{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:56:21Z","timestamp":1760576181029,"version":"build-2065373602"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T00:00:00Z","timestamp":1718496000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T00:00:00Z","timestamp":1718496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s13042-024-02247-8","type":"journal-article","created":{"date-parts":[[2024,6,16]],"date-time":"2024-06-16T05:01:44Z","timestamp":1718514104000},"page":"7201-7222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Augmenting web-based tourist support system with microblog analyzed data"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3159-1432","authenticated-orcid":false,"given":"Victor Alex","family":"Silaa","sequence":"first","affiliation":[]},{"given":"Fumito","family":"Masui","sequence":"additional","affiliation":[]},{"given":"Michal","family":"Ptaszynski","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,16]]},"reference":[{"key":"2247_CR1","unstructured":"Fukushima Y, Masui F, Ptaszynski M, Nakajima Y, Watanabe K, Kawaishi R, Nitta T, Sato R (2014) Macroanalysis of microblogs: An empirical study of communication strategies on twitter during disasters and elections. In: 2014 AAAI Spring Symposium Series"},{"key":"2247_CR2","doi-asserted-by":"publisher","unstructured":"Madichetty S, Sridevi M (2019) Detecting informative tweets during disaster using deep neural networks. In: 2019 11th International Conference on Communication Systems & Networks (COMSNETS), pp. 709\u2013713 . https:\/\/doi.org\/10.1109\/COMSNETS.2019.8711095","DOI":"10.1109\/COMSNETS.2019.8711095"},{"issue":"14","key":"2247_CR3","doi-asserted-by":"publisher","first-page":"6340","DOI":"10.3390\/app11146340","volume":"11","author":"M Ptaszynski","year":"2021","unstructured":"Ptaszynski M, Masui F, Fukushima Y, Oikawa Y, Hayakawa H, Miyamori Y, Takahashi K, Kawajiri S (2021) Deep learning for information triage on twitter. Appl Sci 11(14):6340","journal-title":"Appl Sci"},{"key":"2247_CR4","doi-asserted-by":"crossref","unstructured":"Oikawa Y, Ptaszynski M, Masui F (2022) Ai training for thunderstorm training: Better situational awareness for disaster tweets using context and emotions. In: 2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 1\u201310 . IEEE","DOI":"10.1109\/ACIIW57231.2022.10086020"},{"key":"2247_CR5","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772777","volume-title":"Earthquake shakes twitter users: Real-time event detection by social sensors","author":"T Sakaki","year":"2010","unstructured":"Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: Real-time event detection by social sensors. Association for Computing Machinery, New York, NY, USA"},{"key":"2247_CR6","doi-asserted-by":"publisher","unstructured":"Masui F, Ptaszynski M, Kawaishi R, Maeda Y, Goto F, Masui H (2015) In: Matsuo, T., Hashimoto, K., Iwamoto, H. (eds.) A System for Recommendation of Accommodation Facilities Adaptable to User Interest, pp. 107\u2013118. Springer, Berlin, Heidelberg . https:\/\/doi.org\/10.1007\/978-3-662-47227-9_8","DOI":"10.1007\/978-3-662-47227-9_8"},{"key":"2247_CR7","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/s40558-017-0095-2","volume":"17","author":"E Haris","year":"2017","unstructured":"Haris E, Gan KH (2017) Mining graphs from travel blogs: a review in the context of tour planning. Information Technology & Tourism 17:429\u2013453","journal-title":"Information Technology & Tourism"},{"key":"2247_CR8","unstructured":"Ptaszynski M, Pieciukiewicz A, Dyba\u0142a P (2019) Results of the poleval 2019 shared task 6: First dataset and open shared task for automatic cyberbullying detection in polish twitter"},{"issue":"1","key":"2247_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/data9010001","volume":"9","author":"M Ptaszynski","year":"2023","unstructured":"Ptaszynski M, Pieciukiewicz A, Dybala P, Skrzek P, Soliwoda K, Fortuna M, Leliwa G, Wroczynski M (2023) Expert-annotated dataset to study cyberbullying in polish language. Data 9(1):1","journal-title":"Data"},{"issue":"8","key":"2247_CR10","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.1257\/aer.104.8.2421","volume":"104","author":"D Mayzlin","year":"2014","unstructured":"Mayzlin D, Dover Y, Chevalier J (2014) Promotional reviews: An empirical investigation of online review manipulation. American Economic Review 104(8):2421\u20132455","journal-title":"American Economic Review"},{"key":"2247_CR11","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.jbusres.2018.02.016","volume":"87","author":"M Zhuang","year":"2018","unstructured":"Zhuang M, Cui G, Peng L (2018) Manufactured opinions: The effect of manipulating online product reviews. J Bus Res 87:24\u201335","journal-title":"J Bus Res"},{"issue":"17","key":"2247_CR12","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.1080\/13683500.2019.1626814","volume":"23","author":"Y Xu","year":"2020","unstructured":"Xu Y, Zhang Z, Law R, Zhang Z (2020) Effects of online reviews and managerial responses from a review manipulation perspective. Curr Issue Tour 23(17):2207\u20132222","journal-title":"Curr Issue Tour"},{"key":"2247_CR13","unstructured":"Versteeg M (2020) Online reviews on tripadvisor, how credible are they?: Experimental approach: manipulated by writing experience of the reviewer, review length and argument quality. Master\u2019s thesis, University of Twente"},{"key":"2247_CR14","unstructured":"Antonakakis M, Perdisci R, Nadji Y, Vasiloglou N, Abu-Nimeh S, Lee W, Dagon D (2012) From $$\\{$$Throw-Away$$\\}$$ traffic to bots: Detecting the rise of $$\\{$$DGA-Based$$\\}$$ malware. In: 21st USENIX Security Symposium (USENIX Security 12), pp. 491\u2013506"},{"key":"2247_CR15","doi-asserted-by":"crossref","unstructured":"Morstatter F, Wu L, Nazer TH, Carley KM, Liu H (2016) A new approach to bot detection: striking the balance between precision and recall. In: 2016 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 533\u2013540 . IEEE","DOI":"10.1109\/ASONAM.2016.7752287"},{"issue":"11","key":"2247_CR16","doi-asserted-by":"publisher","first-page":"2707","DOI":"10.1109\/TIFS.2018.2825958","volume":"13","author":"M Fazil","year":"2018","unstructured":"Fazil M, Abulaish M (2018) A hybrid approach for detecting automated spammers in twitter. IEEE Trans Inf Forensics Secur 13(11):2707\u20132719","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"2247_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113383","volume":"151","author":"M Latah","year":"2020","unstructured":"Latah M (2020) Detection of malicious social bots: A survey and a refined taxonomy. Expert Syst Appl 151:113383","journal-title":"Expert Syst Appl"},{"key":"2247_CR18","doi-asserted-by":"crossref","unstructured":"Silaa V, Masui F, Ptaszynski M (2022) A method of supplementing reviews to less-known tourist spots using geotagged tweets. Applied Sciences 12(5)","DOI":"10.3390\/app12052321"},{"issue":"3","key":"2247_CR19","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1177\/194008291000300301","volume":"3","author":"JR Kideghesho","year":"2010","unstructured":"Kideghesho JR (2010) \u2018serengeti shall not die\u2019: transforming an ambition into a reality. Tropical Conservation Science 3(3):228\u2013247","journal-title":"Tropical Conservation Science"},{"issue":"5","key":"2247_CR20","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1111\/cobi.12116","volume":"27","author":"R Fyumagwa","year":"2013","unstructured":"Fyumagwa R, Gereta E, Hassan S, Kideghesho J, Kohi EM, Keyyu J, Magige F, Mfunda I, Mwakatobe A, Ntalwila J et al (2013) Roads as a threat to the serengeti ecosystem. Conserv Biol 27(5):1122\u20131125","journal-title":"Conserv Biol"},{"issue":"4","key":"2247_CR21","first-page":"73","volume":"290","author":"PF Eagles","year":"2006","unstructured":"Eagles PF, Wade D et al (2006) Tourism in tanzania: Serengeti national park. Bois et for\u00eats des tropiques 290(4):73\u201380","journal-title":"Bois et for\u00eats des tropiques"},{"key":"2247_CR22","unstructured":"Mhilu J, Lyimo B (2019) Social media marketing on attracting tourists: a case of tanzania national parks-arusha. olva academy\u2013school of researchers, vol. 2, issue 3. Olva Academy\u2013School of Researchers 2(3), 2"},{"key":"2247_CR23","doi-asserted-by":"crossref","unstructured":"Oku K, Hattori F (2015) Mapping geotagged tweets to tourist spots considering activity region of spot. Tourism Informatics: Towards Novel Knowledge Based Approaches, 15\u201330","DOI":"10.1007\/978-3-662-47227-9_2"},{"key":"2247_CR24","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s40558-021-00196-4","volume":"23","author":"M Paolanti","year":"2021","unstructured":"Paolanti M, Mancini A, Frontoni E, Felicetti A, Marinelli L, Marcheggiani E, Pierdicca R (2021) Tourism destination management using sentiment analysis and geo-location information: a deep learning approach. Information Technology & Tourism 23:241\u2013264","journal-title":"Information Technology & Tourism"},{"key":"2247_CR25","doi-asserted-by":"crossref","unstructured":"Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 759\u2013768","DOI":"10.1145\/1871437.1871535"},{"key":"2247_CR26","doi-asserted-by":"crossref","unstructured":"Gillioz A, Casas J, Mugellini E, Abou\u00a0Khaled O (2020) Overview of the transformer-based models for nlp tasks. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 179\u2013183 . IEEE","DOI":"10.15439\/2020F20"},{"issue":"1","key":"2247_CR27","doi-asserted-by":"publisher","first-page":"12343","DOI":"10.1111\/conl.12343","volume":"11","author":"A Hausmann","year":"2018","unstructured":"Hausmann A, Toivonen T, Slotow R, Tenkanen H, Moilanen A, Heikinheimo V, Di Minin E (2018) Social media data can be used to understand tourists\u2019 preferences for nature-based experiences in protected areas. Conserv Lett 11(1):12343","journal-title":"Conserv Lett"},{"key":"2247_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jort.2021.100383","volume":"35","author":"C Marcotte","year":"2021","unstructured":"Marcotte C, Stokowski PA (2021) Place meanings and national parks: A rhetorical analysis of social media texts. J Outdoor Recreat Tour 35:100383","journal-title":"J Outdoor Recreat Tour"},{"key":"2247_CR29","doi-asserted-by":"publisher","unstructured":"Binabdullah K, Tongtep N (2021) Comparative study on natural language processing for tourism suggestion system. In: 2021 36th International Technical Conference on Circuits\/Systems, Computers and Communications (ITC-CSCC), pp. 1\u20134 . https:\/\/doi.org\/10.1109\/ITC-CSCC52171.2021.9501422","DOI":"10.1109\/ITC-CSCC52171.2021.9501422"},{"key":"2247_CR30","doi-asserted-by":"crossref","unstructured":"Xu F, Weber J, Buhalis D (2013) Gamification in tourism. In: Information and Communication Technologies in Tourism 2014: Proceedings of the International Conference in Dublin, Ireland, January 21-24, 2014, pp. 525\u2013537 . Springer","DOI":"10.1007\/978-3-319-03973-2_38"},{"key":"2247_CR31","doi-asserted-by":"crossref","unstructured":"Gaia G, Boiano S, Borda A (2019) Engaging museum visitors with ai: The case of chatbots. Museums and Digital Culture: New Perspectives and Research, 309\u2013329","DOI":"10.1007\/978-3-319-97457-6_15"},{"key":"2247_CR32","doi-asserted-by":"crossref","unstructured":"Hettmann W, W\u00f6lfel M, Butz M, Torner K, Finken J (2023) Engaging museum visitors with ai-generated narration and gameplay. In: International Conference on ArtsIT, Interactivity and Game Creation, pp. 201\u2013214 . Springer","DOI":"10.1007\/978-3-031-28993-4_15"},{"key":"2247_CR33","doi-asserted-by":"crossref","unstructured":"Hu X, Zhou Z, Li H, Hu Y, Gu F, Kersten J, Fan H, Klan F (2022) Location reference recognition from texts: A survey and comparison. ACM Computing Surveys","DOI":"10.1145\/3625819"},{"issue":"17","key":"2247_CR34","doi-asserted-by":"publisher","first-page":"16259","DOI":"10.1109\/JIOT.2022.3150967","volume":"9","author":"X Hu","year":"2022","unstructured":"Hu X, Zhou Z, Sun Y, Kersten J, Klan F, Fan H, Wiegmann M (2022) Gazpne2: A general place name extractor for microblogs fusing gazetteers and pretrained transformer models. IEEE Internet Things J 9(17):16259\u201316271. https:\/\/doi.org\/10.1109\/JIOT.2022.3150967","journal-title":"IEEE Internet Things J"},{"issue":"6","key":"2247_CR35","doi-asserted-by":"publisher","first-page":"1150","DOI":"10.1109\/TKDE.2018.2852764","volume":"31","author":"P Li","year":"2019","unstructured":"Li P, Lu H, Kanhabua N, Zhao S, Pan G (2019) Location inference for non-geotagged tweets in user timelines. IEEE Trans Knowl Data Eng 31(6):1150\u20131165. https:\/\/doi.org\/10.1109\/TKDE.2018.2852764","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2247_CR36","doi-asserted-by":"crossref","unstructured":"Lindamood J, Heatherly R, Kantarcioglu M, Thuraisingham B (2009) Inferring private information using social network data. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1145\u20131146","DOI":"10.1145\/1526709.1526899"},{"key":"2247_CR37","doi-asserted-by":"crossref","unstructured":"Ren K, Zhang S, Lin H (2012) Where are you settling down: Geo-locating twitter users based on tweets and social networks. In: Information Retrieval Technology: 8th Asia Information Retrieval Societies Conference, AIRS 2012, Tianjin, China, December 17-19, 2012. Proceedings 8, pp. 150\u2013161 . Springer","DOI":"10.1007\/978-3-642-35341-3_13"},{"key":"2247_CR38","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1613\/jair.4200","volume":"49","author":"B Han","year":"2014","unstructured":"Han B, Cook P, Baldwin T (2014) Text-based twitter user geolocation prediction. Journal of Artificial Intelligence Research 49:451\u2013500","journal-title":"Journal of Artificial Intelligence Research"},{"key":"2247_CR39","doi-asserted-by":"crossref","unstructured":"Malmasi S, Dras M (2016) Location mention detection in tweets and microblogs. In: Computational Linguistics: 14th International Conference of the Pacific Association for Computational Linguistics, PACLING 2015, Bali, Indonesia, May 19-21, 2015, Revised Selected Papers 14, pp. 123\u2013134 . Springer","DOI":"10.1007\/978-981-10-0515-2_9"},{"key":"2247_CR40","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1007\/s11280-014-0291-3","volume":"18","author":"S Unankard","year":"2015","unstructured":"Unankard S, Li X, Sharaf MA (2015) Emerging event detection in social networks with location sensitivity. World Wide Web 18:1393\u20131417","journal-title":"World Wide Web"},{"key":"2247_CR41","doi-asserted-by":"publisher","unstructured":"Middleton SE, Kordopatis-Zilos G, Papadopoulos S, Kompatsiaris Y (2018) Location extraction from social media: Geoparsing, location disambiguation, and geotagging. ACM Trans. Inf. Syst. 36(4) . https:\/\/doi.org\/10.1145\/3202662","DOI":"10.1145\/3202662"},{"key":"2247_CR42","doi-asserted-by":"publisher","unstructured":"Jiang X, Torvik VI (2020) On the ambiguity and relevance of place names in scientific text. JCDL \u201920. Association for Computing Machinery, New York, NY, USA . https:\/\/doi.org\/10.1145\/3383583.3398618","DOI":"10.1145\/3383583.3398618"},{"key":"2247_CR43","unstructured":"SILAA V, MASUI F, PTASZYNSKI M (2021) Automatic sentiment score generation method for sightspots review system. In: Proceedings of the 2021 International Workshop on Modern Science and Technology, vol. 2021, pp. 157\u2013162 . The International Center of National University Corporation Kitami Institute\u00a0."},{"key":"2247_CR44","unstructured":"An H-w, Moon N (2022) Design of recommendation system for tourist spot using sentiment analysis based on cnn-lstm. Journal of Ambient Intelligence and Humanized Computing, 1\u201311"},{"key":"2247_CR45","doi-asserted-by":"crossref","unstructured":"Ptaszynski M, Masui F, Fukushima Y, Oikawa Y, Hayakawa H, Miyamori Y, Takahashi K, Kawajiri S (2021) Deep learning for information triage on twitter. Applied Sciences 11(14)","DOI":"10.3390\/app11146340"},{"issue":"23","key":"2247_CR46","doi-asserted-by":"publisher","first-page":"8631","DOI":"10.3390\/app10238631","volume":"10","author":"V Maslej-Kre\u0161\u0148\u00e1kov\u00e1","year":"2020","unstructured":"Maslej-Kre\u0161\u0148\u00e1kov\u00e1 V, Sarnovsk\u1ef3 M, Butka P, Machov\u00e1 K (2020) Comparison of deep learning models and various text pre-processing techniques for the toxic comments classification. Appl Sci 10(23):8631","journal-title":"Appl Sci"},{"issue":"1","key":"2247_CR47","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20(1):37\u201346","journal-title":"Educ Psychol Measur"},{"key":"2247_CR48","doi-asserted-by":"crossref","unstructured":"Landis JR, Koch GG (1977) An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 363\u2013374","DOI":"10.2307\/2529786"},{"issue":"12","key":"2247_CR49","doi-asserted-by":"publisher","first-page":"290","DOI":"10.2196\/jmir.3617","volume":"16","author":"M Conway","year":"2014","unstructured":"Conway M (2014) Ethical issues in using twitter for public health surveillance and research: developing a taxonomy of ethical concepts from the research literature. J Med Internet Res 16(12):290","journal-title":"J Med Internet Res"},{"key":"2247_CR50","doi-asserted-by":"crossref","unstructured":"Purves RS, Clough P, Jones CB, Hall MH, Murdock V, et al. (2018)Geographic information retrieval: Progress and challenges in spatial search of text. Foundations and Trends\u00ae in Information Retrieval 12(2-3), 164\u2013318","DOI":"10.1561\/1500000034"},{"issue":"3","key":"2247_CR51","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1177\/17470161221087542","volume":"18","author":"J Mahoney","year":"2022","unstructured":"Mahoney J, Le Louvier K, Lawson S, Bertel D, Ambrosetti E (2022) Ethical considerations in social media analytics in the context of migration: lessons learned from a horizon 2020 project. Research Ethics 18(3):226\u2013240","journal-title":"Research Ethics"},{"issue":"2","key":"2247_CR52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1177\/1747016117738559","volume":"14","author":"J Taylor","year":"2018","unstructured":"Taylor J, Pagliari C (2018) Mining social media data: How are research sponsors and researchers addressing the ethical challenges? Research Ethics 14(2):1\u201339","journal-title":"Research Ethics"},{"key":"2247_CR53","doi-asserted-by":"crossref","unstructured":"Deng L, Yu D et al.(2014) Deep learning: methods and applications. Foundations and trends\u00ae in signal processing 7(3\u20134), 197\u2013387","DOI":"10.1561\/2000000039"},{"key":"2247_CR54","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez GV, Krpalkova L, Riordan D, Walsh J (2020) Deep learning vs. traditional computer vision. In: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1 1, pp. 128\u2013144 . Springer","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"2247_CR55","doi-asserted-by":"crossref","unstructured":"Dong B, Wang X (2016) Comparison deep learning method to traditional methods using for network intrusion detection. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 581\u2013585 . IEEE","DOI":"10.1109\/ICCSN.2016.7586590"},{"key":"2247_CR56","unstructured":"Neubig G (2017) Neural machine translation and sequence-to-sequence models: A tutorial. arXiv preprint arXiv:1703.01619"},{"key":"2247_CR57","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30"},{"key":"2247_CR58","unstructured":"Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473"},{"key":"2247_CR59","unstructured":"Nozza D, Bianchi F, Hovy D (2020) What the [mask]? making sense of language-specific bert models. arXiv preprint arXiv:2003.02912"},{"key":"2247_CR60","doi-asserted-by":"crossref","unstructured":"Shi W, Demberg V (2019) Next sentence prediction helps implicit discourse relation classification within and across domains. 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), pp. 5790\u20135796","DOI":"10.18653\/v1\/D19-1586"},{"key":"2247_CR61","doi-asserted-by":"crossref","unstructured":"Shaw P, Uszkoreit J, Vaswani A (2018) Self-attention with relative position representations. arXiv preprint arXiv:1803.02155","DOI":"10.18653\/v1\/N18-2074"},{"key":"2247_CR62","unstructured":"Grefenstette G, Tapanainen P (1994) What is a word, what is a sentence?: problems of tokenisation"},{"key":"2247_CR63","doi-asserted-by":"crossref","unstructured":"Grefenstette G (1999) Tokenization. In: Syntactic Wordclass Tagging, pp. 117\u2013133. Springer, ???","DOI":"10.1007\/978-94-015-9273-4_9"},{"key":"2247_CR64","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805"},{"key":"2247_CR65","unstructured":"Han B, Cook P, Baldwin T (2012) Geolocation prediction in social media data by finding location indicative words. In: Proceedings of COLING 2012, pp. 1045\u20131062"},{"key":"2247_CR66","doi-asserted-by":"crossref","unstructured":"Sammut C, Webb GI (2011) Encyclopedia of Machine Learning. Springer, ???","DOI":"10.1007\/978-0-387-30164-8"},{"key":"2247_CR67","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T (2019) Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108"},{"key":"2247_CR68","unstructured":"Clark K, Luong M-T, Le QV, Manning CD (2020) Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555"},{"key":"2247_CR69","unstructured":"Lan Z, Chen M, Goodman S, Gimpel K, Sharma P, Soricut R (2019) ALBERT: A lite BERT for self-supervised learning of language representations. CoRR abs\/1909.11942arXiv:1909.11942"},{"key":"2247_CR70","doi-asserted-by":"crossref","unstructured":"Nguyen DQ, Vu T, Nguyen AT (2020) Bertweet: A pre-trained language model for english tweets. arXiv preprint arXiv:2005.10200","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"2247_CR71","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692"},{"issue":"1","key":"2247_CR72","first-page":"7070","volume":"23","author":"AS Maiya","year":"2022","unstructured":"Maiya AS (2022) ktrain: A low-code library for augmented machine learning. The Journal of Machine Learning Research 23(1):7070\u20137075","journal-title":"The Journal of Machine Learning Research"},{"key":"2247_CR73","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"2247_CR74","unstructured":"Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101"},{"key":"2247_CR75","unstructured":"Loshchilov I, Hutter F. Stochastic gradient descent with warm restarts. In: Proceedings of the 5th Int. Conf. Learning Representations, pp. 1\u201316"},{"key":"2247_CR76","doi-asserted-by":"crossref","unstructured":"Mohtaj S, M\u00f6ller S (2022) The impact of pre-processing on the performance of automated fake news detection. In: Experimental IR Meets Multilinguality, Multimodality, and Interaction: 13th International Conference of the CLEF Association, CLEF 2022, Bologna, Italy, September 5\u20138, 2022, Proceedings, pp. 93\u2013102 . Springer","DOI":"10.1007\/978-3-031-13643-6_7"},{"issue":"2","key":"2247_CR77","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1137\/0206024","volume":"6","author":"DE Knuth","year":"1977","unstructured":"Knuth DE, Morris JH Jr, Pratt VR (1977) Fast pattern matching in strings. SIAM J Comput 6(2):323\u2013350","journal-title":"SIAM J Comput"},{"issue":"4","key":"2247_CR78","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1021\/ci030463z","volume":"44","author":"S Sheik","year":"2004","unstructured":"Sheik S, Aggarwal SK, Poddar A, Balakrishnan N, Sekar K (2004) A fast pattern matching algorithm. J Chem Inf Comput Sci 44(4):1251\u20131256","journal-title":"J Chem Inf Comput Sci"},{"issue":"4","key":"2247_CR79","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1016\/j.jda.2006.11.004","volume":"5","author":"F Franek","year":"2007","unstructured":"Franek F, Jennings CG, Smyth WF (2007) A simple fast hybrid pattern-matching algorithm. Journal of Discrete Algorithms 5(4):682\u2013695","journal-title":"Journal of Discrete Algorithms"},{"key":"2247_CR80","doi-asserted-by":"crossref","unstructured":"Inoue M, Kobayashi T (1985) The research domain and scale construction of adjective-pairs in a semantic differential method in japan. Japanese Journal of Educational Psychology","DOI":"10.5926\/jjep1953.33.3_253"},{"key":"2247_CR81","doi-asserted-by":"crossref","unstructured":"Urabe Y, Rzepka R, Araki K (2013) Emoticon recommendation system for effective communication. In: Proceedings of the 2013 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1460\u20131461","DOI":"10.1145\/2492517.2492594"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02247-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02247-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02247-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T16:57:11Z","timestamp":1760547431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02247-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,16]]},"references-count":81,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["2247"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02247-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,6,16]]},"assertion":[{"value":"3 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}