Evaluation of Supervised Machine Learning Models using Twitter Data on Russia\u2013Ukraine War\u2019","volume":"4","author":"Wadhwani","year":"2023","journal-title":"SN Computer Science"},{"key":"2024061809545224800_fqae015-B36","first-page":"128","author":"Wahl-Jorgensen","year":"2016"},{"key":"2024061809545224800_fqae015-B37","volume-title":"Emotions, Media and Politics","author":"Wahl-Jorgensen","year":"2019"},{"key":"2024061809545224800_fqae015-B38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/2056305119852175","article-title":"\u2018Questioning the Ideal of the Public Sphere: The Emotional Turn\u2019","volume":"5","author":"Wahl-Jorgensen","year":"2019","journal-title":"Social Media + Society"},{"key":"2024061809545224800_fqae015-B39","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1177\/1357034X14539020","article-title":"\u2018Trends in the Turn to Affect: A Social Psychological Critique\u2019","volume":"21","author":"Wetherell","year":"2015","journal-title":"Body & Society"},{"key":"2024061809545224800_fqae015-B40","doi-asserted-by":"crossref","first-page":"586","DOI":"10.22363\/2687-0088-2021-25-3-586-610","article-title":"\u2018Emotionalisation of Contemporary Media Discourse: A Research Agenda\u2019","volume":"25","author":"Zappettini","year":"2021","journal-title":"Russian Journal of Linguistics"}],"container-title":["Digital Scholarship in the Humanities"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/dsh\/article-pdf\/39\/2\/609\/58267490\/fqae015.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/dsh\/article-pdf\/39\/2\/609\/58267490\/fqae015.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T10:57:04Z","timestamp":1718708224000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/dsh\/article\/39\/2\/609\/7638816"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,1]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4,1]]},"published-print":{"date-parts":[[2024,6,1]]}},"URL":"https:\/\/doi.org\/10.1093\/llc\/fqae015","relation":{},"ISSN":["2055-7671","2055-768X"],"issn-type":[{"value":"2055-7671","type":"print"},{"value":"2055-768X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,6]]},"published":{"date-parts":[[2024,4,1]]}}}