{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:42:10Z","timestamp":1760575330993,"version":"build-2065373602"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000181","name":"AFOSR","doi-asserted-by":"crossref","award":["FA9550-22-1-0380 & FA9550-20-1-0224"],"award-info":[{"award-number":["FA9550-22-1-0380 & FA9550-20-1-0224"]}],"id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000185","name":"DARPA","doi-asserted-by":"crossref","award":["HR001121C0168"],"award-info":[{"award-number":["HR001121C0168"]}],"id":[{"id":"10.13039\/100000185","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>The rich and dynamic information environment of social media provides researchers, policymakers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is difficult due to the heterogeneity of topics and events discussed in the highly dynamic online information environment. To address these challenges, we present a method for systematically detecting and measuring emotional reactions to offline events using change point detection on the time series of collective affect and further explaining these reactions using a transformer-based topic model. We demonstrate the utility of the method by successfully detecting major and smaller events on three different datasets, including (1) a Los Angeles Tweet dataset between Jan. and Aug. 2020, in which we revealed the complex psychological impact of the BlackLivesMatter movement and the COVID-19 pandemic, (2) a dataset related to abortion rights discussions in the USA, in which we uncovered the strong emotional reactions to the overturn of Roe v. Wade and state abortion bans, and (3) a dataset about the 2022 French presidential election, in which we discovered the emotional and moral shift from positive before voting to fear and criticism after voting. We further demonstrate the importance of disaggregating data by topics and populations to mitigate potential biases when studying collective emotions. The capability of our method allows for better sensing and monitoring of the population\u2019s reactions during crises using online data.<\/jats:p>","DOI":"10.1145\/3708513","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T06:24:15Z","timestamp":1734416655000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6422-3420","authenticated-orcid":false,"given":"Siyi","family":"Guo","sequence":"first","affiliation":[{"name":"Information Sciences Institute","place":["Marina Del Rey, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9052-6310","authenticated-orcid":false,"given":"Zihao","family":"He","sequence":"additional","affiliation":[{"name":"Information Sciences Institute","place":["Marina Del Rey, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8751-3076","authenticated-orcid":false,"given":"Ashwin","family":"Rao","sequence":"additional","affiliation":[{"name":"Information Sciences Institute","place":["Marina Del Rey, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0247-4328","authenticated-orcid":false,"given":"Fred","family":"Morstatter","sequence":"additional","affiliation":[{"name":"Information Sciences Institute","place":["Marina Del Rey, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1079-0053","authenticated-orcid":false,"given":"Jeffrey","family":"Brantingham","sequence":"additional","affiliation":[{"name":"University of California Los Angeles","place":["Los Angeles, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5071-0575","authenticated-orcid":false,"given":"Kristina","family":"Lerman","sequence":"additional","affiliation":[{"name":"Information Sciences Institute","place":["Marina Del Rey, United States"]}]}],"member":"320","published-online":{"date-parts":[[2025,10,15]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Ryan Prescott Adams and David J. C. MacKay. 2007. Bayesian online changepoint detection. arXiv:0710.3742. Retrieved from https:\/\/arxiv.org\/abs\/0710.3742"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3516491"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","unstructured":"Hassan Alhuzali and Sophia Ananiadou. 2021. SpanEmo: Casting multi-label emotion classification as span-prediction. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume Paola Merlo Jorg Tiedemann and Reut Tsarfaty (Eds.). Association for Computational Linguistics Online 1573\u20131584. 10.18653\/v1\/2021.eacl-main.135","DOI":"10.18653\/v1\/2021.eacl-main.135"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.02.016"},{"key":"e_1_3_2_6_2","article-title":"Emotion analysis of user reactions to online news","author":"Babac Marina Bagi\u0107","year":"2022","unstructured":"Marina Bagi\u0107 Babac. 2022. Emotion analysis of user reactions to online news. Information Discovery and Delivery 51, 2 (2022), 179\u2013193.","journal-title":"Information Discovery and Delivery"},{"key":"e_1_3_2_7_2","first-page":"258","volume-title":"Proceedings of the LREC","author":"Barbieri Francesco","year":"2022","unstructured":"Francesco Barbieri, Luis Espinosa Anke, and Jose Camacho-Collados. 2022. XLM-T: Multilingual language models in twitter for sentiment analysis and beyond. In Proceedings of the LREC. 258\u2013266."},{"key":"e_1_3_2_8_2","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","author":"Blei David M.","year":"2003","unstructured":"David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3, Jan (2003), 993\u20131022.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2010.12.007"},{"key":"e_1_3_2_10_2","unstructured":"Gerlof Bouma. 2009. Normalized (pointwise) mutual information in collocation extraction. In Proceedings of GSCL 30 (2009) 31\u201340."},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Yuwei Cao Hao Peng JiaWu Yingtong Dou Jianxin Li and Philip S. Yu. 2021. Knowledge-preserving incremental social event detection via heterogeneous gnns. In Proceedings of the Web Conference 2021. 3383\u20133395.","DOI":"10.1145\/3442381.3449834"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v17i1.22207"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v18i1.31434"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0136092"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0064679"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Adji B. Dieng Francisco J. R. Ruiz and David M. Blei. 2020. Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics 8 (2020) 439\u2013453.","DOI":"10.1162\/tacl_a_00325"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Peter Dodds Joshua Minot Michael Arnold Thayer Alshaabi Jane Adams David Dewhurst Andrew Reagan and Christopher Danforth. 2022. Fame and ultrafame: Measuring and comparing daily levels of \u2018being talked about\u2019 for United States\u2019 presidents their rivals god countries and k-pop.Journal of Quantitative Description: Digital Media 2 (2022). DOI:10.51685\/jqd.2022.004","DOI":"10.51685\/jqd.2022.004"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0026752"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.3758\/s13428-017-0875-9"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjds\/s13688-023-00414-5"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1202775"},{"key":"e_1_3_2_22_2","unstructured":"Maarten Grootendorst. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv:2203.05794. Retrieved from https:\/\/arxiv.org\/abs\/2203.05794"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"Siyi Guo Negar Mokhberian and Kristina Lerman. 2023. A data fusion framework for multi-domain morality learning. In Proceedings of the International AAAI Conference on Web and Social Media Vol. 17. 281\u2013291.","DOI":"10.1609\/icwsm.v17i1.22145"},{"key":"e_1_3_2_24_2","first-page":"367","article-title":"The moral mind: How five sets of innate intuitions guide the development of many culture-specific virtues, and perhaps even modules","volume":"3","author":"Haidt Jonathan","year":"2007","unstructured":"Jonathan Haidt, Craig Joseph, et\u00a0al. 2007. The moral mind: How five sets of innate intuitions guide the development of many culture-specific virtues, and perhaps even modules. The Innate Mind 3 (2007), 367\u2013391.","journal-title":"The Innate Mind"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8030113"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.wassa-1.7"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1093\/pnasnexus\/pgad382"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-018-0525-y"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/58.3.509"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1177\/1948550619876629"},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Kokil Jaidka Salvatore Giorgi H. Andrew Schwartz Margaret L. Kern Lyle H. Ungar and Johannes C. Eichstaedt. 2020. Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods. In Proceedings of the National Academy of Sciences 117 19 (2020) 10165\u201310171.","DOI":"10.1073\/pnas.1906364117"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbe2.202"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","unstructured":"Marko Kla\u0161nja Pablo Barber\u00e1 Nicholas Beauchamp Jonathan Nagler and Joshua A. Tucker. 2018. Measuring Public Opinion with Social Media Data. Oxford University Press 555\u2013582. 10.1093\/oxfordhb\/9780190213299.013.3","DOI":"10.1093\/oxfordhb\/9780190213299.013.3"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42001-017-0007-4"},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Jure Leskovec Lars Backstrom and Jon Kleinberg. 2009. Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.497\u2013506.","DOI":"10.1145\/1557019.1557077"},{"key":"e_1_3_2_36_2","doi-asserted-by":"crossref","unstructured":"Chenliang Li Aixin Sun and Anwitaman Datta. 2012. Twevent: Segment-based event detection from tweets. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management.155\u2013164.","DOI":"10.1145\/2396761.2396785"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5980043"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.645"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505695"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.24440"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0064417"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/S18-1001"},{"key":"e_1_3_2_43_2","first-page":"26","volume-title":"Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text","author":"Mohammad Saif","year":"2010","unstructured":"Saif Mohammad and Peter Turney. 2010. Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. Association for Computational Linguistics, Los Angeles, CA, 26\u201334. Retrieved from https:\/\/aclanthology.org\/W10-0204"},{"key":"e_1_3_2_44_2","unstructured":"Keval Morabia Neti Lalita Bhanu Murthy Aruna Malapati and Surender Samant. 2019. SEDTWik: Segmentation-based event detection from tweets using Wikipedia. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop. 77\u201385."},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/IALP.2015.7451564","volume-title":"Proceedings of the 2015 International Conference on Asian Language Processing (IALP\u201915)","author":"Niu Liqiang","year":"2015","unstructured":"Liqiang Niu, Xinyu Dai, Jianbing Zhang, and Jiajun Chen. 2015. Topic2Vec: Learning distributed representations of topics. In Proceedings of the 2015 International Conference on Asian Language Processing (IALP\u201915). IEEE, 193\u2013196."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-14579-y"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acap.2013.08.002"},{"key":"e_1_3_2_48_2","unstructured":"Alexandru Petrescu Ciprian-Octavian Truic\u0103 Elena-Simona Apostol and Adrian Paschke. 2023. EDSA-ensemble: An event detection sentiment analysis ensemble architecture. arXiv:2301.12805. https:\/\/api.semanticscholar.org\/CorpusID:256389541"},{"key":"e_1_3_2_49_2","unstructured":"Ashwin Rao Rong-Ching Chang Qiankun Zhong Kristina Lerman and Magdalena Wojcieszak. 2023. Tracking a year of polarized twitter discourse on abortion. arXiv:2311.16831. Retrieved from https:\/\/arxiv.org\/abs\/2311.16831"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence embeddings using siamese BERT-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) Kentaro Inui Jing Jiang Vincent Ng and Xiaojun Wan (Eds.). Association for Computational Linguistics Hong Kong China 3982\u20133992. 10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_2_51_2","article-title":"Event detection in Twitter by deep learning classification and multi label clustering virtual backbone formation","author":"Rezaei Zahra","year":"2022","unstructured":"Zahra Rezaei, Behnaz Eslami, Mohammad Amin Amini, and Mohammad Eslami. 2022. Event detection in Twitter by deep learning classification and multi label clustering virtual backbone formation. Evolutionary Intelligence 16, 3 (2023), 833\u2013847.","journal-title":"Evolutionary Intelligence"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3382735"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-021-01235-8"},{"key":"e_1_3_2_54_2","unstructured":"Hugh Schofield. 2022. French elections: Putin\u2019s war gives Macron boost in presidential race. Retrieved from https:\/\/www.bbc.com\/news\/world-europe-60793320"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.5210\/fm.v28i2.12777"},{"key":"e_1_3_2_56_2","unstructured":"Jackson Trager Alireza S. Ziabari Aida Mostafazadeh Davani Preni Golazizian Farzan Karimi-Malekabadi Ali Omrani Zhihe Li Brendan Kennedy Nils Karl Reimer Melissa Reyes et\u00a0al. 2022. The moral foundations Reddit corpus. arXiv:2208.05545. Retrieved from https:\/\/arxiv.org\/abs\/2208.05545"},{"key":"e_1_3_2_57_2","doi-asserted-by":"crossref","unstructured":"Andranik Tumasjan Timm Sprenger Philipp Sandner and Isabell Welpe. 2010. Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proceedings of the International AAAI Conference on Web and Social Media Vol. 4. 178\u2013185.","DOI":"10.1609\/icwsm.v4i1.14009"},{"key":"e_1_3_2_58_2","first-page":"896","article-title":"Editorial: The social nature of emotions","volume":"7","year":"2016","unstructured":"Gerben A. Van Kleef, Arik Cheshin, Agneta H. Fischer, and Iris K. Schneider. 2016. Editorial: The social nature of emotions. Frontiers in Psychology 7 (2016), 896.","journal-title":"Frontiers in Psychology"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","unstructured":"Jianshu Weng and Bu-Sung Lee. 2011. Event detection in Twitter. In Proceedings of the International AAAI Conference on Web and Social Media Vol. 5. 401\u2013408.","DOI":"10.1609\/icwsm.v5i1.14102"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03592-3"}],"container-title":["ACM Transactions on the Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708513","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3708513","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T12:27:03Z","timestamp":1760531223000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3708513"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,11,30]]}},"alternative-id":["10.1145\/3708513"],"URL":"https:\/\/doi.org\/10.1145\/3708513","relation":{},"ISSN":["1559-1131","1559-114X"],"issn-type":[{"type":"print","value":"1559-1131"},{"type":"electronic","value":"1559-114X"}],"subject":[],"published":{"date-parts":[[2025,10,15]]},"assertion":[{"value":"2023-12-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-11","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}