{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:31:25Z","timestamp":1772555485246,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cloud Comp"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This study presents a novel approach to identifying trolls and toxic content on social media using deep learning. We developed a machine-learning model capable of detecting toxic images through their embedded text content. Our approach leverages GloVe word embeddings to enhance the model's predictive accuracy. We also utilized Graph Convolutional Networks (GCNs) to effectively analyze the intricate relationships inherent in social media data. The practical implications of our work are significant, despite some limitations in the model's performance. While the model accurately identifies toxic content more than half of the time, it struggles with precision, correctly identifying positive instances less than 50% of the time. Additionally, its ability to detect all positive cases (recall) is limited, capturing only 40% of them. The F1-score, which is a measure of the model's balance between precision and recall, stands at around 0.4, indicating a need for further refinement to enhance its effectiveness. This research offers a promising step towards more effective monitoring and moderation of toxic content on social platforms.<\/jats:p>","DOI":"10.1186\/s13677-024-00600-4","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T23:02:17Z","timestamp":1707260537000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Graph convolution networks for social media trolls detection use deep feature extraction"],"prefix":"10.1186","volume":"13","author":[{"given":"Muhammad","family":"Asif","sequence":"first","affiliation":[]},{"given":"Muna","family":"Al-Razgan","sequence":"additional","affiliation":[]},{"given":"Yasser A.","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Long","family":"Yunrong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"600_CR1","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.3390\/electronics9081215","volume":"9","author":"S Kim","year":"2020","unstructured":"Kim S, Park M, Lee S, Kim J (2020) Smart home forensics\u2014data analysis of IoT devices. Electronics 9:1215. https:\/\/doi.org\/10.3390\/electronics9081215","journal-title":"Electronics"},{"key":"600_CR2","doi-asserted-by":"publisher","first-page":"3967","DOI":"10.3390\/s22113967","volume":"22","author":"S Solera-Cotanilla","year":"2022","unstructured":"Solera-Cotanilla S, Vega-Barbas M, P\u00e9rez J, L\u00f3pez G, Matanza J, \u00c1lvarez-Campana M (2022) Security and privacy analysis of youth-oriented connected devices. Sensors 22:3967. https:\/\/doi.org\/10.3390\/s22113967","journal-title":"Sensors"},{"key":"600_CR3","doi-asserted-by":"publisher","first-page":"7027","DOI":"10.3390\/ijerph19127027","volume":"19","author":"Z Shahbazi","year":"2022","unstructured":"Shahbazi Z, Byun Y-C (2022) NLP-based digital forensic analysis for online social network based on system security. Int J Environ Res Public Health 19:7027. https:\/\/doi.org\/10.3390\/ijerph19127027","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"600_CR4","doi-asserted-by":"publisher","first-page":"e23254","DOI":"10.1016\/j.heliyon.2023.e23254","volume":"10","author":"AA Khan","year":"2024","unstructured":"Khan AA, Zhang X, Hajjej F, Yang J, Ku CS, Por LY (2024) ASMF: Ambient social media forensics chain of custody with an intelligent digital investigation process using federated learning. Heliyon. 10(1):e23254. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e23254. (ISSN 2405-8440)","journal-title":"Heliyon."},{"key":"600_CR5","unstructured":"Manheim KM, Kaplan L (2019) Artificial intelligence: risks to privacy and democracy (October 25, 2018). 21 Yale J Law Technol. 106. Loyola Law School, Los Angeles Legal Studies Research Paper No. 2018\u201337, Available at SSRN: https:\/\/ssrn.com\/abstract=3273016"},{"key":"600_CR6","doi-asserted-by":"publisher","first-page":"103123","DOI":"10.1016\/j.cose.2023.103123","volume":"128","author":"MS Pour","year":"2023","unstructured":"Pour MS, Nader C, Friday K, Bou-Harb E (2023) A comprehensive survey of recent internet measurement techniques for cyber security. Comput Secur. 128:103123. https:\/\/doi.org\/10.1016\/j.cose.2023.103123. (ISSN 0167\u20134048)","journal-title":"Comput Secur."},{"key":"600_CR7","doi-asserted-by":"publisher","first-page":"3343","DOI":"10.1007\/s10586-022-03568-5","volume":"25","author":"AC Ikegwu","year":"2022","unstructured":"Ikegwu AC, Nweke HF, Anikwe CV et al (2022) Big data analytics for data-driven industry: a review of data sources, tools, challenges, solutions, and research directions. Cluster Comput 25:3343\u20133387. https:\/\/doi.org\/10.1007\/s10586-022-03568-5","journal-title":"Cluster Comput"},{"key":"600_CR8","doi-asserted-by":"publisher","unstructured":"Rathore MM, Paul A, Ahmad A, Imran M, Guizani M (2017) Big data analytics of geosocial media for planning and real-time decisions. Paris: 2017 IEEE International Conference on Communications (ICC). pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICC.2017.7996545.","DOI":"10.1109\/ICC.2017.7996545"},{"issue":"1","key":"600_CR9","first-page":"2","volume":"6","author":"F Bandr","year":"2020","unstructured":"Bandr F (2020) Digital forensics: crimes and challenges in online social networks forensics. J Arab American Univ. 6(1):2.\u00a0Available at:\u00a0https:\/\/digitalcommons.aaru.edu.jo\/aaup\/vol6\/iss1\/2","journal-title":"J Arab American Univ."},{"key":"600_CR10","doi-asserted-by":"publisher","first-page":"580","DOI":"10.3390\/jcp1040029","volume":"1","author":"C Horan","year":"2021","unstructured":"Horan C, Saiedian H (2021) Cyber crime investigation: landscape, challenges, and future research directions. J Cybersecur Priv 1:580\u2013596. https:\/\/doi.org\/10.3390\/jcp1040029","journal-title":"J Cybersecur Priv"},{"key":"600_CR11","doi-asserted-by":"publisher","unstructured":"Baca M, Cosic J, Cosic Z (2013) Forensic analysis of social networks (case study). Proceedings of the ITI 2013 35th International Conference on Information Technology Interfaces, Cavtat, Croatia. pp. 219\u2013223. https:\/\/doi.org\/10.2498\/iti.2013.0526.","DOI":"10.2498\/iti.2013.0526"},{"key":"600_CR12","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.diin.2019.02.001","volume":"28","author":"H Arshad","year":"2019","unstructured":"Arshad H, Jantan A, Omolara E (2019) Evidence collection and forensics on social networks: Research challenges and directions. Digit Invest. 28:126\u2013138. https:\/\/doi.org\/10.1016\/j.diin.2019.02.001. (ISSN 1742\u20132876)","journal-title":"Digit Invest."},{"key":"600_CR13","doi-asserted-by":"publisher","unstructured":"Elezaj O, Yayilgan SY, Kalemi E (2021) Criminal network community detection in social media forensics. In: Yildirim Yayilgan S, Bajwa IS, Sanfilippo F. (eds) Intelligent technologies and applications. INTAP 2020. Communications in Computer and Information Science. Cham: Springer. https:\/\/doi.org\/10.1007\/978-3-030-71711-7_31","DOI":"10.1007\/978-3-030-71711-7_31"},{"issue":"9","key":"600_CR14","doi-asserted-by":"publisher","first-page":"e20281","DOI":"10.1016\/j.heliyon.2023.e20281","volume":"9","author":"RK Das","year":"2023","unstructured":"Das RK, Islam M, Hasan MM, Razia S, Hassan M, Khushbu SA (2023) Sentiment analysis in multilingual context: comparative analysis of machine learning and hybrid deep learning models. Heliyon 9(9):e20281. https:\/\/doi.org\/10.1016\/j.heliyon.2023.e20281","journal-title":"Heliyon"},{"key":"600_CR15","doi-asserted-by":"publisher","first-page":"483","DOI":"10.3390\/electronics9030483","volume":"9","author":"NC Dang","year":"2020","unstructured":"Dang NC, Moreno-Garc\u00eda MN, De la Prieta F (2020) Sentiment analysis based on deep learning: a comparative study. Electronics 9:483. https:\/\/doi.org\/10.3390\/electronics9030483","journal-title":"Electronics"},{"key":"600_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s13735-023-00308-2","volume":"12","author":"C Sahoo","year":"2023","unstructured":"Sahoo C, Wankhade M, Singh BK (2023) Sentiment analysis using deep learning techniques: a comprehensive review. Int J Multimed Info Retr 12:41. https:\/\/doi.org\/10.1007\/s13735-023-00308-2","journal-title":"Int J Multimed Info Retr"},{"key":"600_CR17","doi-asserted-by":"publisher","first-page":"629","DOI":"10.3390\/info14120629","volume":"14","author":"K Gupta","year":"2023","unstructured":"Gupta K, Oladimeji D, Varol C, Rasheed A, Shahshidhar N (2023) A comprehensive survey on artifact recovery from social media platforms: approaches and future research directions. Information 14:629. https:\/\/doi.org\/10.3390\/info14120629","journal-title":"Information"},{"key":"600_CR18","doi-asserted-by":"publisher","unstructured":"Uppada SK, Patel P, Sivaselvan B (2022) An image and text-based multimodal model for detecting fake news in OSN's. J Intell Inf Syst. 1\u201327. https:\/\/doi.org\/10.1007\/s10844-022-00764-y","DOI":"10.1007\/s10844-022-00764-y"},{"key":"600_CR19","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/s42979-021-00958-1","volume":"3","author":"NV Babu","year":"2022","unstructured":"Babu NV, Kanaga EGM (2022) Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput Sci 3:74. https:\/\/doi.org\/10.1007\/s42979-021-00958-1","journal-title":"SN Comput Sci"},{"key":"600_CR20","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2:160. https:\/\/doi.org\/10.1007\/s42979-021-00592-x","journal-title":"SN Comput Sci"},{"key":"600_CR21","doi-asserted-by":"publisher","first-page":"2449","DOI":"10.3390\/electronics12112449","volume":"12","author":"K Mali\u0144ski","year":"2023","unstructured":"Mali\u0144ski K, Okarma K (2023) Analysis of image preprocessing and Binarization methods for OCR-based detection and classification of electronic integrated circuit labeling. Electronics 12:2449. https:\/\/doi.org\/10.3390\/electronics12112449","journal-title":"Electronics"},{"key":"600_CR22","doi-asserted-by":"publisher","first-page":"301446","DOI":"10.1016\/j.fsidi.2022.301446","volume":"43","author":"A MacDermott","year":"2022","unstructured":"MacDermott A, Motylinski M, Iqbal F, Stamp K, Hussain M, Marrington A (2022) Using deep learning to detect social media \u2018trolls\u2019. Forensic Sci Int: Digit Invest. 43:301446. https:\/\/doi.org\/10.1016\/j.fsidi.2022.301446. ISSN 2666\u20132817","journal-title":"For Sci Int: Digit Invest."},{"issue":"2022","key":"600_CR23","doi-asserted-by":"publisher","first-page":"4637594","DOI":"10.1155\/2022\/4637594","volume":"8","author":"MH Al-Adhaileh","year":"2022","unstructured":"Al-Adhaileh MH, Aldhyani THH, Alghamdi AD (2022) Online troll reviewer detection using deep learning techniques. Appl Bionics Biomech 8(2022):4637594. https:\/\/doi.org\/10.1155\/2022\/4637594","journal-title":"Appl Bionics Biomech"},{"issue":"6","key":"600_CR24","doi-asserted-by":"publisher","first-page":"562","DOI":"10.3390\/e21060562","volume":"21","author":"H Michalak","year":"2019","unstructured":"Michalak H, Okarma K (2019) Improvement of image Binarization methods using image preprocessing with local entropy filtering for alphanumerical character recognition purposes. Entropy (Basel) 21(6):562. https:\/\/doi.org\/10.3390\/e21060562","journal-title":"Entropy (Basel)"},{"key":"600_CR25","first-page":"361","volume":"2018","author":"H Michalak","year":"2018","unstructured":"Michalak H, Okarma K (2018) Region based adaptive binarization for optical character recognition purposes. Int Interdiscipl PhD Workshop (IIPhDW) 2018:361\u2013366","journal-title":"Int Interdiscipl PhD Workshop (IIPhDW)"},{"key":"600_CR26","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R, Nishio M, Do RKG et al (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611\u2013629. https:\/\/doi.org\/10.1007\/s13244-018-0639-9","journal-title":"Insights Imaging"},{"key":"600_CR27","doi-asserted-by":"publisher","unstructured":"Uzair B, Mehdi M, Sibghat B, Hao T (2023) Editorial: Investigating AI-based smart precision agriculture techniques. Front Plant Sci. 14. https:\/\/doi.org\/10.3389\/fpls.2023.1237783","DOI":"10.3389\/fpls.2023.1237783"},{"key":"600_CR28","doi-asserted-by":"publisher","first-page":"24365","DOI":"10.1007\/s11042-021-10707-4","volume":"80","author":"M Puttagunta","year":"2021","unstructured":"Puttagunta M, Ravi S (2021) Medical image analysis based on deep learning approach. Multimed Tools Appl 80:24365\u201324398. https:\/\/doi.org\/10.1007\/s11042-021-10707-4","journal-title":"Multimed Tools Appl"},{"key":"600_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2023\/8342104","volume":"2023","author":"UA Bhatti","year":"2023","unstructured":"Bhatti UA, Tang H, Wu G, Marjan S, Hussain A (2023) Deep learning with graph convolutional networks: an overview and latest applications in computational intelligence. Int J Intell Syst 2023:1\u201328","journal-title":"Int J Intell Syst"},{"key":"600_CR30","doi-asserted-by":"publisher","first-page":"104310","DOI":"10.1016\/j.imavis.2021.104310","volume":"116","author":"S Anjomshoae","year":"2021","unstructured":"Anjomshoae S, Omeiza D, Jiang L (2021) Context-based image explanations for deep neural networks. Image Vision Comput. 116:104310. https:\/\/doi.org\/10.1016\/j.imavis.2021.104310. (ISSN 0262-8856)","journal-title":"Image Vision Comput."},{"key":"600_CR31","doi-asserted-by":"publisher","first-page":"120496","DOI":"10.1016\/j.eswa.2023.120496","volume":"229","author":"U Bhatti","year":"2023","unstructured":"Bhatti U, Mengxing H, Neira-Molin H, Marjan S, Baryalai M, Hao T, Wu G, Bazai S (2023) MFFCG \u2013 multi feature fusion for hyperspectral image classification using graph attention network. Expert Syst Appl 229:120496. https:\/\/doi.org\/10.1016\/j.eswa.2023.120496","journal-title":"Expert Syst Appl"},{"issue":"Part A","key":"600_CR32","doi-asserted-by":"publisher","first-page":"121282","DOI":"10.1016\/j.eswa.2023.121282","volume":"237","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Chen J, Ma X, Wang G, Bhatti UA, Huang M (2024) Interactive medical image annotation using improved Attention U-net with compound geodesic distance. Expert Syst Appl. 237(Part A):121282. https:\/\/doi.org\/10.1016\/j.eswa.2023.121282. (ISSN 0957\u20134174)","journal-title":"Expert Syst Appl."},{"key":"600_CR33","doi-asserted-by":"publisher","first-page":"207","DOI":"10.3390\/jimaging9100207","volume":"9","author":"J Valente","year":"2023","unstructured":"Valente J, Ant\u00f3nio J, Mora C, Jardim S (2023) Developments in image processing using deep learning and reinforcement learning. J Imaging 9:207. https:\/\/doi.org\/10.3390\/jimaging9100207","journal-title":"J Imaging"},{"issue":"9","key":"600_CR34","doi-asserted-by":"publisher","first-page":"101793","DOI":"10.1016\/j.jksuci.2023.101793","volume":"35","author":"AH Nizamani","year":"2023","unstructured":"Nizamani AH, Chen Z, Nizamani AA, Aslam BU (2023) Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data. J King Saud Univ Comput Inform Sci. 35(9):101793. https:\/\/doi.org\/10.1016\/j.jksuci.2023.101793. (ISSN 1319-1578)","journal-title":"J King Saud Univ Comput Inform Sci."},{"key":"600_CR35","doi-asserted-by":"publisher","first-page":"100216","DOI":"10.1016\/j.health.2023.100216","volume":"4","author":"PK Mall","year":"2023","unstructured":"Mall PK, Singh PK, Srivastav S, Narayan V, Paprzycki M, Jaworska J, Ganzha M (2023) A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities. Healthcare Analyt. 4:100216. https:\/\/doi.org\/10.1016\/j.health.2023.100216. (ISSN 2772-4425)","journal-title":"Healthcare Analyt."},{"key":"600_CR36","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.neucom.2021.02.069","volume":"443","author":"X Li","year":"2021","unstructured":"Li X, Cui M, Li J, Bai R, Lu Z, Aickelin U (2021) A hybrid medical text classification framework: Integrating attentive rule construction and neural network. Neurocomputing. 443:345\u2013355. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.069. (ISSN 0925-2312)","journal-title":"Neurocomputing."},{"issue":"5","key":"600_CR37","doi-asserted-by":"publisher","first-page":"e13114","DOI":"10.1111\/exsy.13114","volume":"40","author":"K Naithani","year":"2023","unstructured":"Naithani K, Raiwani YP (2023) Realization of natural language processing and machine learning approaches for text-based sentiment analysis. Expert Syst 40(5):e13114. https:\/\/doi.org\/10.1111\/exsy.13114","journal-title":"Expert Syst"},{"issue":"1","key":"600_CR38","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1016\/j.jksuci.2018.09.022","volume":"34","author":"ANM JayaLakshmi","year":"2022","unstructured":"JayaLakshmi ANM, Kishore KV (2022) Performance evaluation of DNN with other machine learning techniques in a cluster using Apache Spark and MLlib. J King Saud Univ- Comput Inform Sci. 34(1):1311\u20131319. https:\/\/doi.org\/10.1016\/j.jksuci.2018.09.022. (ISSN 1319-1578)","journal-title":"J King Saud Univ- Comput Inform Sci."},{"issue":"8","key":"600_CR39","doi-asserted-by":"publisher","first-page":"e1100","DOI":"10.7717\/peerj-cs.1100","volume":"20","author":"A Yenkikar","year":"2022","unstructured":"Yenkikar A, Babu CN, Hemanth DJ (2022) Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous classifier ensemble. PeerJ Comput Sci 20(8):e1100. https:\/\/doi.org\/10.7717\/peerj-cs.1100","journal-title":"PeerJ Comput Sci"},{"key":"600_CR40","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s44163-023-00089-x","volume":"3","author":"M Elahi","year":"2023","unstructured":"Elahi M, Afolaranmi SO, Martinez Lastra JL et al (2023) A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment. Discov Artif Intell 3:43. https:\/\/doi.org\/10.1007\/s44163-023-00089-x","journal-title":"Discov Artif Intell"},{"key":"600_CR41","doi-asserted-by":"publisher","first-page":"205","DOI":"10.2478\/ausi-2020-0012","volume":"12","author":"D Androcec","year":"2020","unstructured":"Androcec D (2020) Machine learning methods for toxic comment classification: a systematic review. Acta Universitatis Sapientiae, Informatica 12:205\u2013216. https:\/\/doi.org\/10.2478\/ausi-2020-0012","journal-title":"Acta Universitatis Sapientiae, Informatica"},{"key":"600_CR42","doi-asserted-by":"publisher","unstructured":"Rahul, Kajla H, Jatin H, Gajanand S (2020) Classification of online toxic comments using machine learning algorithms. 1119\u20131123. https:\/\/doi.org\/10.1109\/ICICCS48265.2020.9120939.","DOI":"10.1109\/ICICCS48265.2020.9120939"},{"key":"600_CR43","doi-asserted-by":"publisher","first-page":"3785","DOI":"10.3390\/electronics12183785","volume":"12","author":"A \u010cepulionyt\u0117","year":"2023","unstructured":"\u010cepulionyt\u0117 A, Toldinas J, Lozinskis B (2023) A multilayered preprocessing approach for recognition and classification of malicious social network messages. Electronics 12:3785. https:\/\/doi.org\/10.3390\/electronics12183785","journal-title":"Electronics"},{"issue":"5","key":"600_CR44","doi-asserted-by":"publisher","first-page":"e0282924","DOI":"10.1371\/journal.pone.0282924","volume":"18","author":"SJ Belfield","year":"2023","unstructured":"Belfield SJ, Cronin MTD, Enoch SJ, Firman JW (2023) Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs). PLoS ONE 18(5):e0282924. https:\/\/doi.org\/10.1371\/journal.pone.0282924","journal-title":"PLoS ONE"},{"issue":"1","key":"600_CR45","doi-asserted-by":"publisher","first-page":"17478","DOI":"10.1038\/s41598-022-22523-3","volume":"12","author":"A Abbasi","year":"2022","unstructured":"Abbasi A, Javed AR, Iqbal F, Kryvinska N, Jalil Z (2022) Deep learning for religious and continent-based toxic content detection and classification. Sci Rep 12(1):17478. https:\/\/doi.org\/10.1038\/s41598-022-22523-3","journal-title":"Sci Rep"},{"key":"600_CR46","doi-asserted-by":"publisher","first-page":"10345","DOI":"10.1007\/s10462-023-10419-1","volume":"56","author":"DS Asudani","year":"2023","unstructured":"Asudani DS, Nagwani NK, Singh P (2023) Impact of word embedding models on text analytics in deep learning environment: a review. Artif Intell Rev 56:10345\u201310425. https:\/\/doi.org\/10.1007\/s10462-023-10419-1","journal-title":"Artif Intell Rev"},{"key":"600_CR47","doi-asserted-by":"publisher","unstructured":"Danilo D, Recupero R, Diego, Harald S (2021) An assessment of deep learning models and word embeddings for toxicity detection within online textual comments. Electronics. 10. https:\/\/doi.org\/10.3390\/electronics10070779.","DOI":"10.3390\/electronics10070779"},{"key":"600_CR48","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.neucom.2021.02.023","volume":"441","author":"J Ashok Kumar","year":"2021","unstructured":"Ashok Kumar J, Abirami S, Trueman TE, Cambria E (2021) Comment toxicity detection via a multichannel convolutional bidirectional gated recurrent unit. Neurocomputing. 441:272\u2013278. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.023. (ISSN 0925-2312)","journal-title":"Neurocomputing."},{"key":"600_CR49","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\u00fd 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 10:8631. https:\/\/doi.org\/10.3390\/app10238631","journal-title":"Appl Sci"},{"key":"600_CR50","doi-asserted-by":"publisher","first-page":"126232","DOI":"10.1016\/j.neucom.2023.126232","volume":"546","author":"MdS Jahan","year":"2023","unstructured":"Jahan MdS, Oussalah M (2023) A systematic review of hate speech automatic detection using natural language processing. Neurocomputing. 546:126232. https:\/\/doi.org\/10.1016\/j.neucom.2023.126232. (ISSN 0925-2312)","journal-title":"Neurocomputing."},{"key":"600_CR51","doi-asserted-by":"publisher","unstructured":"Mehendale N, Shah K, Phadtare C, Rajpara K. Cyber bullying detection for Hindi-English language using machine learning (May 21, 2022). Available at SSRN: https:\/\/ssrn.com\/abstract=4116143 Or https:\/\/doi.org\/10.2139\/ssrn.4116143","DOI":"10.2139\/ssrn.4116143"},{"key":"600_CR52","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.3390\/electronics10101143","volume":"10","author":"M Alruily","year":"2021","unstructured":"Alruily M (2021) Classification of Arabic tweets: a review. Electronics 10:1143. https:\/\/doi.org\/10.3390\/electronics10101143","journal-title":"Electronics"}],"container-title":["Journal of Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00600-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13677-024-00600-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13677-024-00600-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T23:03:11Z","timestamp":1707260591000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofcloudcomputing.springeropen.com\/articles\/10.1186\/s13677-024-00600-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,6]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["600"],"URL":"https:\/\/doi.org\/10.1186\/s13677-024-00600-4","relation":{},"ISSN":["2192-113X"],"issn-type":[{"value":"2192-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,6]]},"assertion":[{"value":"20 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"33"}}