{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T21:02:46Z","timestamp":1767906166944,"version":"3.49.0"},"reference-count":62,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T00:00:00Z","timestamp":1721001600000},"content-version":"vor","delay-in-days":196,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008793","name":"Universidad del Rosario","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008793","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100026234","name":"Big Ten Academic Alliance","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100026234","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>Governments and law enforcement agencies (LEAs) are increasingly concerned about growing illicit activities in cyberspace, such as cybercrimes, cyberespionage, cyberterrorism, and cyberwarfare. In the particular context of cyberterrorism, hostile social manipulation (HSM) represents a strategy that employs different manipulation methods, mostly through social media, to promote extremism in social groups and encourage hostile behavior against a target. Thus, this paper proposes a framework based on natural language processing (NLP) that detects and inspects supposed HSM actions to support law enforcement agencies (LEAs) in the prevention of cyberterrorism. The proposal integrates different NLP techniques through three models: (i) a similarity model that relates content with similar semantic meaning, (ii) a polarity analysis model that estimates polarity, and (iii) a named\u2010entity recognition (NER) model that recognizes relevant entities. In addition, our proposed framework is evaluated in each of its components through exhaustive experiments and is tested with a particular use case related to violent protests in Ecuador in October 2021. Use case\u2019s results indicate that 3 and 4 clusters are obtained when Spanish and English\u2010translated tweets are used, respectively. An analysis of polarity over English\u2010translated tweets allows us to identify, through two different methods, the most negative cluster (#1). The results of the extraction of the mentions show that our framework is able to identify entities of the type of person that may be at risk with a precision of 89.91%. Knowledge graphs achieved in our use case allow us to identify how nodes that promote HSM are interconnected and work collaboratively. Finally, the computational costs of our proposal are quite favorable as memory consumption of similarity and polarity models is proportional to the number of processed tweets, confirming the feasibility of the solution in a real context.<\/jats:p>","DOI":"10.1155\/2024\/3380488","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T16:19:16Z","timestamp":1721060356000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An NLP\u2010Based Framework to Spot Extremist Networks in Social Media"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7751-9738","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Zapata Rozo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7244-2631","authenticated-orcid":false,"given":"Daniel","family":"D\u00edaz-L\u00f3pez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4827-6682","authenticated-orcid":false,"given":"Javier","family":"Pastor-Galindo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6424-3322","authenticated-orcid":false,"given":"F\u00e9lix","family":"G\u00f3mez M\u00e1rmol","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6760-259X","authenticated-orcid":false,"given":"Umit","family":"Karabiyik","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2024,7,15]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5415724"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/msp.2006.27"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102145"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.52306\/01010418zdcd5438"},{"key":"e_1_2_12_5_2","article-title":"The colombian trap: another partial peace","volume":"258","author":"Nussio E.","year":"2020","journal-title":"CSS Analyses in Security Policy"},{"key":"e_1_2_12_6_2","first-page":"1","article-title":"Systematic literature review to investigate the application of open source intelligence (osint) with artificial intelligence","volume":"1","author":"Goncalves J.","year":"2020","journal-title":"Journal of Applied Security Research"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2965257"},{"key":"e_1_2_12_8_2","doi-asserted-by":"crossref","unstructured":"WilliamsH. J.andBlumI. Defining second generation open source intelligence (osint) for the defense enterprise 2018 RAND Corporation Santa Monica CA USA Technical report.","DOI":"10.7249\/RR1964"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/mic.2021.3088335"},{"key":"e_1_2_12_10_2","volume-title":"Natural Language Processing with Spark NLP: Learning to Understand Text at Scale","author":"Thomas A.","year":"2020"},{"key":"e_1_2_12_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9111779"},{"key":"e_1_2_12_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsm.2020.3031573"},{"key":"e_1_2_12_13_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7955637"},{"key":"e_1_2_12_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/mic.2022.3154712"},{"key":"e_1_2_12_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/msec.2024.3380511"},{"key":"e_1_2_12_16_2","first-page":"42","article-title":"An architecture to fight cyberterrorism with natural language processing","volume":"1","author":"Zapata A.","year":"2022","journal-title":"VII Jornadas Nacionales de Investigaci\u00f3n en Ciberseguridad (JNIC)"},{"key":"e_1_2_12_17_2","doi-asserted-by":"crossref","unstructured":"KumariS. SaquibZ. andPawarS. Machine Learning Approach for Text Classification in Cybercrime Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) August 2018 Pune India.","DOI":"10.1109\/ICCUBEA.2018.8697442"},{"key":"e_1_2_12_18_2","unstructured":"SaifH. Fern\u00e1ndezM. HeY. andAlaniH. Evaluation datasets for twitter sentiment analysis: a survey and a new dataset the sts-gold Proceedings of the 1st Interantional Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013) October 2013 Brisbane Australia."},{"key":"e_1_2_12_19_2","unstructured":"NiekJ. Sanders-twitter sentiment corpus 2011 http:\/\/www.sananalytics.com\/lab\/twitter-sentiment\/."},{"key":"e_1_2_12_20_2","doi-asserted-by":"crossref","unstructured":"RosenthalS. RitterA. NakovP. andStoyanovV. SemEval-2014 task 9: sentiment analysis in twitter Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) August 2014 Dublin Ireland Association for Computational Linguistics 73\u201380.","DOI":"10.3115\/v1\/S14-2009"},{"key":"e_1_2_12_21_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/1238780"},{"key":"e_1_2_12_22_2","unstructured":"TribeF. How isis uses twitter 2019 https:\/\/www.kaggle.com\/datasets\/fifthtribe\/how-isis-uses-twitter."},{"key":"e_1_2_12_23_2","doi-asserted-by":"publisher","DOI":"10.4018\/ijswis.2020010104"},{"key":"e_1_2_12_24_2","doi-asserted-by":"crossref","unstructured":"UzelV. N. Sara\u00e7 E\u015fsizE. andAy\u015fe \u00d6zelS. Using fuzzy sets for detecting cyber terrorism and extremism in the text Proceedings of the 2018 Innovations in Intelligent Systems and Applications Conference (ASYU) October 2018 Adana Turkey 1\u20134.","DOI":"10.1109\/ASYU.2018.8554017"},{"key":"e_1_2_12_25_2","unstructured":"MunezeroM. MozgovoyM. KakkonenT. KlyuevV. andSutinenE. Antisocial behavior corpus for harmful language detection Proceedings of the 2013 Federated Conference on Computer Science and Information Systems September 2013 Krakow Poland 261\u2013265."},{"key":"e_1_2_12_26_2","doi-asserted-by":"crossref","unstructured":"PangBo LeeL. andVaithyanathanS. Thumbs up? sentiment classification using machine learning techniques Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002) July 2002 Philadelphia PA USA Association for Computational Linguistics 79\u201386.","DOI":"10.3115\/1118693.1118704"},{"key":"e_1_2_12_27_2","doi-asserted-by":"publisher","DOI":"10.1177\/0165551520917651"},{"key":"e_1_2_12_28_2","doi-asserted-by":"crossref","unstructured":"NouhM. NurseJ. R. C. andGoldsmithM. Understanding the radical mind: identifying signals to detect extremist content on twitter Proceedings of the 2019 IEEE International Conference on Intelligence and Security Informatics (ISI) July 2019 Miami FL USA 98\u2013103.","DOI":"10.1109\/ISI.2019.8823548"},{"key":"e_1_2_12_29_2","unstructured":"ActiveGalaXy Tweets targeting isis 2019 https:\/\/www.brookings.edu\/wp-content\/uploads\/2016\/06\/isis_twitter_census_berger_morgan.pdf."},{"key":"e_1_2_12_30_2","doi-asserted-by":"crossref","unstructured":"GialampoukidisI. GeorgeK. TsikrikaT. PapadopoulosS. VrochidisS. andKompatsiarisI. Detection of terrorism-related twitter communities using centrality scores Proceedings of the 2nd International Workshop on Multimedia Forensics and Security MFSec \u201917 June 2017 New York NY USA Association for Computing Machinery 21\u201325.","DOI":"10.1145\/3078897.3080534"},{"key":"e_1_2_12_31_2","unstructured":"Cristian Cardellino Spanish billion words corpus and embeddings 2019 https:\/\/github.com\/crscardellino\/sbwce."},{"key":"e_1_2_12_32_2","unstructured":"Google word2vec 2013 https:\/\/www.tensorflow.org\/text\/tutorials\/word2vec."},{"key":"e_1_2_12_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3013145"},{"key":"e_1_2_12_34_2","doi-asserted-by":"crossref","DOI":"10.1201\/9780429443237","volume-title":"Fundamentals of Data Science","author":"Wagh S. J.","year":"2021"},{"key":"e_1_2_12_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.tele.2020.101517"},{"key":"e_1_2_12_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2983656"},{"key":"e_1_2_12_37_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-44242-2","volume-title":"Trump, Twitter, and the American Democracy: Political Communication in the Digital Age","author":"Ouyang Yu","year":"2020"},{"key":"e_1_2_12_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2022.122112"},{"key":"e_1_2_12_39_2","doi-asserted-by":"publisher","DOI":"10.1086\/715162"},{"key":"e_1_2_12_40_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_12_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2020.03.003"},{"key":"e_1_2_12_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.05.109"},{"key":"e_1_2_12_43_2","doi-asserted-by":"crossref","unstructured":"PoornimaA.andSathiya PriyaK. A comparative sentiment analysis of sentence embedding using machine learning techniques Proceedings of the 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) March 2020 Coimbatore India 493\u2013496.","DOI":"10.1109\/ICACCS48705.2020.9074312"},{"key":"e_1_2_12_44_2","doi-asserted-by":"crossref","unstructured":"ZahoorS.andRohillaR. Twitter sentiment analysis using lexical or rule based approach: a case study Proceedings of the 2020 8th International Conference on Reliability Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) June 2020 Noida India 537\u2013542.","DOI":"10.1109\/ICRITO48877.2020.9197910"},{"key":"e_1_2_12_45_2","unstructured":"LiuZ. JiangF. HuY. ShiC. andFungP. Ner-bert: a pre-trained model for low-resource entity tagging 2021 https:\/\/arxiv.org\/abs\/2112.00405."},{"key":"e_1_2_12_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2012.03.006"},{"key":"e_1_2_12_47_2","first-page":"321","article-title":"Between a rock and a hard place: Ecuador during the covid-19 pandemic","volume":"41","author":"Dandoy R.","year":"2021","journal-title":"Revista de Ciencia Pol\u00edtica"},{"key":"e_1_2_12_48_2","doi-asserted-by":"publisher","DOI":"10.51983\/ajcst-2019.8.s2.2037"},{"key":"e_1_2_12_49_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2024.03.025"},{"key":"e_1_2_12_50_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph19127027"},{"key":"e_1_2_12_51_2","doi-asserted-by":"crossref","unstructured":"DixonA.andBirksD. Improving policing with natural language processing Proceedings of the 1st Workshop on NLP for Positive Impact August 2021 Virtual 115\u2013124.","DOI":"10.18653\/v1\/2021.nlp4posimpact-1.13"},{"key":"e_1_2_12_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2019.02.028"},{"key":"e_1_2_12_53_2","unstructured":"Roxanne Roxanne project 2023 https:\/\/www.roxanne-euproject.org\/."},{"key":"e_1_2_12_54_2","unstructured":"DikiciE. FabienM. Hor\u0131nekJ. HughesJ. Jano\u0161ikM. KovacM. MotlicekP. NguyenH. H. ParidaS. RohdinJ. Sk\u00e1celM. ZerrS. DietrichK. ZhuD. andRoxsdA. K. A simulated dataset of communication in organized crime ISCA Symposium on Security and Privacy in Speech Communication January 2021 Graz Austria."},{"key":"e_1_2_12_55_2","doi-asserted-by":"crossref","unstructured":"FabienM. Saeed SarfjooS. MotlicekP. andMadikeriS. Graph2speak: improving speaker identification using network knowledge in criminal conversational data 2020 https:\/\/arxiv.org\/abs\/2006.02093.","DOI":"10.21437\/SPSC.2021-3"},{"key":"e_1_2_12_56_2","unstructured":"AiLECS Lab The explain project 2023 https:\/\/ailecs.org\/project\/the-explain-project\/."},{"key":"e_1_2_12_57_2","unstructured":"ShepardS. Japan is experimenting with ai to combat terrorism 2019 https:\/\/securitytoday.com\/articles\/2019\/01\/18\/japan-is-experimenting-with-ai-to-combat-terrorism.aspx."},{"key":"e_1_2_12_58_2","first-page":"3","article-title":"Harnessing ai for due diligence in cbi programmes. legal and ethical challenges","volume":"4","author":"Joseph J. D.","year":"2022","journal-title":"Journal of Ethics and Legal Technologies (JELT)"},{"key":"e_1_2_12_59_2","doi-asserted-by":"crossref","first-page":"251","DOI":"10.5771\/9783748922834-251","volume-title":"Legal Tech","author":"Dymitruk M.","year":"2021"},{"key":"e_1_2_12_60_2","first-page":"88","volume-title":"Research Handbook on the Law of Artificial Intelligence","author":"Geslevich Packin N.","year":"2018"},{"key":"e_1_2_12_61_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaz3873"},{"key":"e_1_2_12_62_2","doi-asserted-by":"publisher","DOI":"10.1177\/2053951716679679"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2024\/3380488","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T16:19:35Z","timestamp":1721060375000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2024\/3380488"}},"subtitle":[],"editor":[{"given":"Fei","family":"Xiong","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.1155\/2024\/3380488"],"URL":"https:\/\/doi.org\/10.1155\/2024\/3380488","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"2024-01-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"3380488"}}