{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T14:58:50Z","timestamp":1773413930696,"version":"3.50.1"},"reference-count":64,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:00:00Z","timestamp":1623974400000},"content-version":"vor","delay-in-days":168,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Social networks are real\u2010time platforms formed by users involving conversations and interactions. This phenomenon of the new information era results in a very huge amount of data in different forms and modalities such as text, images, videos, and voice. The data with such characteristics are also known as big data with 5\u2010V properties and in some cases are also referred to as social big data. To find useful information from such valuable data, many researchers tried to address different aspects of it for different modalities. In the case of text, NLP researchers conducted many research studies and scientific works to extract valuable information such as topics. Many enlightening works on different platforms of social media, like Twitter, tried to address the problem of finding important topics from different aspects and utilized it to propose solutions for diverse use cases. The importance of Twitter in this scope lies in its content and the behavior of its users. For example, it is also known as first\u2010hand news reporting social media which has been a news reporting and informing platform even for political influencers or catastrophic news reporting. In this review article, we cover more than 50 research articles in the scope of topic detection from Twitter. We also address deep learning\u2010based methods.<\/jats:p>","DOI":"10.1155\/2021\/8833084","type":"journal-article","created":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T19:35:06Z","timestamp":1624044906000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Topic Detection and Tracking Techniques on Twitter: A Systematic Review"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7892-9675","authenticated-orcid":false,"given":"Meysam","family":"Asgari-Chenaghlu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8548-976X","authenticated-orcid":false,"given":"Mohammad-Reza","family":"Feizi-Derakhshi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2369-9833","authenticated-orcid":false,"given":"Leili","family":"Farzinvash","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5898-0871","authenticated-orcid":false,"given":"Mohammad-Ali","family":"Balafar","sequence":"additional","affiliation":[]},{"given":"Cina","family":"Motamed","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-0933-2"},{"key":"e_1_2_9_2_2","volume-title":"Topic Detection and Tracking: Event-Based Information Organization","author":"Allan J.","year":"2012"},{"key":"e_1_2_9_3_2","unstructured":"AllanJ. CarbonellJ. G. GeorgeD. YamronJ. andYangY. Topic detection and tracking pilot study final report Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop February 1998 Lansdowne VA USA 194\u2013218."},{"key":"e_1_2_9_4_2","unstructured":"Twitter Usage Statistics 2017 InternetLiveStats.com."},{"key":"e_1_2_9_5_2","unstructured":"Twitter Tweets Per Day Statistics 2013 https:\/\/blog.twitter.com\/2013\/new-tweets-per-second-record-andhow."},{"key":"e_1_2_9_6_2","volume-title":"Big Data: The Next Frontier for Innovation, Competition, and Productivity","author":"James M.","year":"2011"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2015.08.005"},{"key":"e_1_2_9_8_2","unstructured":"Bia\u0142eckiA. MuirR. andGrantI. Apache lucene 4 Proceedings of the SIGIR 2012 Workshop on Open Source Information Retrieval August 2012 Portland OR USA."},{"key":"e_1_2_9_9_2","unstructured":"UIMA Apache Apache Software Foundation 2011 https:\/\/java.apache.org."},{"key":"e_1_2_9_10_2","unstructured":"Apache Apache Storm 2013."},{"key":"e_1_2_9_11_2","unstructured":"MongoDB Mongodb 2013."},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"SankaranarayananJ. SametH. TeitlerB. E. LiebermanM. D. andSperlingJ. Twitterstand: news in tweets Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems January 2009 Seattle WA USA ACM 42\u201351 https:\/\/doi.org\/10.1145\/1653771.1653781 2-s2.0-74049120785.","DOI":"10.1145\/1653771.1653781"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"PhuvipadawatS.andMurataT. Breaking news detection and tracking in twitter Proceedings of the 2010 IEEE\/WIC\/ACM International Conference onWeb Intelligence and Intelligent Agent Technology (WI-IAT) August 2010 Toronto Canada IEEE 120\u2013123 https:\/\/doi.org\/10.1109\/WI-IAT.2010.205 2-s2.0-78649830319.","DOI":"10.1109\/WI-IAT.2010.205"},{"key":"e_1_2_9_14_2","unstructured":"Petrovi\u0107S. OsborneM. andLavrenkoV. Streaming first story detection with application to twitter Proceedings of the Human Language Technologies: the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics June 2010 Los Angeles CA USA Association for Computational Linguistics 181\u2013189."},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"TembhurnikarS. D.andPatilN. N. Topic detection using bngram method and sentiment analysis on twitter dataset Proceedings of the 2015 4th International Conference on Reliability Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) September 2015 Noida India 1\u20136 https:\/\/doi.org\/10.1109\/ICRITO.2015.7359267 2-s2.0-84961781999.","DOI":"10.1109\/ICRITO.2015.7359267"},{"key":"e_1_2_9_16_2","unstructured":"OsborneM. PetrovicS. McCreadieR. MacdonaldC. andOunisI. Bieber no more: first story detection using twitter and wikipedia Proceedings of the SIGIR 2012 Workshop on Time-Aware Information Access August 2012 Portland OR USA."},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.03.011"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.02.028"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"PetkosG. PapadopoulosS. AielloL. RyanS. andKompatsiarisY. A soft frequent pattern mining approach for textual topic detection Proceedings of the 4th International Conference on Web Intelligence Mining and Semantics (WIMS14) June 2014 Thessaloniki Greece ACM https:\/\/doi.org\/10.1145\/2611040.2611068 2-s2.0-84903642880.","DOI":"10.1145\/2611040.2611068"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2016.06.012"},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"GaglioS. Lo ReG. andMoranaM. Real-time detection of twitter social events from the user\u2019s perspective Proceedings of the 2015 IEEE International Conference on Communications (ICC) June 2015 London UK IEEE 1207\u20131212 https:\/\/doi.org\/10.1109\/ICC.2015.7248487 2-s2.0-84953734545.","DOI":"10.1109\/ICC.2015.7248487"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.08.062"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"BeckerH. NaamanM. andGravanoL. Beyond trending topics: real-world event identification on twitter 11 Proceedings of the Fifth International Conference on Weblogs and Social Media July 2011 Barcelona Spain 438\u2013441.","DOI":"10.1609\/icwsm.v5i1.14146"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"TangD. WeiF. YangN. ZhouM. LiuT. andQinB. Learning sentiment-specific word embedding for twitter sentiment classification Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics June 2014 Baltimore MD USA 1555\u20131565.","DOI":"10.3115\/v1\/P14-1146"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-016-0263-0"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.14778\/2536274.2536307"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"LeeR.andSumiyaK. Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks November 2010 San Jose CA USA ACM 1\u201310 https:\/\/doi.org\/10.1145\/1867699.1867701 2-s2.0-78650855097.","DOI":"10.1145\/1867699.1867701"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"LiR. LeiK. H. KhadiwalaR. andChangK. C. C. Tedas: a twitter-based event detection and analysis system Proceedings of the 2012 IEEE 28th International Conference on Data Engineering (ICDE) April 2012 Arlington VA USA IEEE 1273\u20131276 https:\/\/doi.org\/10.1109\/ICDE.2012.125 2-s2.0-84864193218.","DOI":"10.1109\/ICDE.2012.125"},{"key":"e_1_2_9_29_2","doi-asserted-by":"crossref","unstructured":"SakakiT. OkazakiM. andMatsuoY. Earthquake shakes twitter users: real-time event detection by social sensors Proceedings of the 19th International Conference on World Wide Web April 2010 Raleigh NC USA ACM 851\u2013860 https:\/\/doi.org\/10.1145\/1772690.1772777 2-s2.0-77954571408.","DOI":"10.1145\/1772690.1772777"},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"SnowsillT. NicartF. StefaniM. De BieT. andCristianiniN. Finding surprising patterns in textual data streams Proceedings of the 2010 2nd International Workshop on Cognitive Information Processing (CIP) June 2010 Elba Italy IEEE 405\u2013410 https:\/\/doi.org\/10.1109\/CIP.2010.5604085 2-s2.0-78349249087.","DOI":"10.1109\/CIP.2010.5604085"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"SaeedZ. AbbasiR. A. SadafA. RazzakM. I. andXuG. Text Stream to temporal network\u2014a dynamic heartbeat graph to detect emerging events on twitter Proceedings of the Advances in Knowledge Discovery and Data Mining in Pacific-Asia Conference on Knowledge Discovery and Data Mining June 2018 Melbourne Australia Springer 534\u2013545 https:\/\/doi.org\/10.1007\/978-3-319-93037-4_42 2-s2.0-85049376201.","DOI":"10.1007\/978-3-319-93037-4_42"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.06.005"},{"key":"e_1_2_9_33_2","unstructured":"Asgari-ChenaghluM. Feizi-DerakhshiM. R. FarzinvashL. BalafarM. A. andMotamedC. Topicbert: a transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection 2020 https:\/\/arxiv.org\/abs\/2008.06877."},{"key":"e_1_2_9_34_2","unstructured":"Asgari-ChenaghluM. Nikzad-KhasmakhiN. andMinaeeS. Covid-transformer: detecting trending topics on twitter using universal sentence encoder 2020 https:\/\/arxiv.org\/abs\/2009.03947."},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113990"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.107057"},{"key":"e_1_2_9_37_2","unstructured":"MurfiH. RosalineN. andHariadiN. Deep autoencoder-based fuzzy c-means for topic detection 2021 https:\/\/arxiv.org\/abs\/2102.02636."},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139924801"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"e_1_2_9_40_2","unstructured":"NoSlang.com 2017."},{"key":"e_1_2_9_41_2","doi-asserted-by":"crossref","unstructured":"PhuvipadawatS.andMurataT. Breaking news detection and tracking in twitter Proceedings of the 2010 IEEE\/WIC\/ACM International Conference on Web Intelligence and Intelligent Agent Technology\u2014Volume 3 WI-IAT \u201910 August 2010 Washington DC USA IEEE Computer Society 120\u2013123.","DOI":"10.1109\/WI-IAT.2010.205"},{"key":"e_1_2_9_42_2","doi-asserted-by":"crossref","unstructured":"KumaranG.andAllanJ. Text classification and named entities for new event detection Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval July 2004 Sheffield UK ACM 297\u2013304 https:\/\/doi.org\/10.1145\/1008992.1009044.","DOI":"10.1145\/1008992.1009044"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656278"},{"key":"e_1_2_9_44_2","doi-asserted-by":"crossref","unstructured":"Amig\u00f3E. De AlbornozJ. C. ChugurI.et al. Overview of replab 2013: evaluating online reputation monitoring systems Proceedings of the Lecture Notes in Computer Science in International Conference of the Cross-Language Evaluation Forum for European Languages September 2013 Valencia Spain Springer 333\u2013352 https:\/\/doi.org\/10.1007\/978-3-642-40802-1_31 2-s2.0-84886444283.","DOI":"10.1007\/978-3-642-40802-1_31"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10472-007-9053-6"},{"key":"e_1_2_9_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07821-2"},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/360402.360421"},{"key":"e_1_2_9_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.10.016"},{"key":"e_1_2_9_49_2","doi-asserted-by":"crossref","unstructured":"QiH. ChangK. andLimE. P. Analyzing feature trajectories for event detection Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval July 2007 Amsterdam The Netherlands ACM 207\u2013214 https:\/\/doi.org\/10.1145\/1277741.1277779 2-s2.0-36448936021.","DOI":"10.1145\/1277741.1277779"},{"key":"e_1_2_9_50_2","doi-asserted-by":"crossref","unstructured":"WengJ.andLeeB. S. Event detection in twitter 11 Proceedings of the Fifth International Conference on Weblogs and Social Media July 2011 Barcelona Spain 401\u2013408.","DOI":"10.1609\/icwsm.v5i1.14102"},{"key":"e_1_2_9_51_2","unstructured":"CordeiroM. Twitter event detection: combining wavelet analysis and topic inference summarization Proceedings of the Doctoral Symposium on Informatics Engineering January 2012 Porto Portugal."},{"key":"e_1_2_9_52_2","unstructured":"Asgari-ChenaghluM. Feizi-DerakhshiM. R. FarzinvashL. BalafarM. A. andMotamedC. A multimodal deep learning approach for named entity recognition from social media 2020 https:\/\/arxiv.org\/abs\/2001.06888."},{"key":"e_1_2_9_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2012.04.010"},{"key":"e_1_2_9_54_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_55_2","doi-asserted-by":"crossref","unstructured":"CollobertR.andWestonJ. A unified architecture for natural language processing: deep neural networks with multitask learning Proceedings of the 25th International Conference on Machine Learning June 2008 Helsinki Finland ACM 160\u2013167 https:\/\/doi.org\/10.1145\/1390156.1390177.","DOI":"10.1145\/1390156.1390177"},{"key":"e_1_2_9_56_2","doi-asserted-by":"crossref","unstructured":"PenningtonJ. SocherR. andManningC. D. GloVe: global vectors for word representation Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) October 2014 Doha Qatar https:\/\/doi.org\/10.3115\/v1\/D14-1162.","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_2_9_57_2","unstructured":"MikolovT. ChenK. CorradoG. andDeanJ. Efficient estimation of word representations in vector space 2013 https:\/\/arxiv.org\/abs\/1301.3781."},{"key":"e_1_2_9_58_2","unstructured":"Dos SantosC. N.andGattiM. Deep convolutional neural networks for sentiment analysis of short texts Proceedings of COLING 2014 the 25th International Conference on Computational Linguistics: Technical Papers August 2014 Dublin Ireland 69\u201378."},{"key":"e_1_2_9_59_2","first-page":"2493","article-title":"Natural language processing (almost) from scratch","volume":"12","author":"Collobert R.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_9_60_2","doi-asserted-by":"crossref","unstructured":"MathioudakisM.andKoudasN. Twittermonitor: trend detection over the twitter stream Proceedings of the 2010 ACM SIGMOD International Conference on Management of data June 2010 Indianapolis IN USA ACM 1155\u20131158 https:\/\/doi.org\/10.1145\/1807167.1807306 2-s2.0-77954707603.","DOI":"10.1145\/1807167.1807306"},{"key":"e_1_2_9_61_2","doi-asserted-by":"crossref","unstructured":"PopescuA. M.andPennacchiottiM. Detecting controversial events from twitter Proceedings of the 19th ACM International Conference on Information and knowledge Management October 2010 Toronto Canada ACM 1873\u20131876 https:\/\/doi.org\/10.1145\/1871437.1871751 2-s2.0-78651265047.","DOI":"10.1145\/1871437.1871751"},{"key":"e_1_2_9_62_2","doi-asserted-by":"crossref","unstructured":"PopescuA. M. PennacchiottiM. andParanjpeD. Extracting events and event descriptions from twitter Proceedings of the 20th International Conference Companion on World Wide Web March 2011 Hyderabad India ACM 105\u2013106 https:\/\/doi.org\/10.1145\/1963192.1963246 2-s2.0-79955407927.","DOI":"10.1145\/1963192.1963246"},{"key":"e_1_2_9_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/mc.2016.183"},{"key":"e_1_2_9_64_2","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxw056"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/8833084.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/8833084.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/8833084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T23:04:42Z","timestamp":1723244682000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/8833084"}},"subtitle":[],"editor":[{"given":"Fei","family":"Xiong","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/8833084"],"URL":"https:\/\/doi.org\/10.1155\/2021\/8833084","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-02-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-06-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8833084"}}