{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T04:39:09Z","timestamp":1780375149905,"version":"3.54.1"},"reference-count":63,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information Systems Research"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:p>Which emotions make a post go viral, and which hold it back?\u202fAnalyzing 387,\u202f000 news articles and the sharing paths of more than six\u202fmillion users on\u202fWeChat\u2014China\u2019s super-app for social media\u2014we map how eight discrete emotions drive information diffusion.\u202fEconometric models show that content expressing anxiety, love, or surprise reliably travels farther; reaches more unique people; and forms deeper, broader, more viral cascades, whereas anger, sadness, and even joy dampen propagation.\u202fDiffusion also varies with who shares the content and how strongly sharers are connected, underscoring the importance of audience- and tie-specific strategies.\u202fFor practitioners, framing messages around constructive uncertainty (anxiety), prosocial appreciation (love), or unexpected insight (surprise) can amplify reach, whereas caution is warranted when leveraging anger.\u202fFor policymakers and platforms, monitoring anxiety- and surprise-laden posts enables early intervention, and transparency audits must weigh the unequal amplification power of specific emotions when evaluating recommender algorithms and influence operations.<\/jats:p>","DOI":"10.1287\/isre.2022.0611","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T14:02:00Z","timestamp":1754316120000},"page":"398-415","source":"Crossref","is-referenced-by-count":8,"title":["Emotions in Online Content Diffusion"],"prefix":"10.1287","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8959-3169","authenticated-orcid":false,"given":"Yifan","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0276-1271","authenticated-orcid":false,"given":"Shan","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Business and Economics, The University of Hong Kong, University Dr, Lung Fu Shan, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5043-4411","authenticated-orcid":false,"given":"Yuchen","family":"Liu","sequence":"additional","affiliation":[{"name":"Information Systems & Operations Management Department, Warrington College of Business, University of Florida, Gainesville, Florida 32611"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Information Systems and Operations Management, Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"109","reference":[{"key":"B1","unstructured":"Anderson M (2015) Men catch up with women on overall social media use.\n                      Pew Research Center\n                      (August 28), https:\/\/www.pewresearch.org\/short-reads\/2015\/08\/28\/men-catch-up-with-women-on-overall-social-media-use\/."},{"key":"B2","volume-title":"Econometric Analysis of Panel Data","volume":"4","author":"Baltagi BH","year":"2008"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1177\/0956797611413294"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcps.2014.05.002"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1086\/671345"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.10.0353"},{"key":"B7","first-page":"993","volume":"3","author":"Blei DM","year":"2003","journal-title":"J. Machine Learn. Res."},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1037\/xge0000532"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1618923114"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1023\/A:1024569803230"},{"key":"B11","doi-asserted-by":"crossref","unstructured":"Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W, Robins J (2018) Double\/debiased machine learning for treatment and structural parameters.\n                      Econometrics J.\n                      21(1):C1\u2013C68.","DOI":"10.1111\/ectj.12097"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1016\/0022-1031(91)90029-6"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.copsyc.2021.12.005"},{"key":"B14","doi-asserted-by":"crossref","unstructured":"Dev H, Karahalios K, Sundaram H (2019) Quantifying voter biases in online platforms: An instrumental variable approach. Lampinen A, Gergle D, Shamma DA, eds.\n                      Proc. ACM Human-Comput. Interaction\n                      , vol. 3 (Association for Computing Machinery, New York), 1\u201327.","DOI":"10.1145\/3359222"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1177\/001872675400700202"},{"key":"B16","unstructured":"Finkenauer C (1998) Secrets: Types, determinants, functions, and consequences. Unpublished doctoral dissertation, University of Louvain at Louvain-la-Neuve, Belgium."},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2015.2158"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1257\/aer.20220129"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1111\/1467-8721.ep10770953"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcps.2013.04.001"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1037\/0022-3514.81.6.1028"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1080\/02699931.2018.1476324"},{"key":"B23","doi-asserted-by":"publisher","DOI":"10.1007\/s11747-014-0388-3"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.5465\/amj.2017.1423"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.2307\/20445398"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1080\/026999399379168"},{"issue":"1","key":"B27","first-page":"P13","volume":"63","author":"Kensinger EA","year":"2008","journal-title":"J. Gerontology Ser. B Psych. Sci. Soc. Sci."},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1017\/pan.2024.2"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-2885.2005.tb00329.x"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1111\/j.0956-7976.2004.00679.x"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-psych-010213-115043"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.05.004"},{"key":"B33","volume-title":"Surprise: Embrace the Unpredictable and Engineer the Unexpected","author":"Luna T","year":"2015"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2017.03.053"},{"key":"B35","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. Preprint, submitted January 16, https:\/\/arxiv.org\/abs\/1301.3781."},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2018.3226"},{"key":"B37","doi-asserted-by":"crossref","unstructured":"Pang B, Lee L (2005) Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Knight K, Ng HT, Oflazer K, eds.\n                      Proc. 43rd Annual Meeting Assoc. Comput. Linguistics\n                      (Association for Computational Linguistics, Ann Arbor, MI), 115\u2013124.","DOI":"10.3115\/1219840.1219855"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.1511\/2001.28.344"},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2010.02.002"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1111\/obes.12088"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.1177\/1754073908097189"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2012.12.022"},{"key":"B43","doi-asserted-by":"publisher","DOI":"10.2307\/1907619"},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195130072.003.0005"},{"key":"B45","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2014\/38.1.06"},{"key":"B46","doi-asserted-by":"crossref","unstructured":"Song Y, Shi S, Li J, Zhang H (2018) Directional skip-gram: Explicitly distinguishing left and right context for word embeddings. Walker M, Ji H, Stent A, eds.\n                      Proc. 2018 Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Tech. Vol. 2 (Short Papers)\n                      (Association for Computational Linguistics, New Orleans, LA), 175\u2013180.","DOI":"10.18653\/v1\/N18-2028"},{"key":"B47","doi-asserted-by":"publisher","DOI":"10.2753\/MIS0742-1222290408"},{"key":"B48","doi-asserted-by":"crossref","unstructured":"Stock J, Yogo M (2005)\n                      Testing for Weak Instruments in Linear IV Regression\n                      (Cambridge University Press, New York), 80\u2013108.","DOI":"10.1017\/CBO9780511614491.006"},{"key":"B49","volume-title":"Affect Imagery Consciousness: Volume I: The Positive Affects","author":"Tomkins SS","year":"1962"},{"key":"B50","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.14.0653"},{"key":"B51","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8721.2009.01633.x"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1111\/j.1751-9004.2010.00262.x"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.45.10.1324"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1126\/science.aap9559"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1108\/INTR-04-2020-0181"},{"key":"B56","volume-title":"Econometric Analysis of Cross Section and Panel Data","author":"Wooldridge JM","year":"2002","edition":"2"},{"key":"B57","doi-asserted-by":"publisher","DOI":"10.1111\/jpim.12425"},{"key":"B58","doi-asserted-by":"crossref","unstructured":"Xue B, Fu C, Shaobin Z (2014) A study on sentiment computing and classification of Sina Weibo with Word2vec.\n                      IEEE Internat. Congress Big Data\n                      (IEEE, Piscataway, NJ), 358\u2013363.","DOI":"10.1109\/BigData.Congress.2014.59"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1287\/isre.2017.0727"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2014\/38.2.10"},{"key":"B61","doi-asserted-by":"publisher","DOI":"10.1509\/jmr.13.0379"},{"key":"B62","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2021\/15363"},{"key":"B63","doi-asserted-by":"publisher","DOI":"10.25300\/MISQ\/2022\/16600"}],"container-title":["Information Systems Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubsonline.informs.org\/doi\/pdf\/10.1287\/isre.2022.0611","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:46:54Z","timestamp":1775144814000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubsonline.informs.org\/doi\/10.1287\/isre.2022.0611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1287\/isre.2022.0611"],"URL":"https:\/\/doi.org\/10.1287\/isre.2022.0611","relation":{},"ISSN":["1047-7047","1526-5536"],"issn-type":[{"value":"1047-7047","type":"print"},{"value":"1526-5536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3]]}}}