Statistics of training and testing corpus.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"8502",leadTitle:null,fullTitle:"Plant Science - Structure, Anatomy and Physiology in Plants Cultured in Vivo and in Vitro",title:"Plant Science",subtitle:"Structure, Anatomy and Physiology in Plants Cultured in Vivo and in Vitro",reviewType:"peer-reviewed",abstract:"Over seven chapters, this book helps readers to integrate knowledge of plant anatomy, physiology, and morphogenesis as well as consider the conditions of the different environments to which plants are exposed. It highlights the importance of knowledge of the anatomy of plant tissues for different applications. In addition to the variety of physiological studies presented here, the book also emphasizes anatomical studies in botanical quality control of medicinal herbs with human health benefits. It is reflected in this book that studies on plant structure have greatly benefited from the new approaches and techniques available today.",isbn:"978-1-78984-747-5",printIsbn:"978-1-78984-746-8",pdfIsbn:"978-1-78985-562-3",doi:"10.5772/intechopen.78878",price:119,priceEur:129,priceUsd:155,slug:"plant-science-structure-anatomy-and-physiology-in-plants-cultured-in-vivo-and-in-vitro",numberOfPages:120,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"13ca4ffde7bd7a7ce518358e1ac56c6e",bookSignature:"Ana Gonzalez, María Rodriguez and Nihal Gören Sağlam",publishedDate:"May 13th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/8502.jpg",numberOfDownloads:8535,numberOfWosCitations:3,numberOfCrossrefCitations:7,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:16,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:26,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 13th 2018",dateEndSecondStepPublish:"March 12th 2019",dateEndThirdStepPublish:"May 11th 2019",dateEndFourthStepPublish:"July 30th 2019",dateEndFifthStepPublish:"September 28th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"281854",title:"Dr.",name:"Ana Maria",middleName:null,surname:"Gonzalez",slug:"ana-maria-gonzalez",fullName:"Ana Maria Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/281854/images/system/281854.png",biography:"Ana Maria Gonzalez is the director of the Northeast Botanical Institute (IBONE), where she is a National Scientific and Technical Research Council (CONICET) researcher. She is also in charge of the IBONE's Plant Anatomy Area. Her teaching career includes being a Professor of Vascular Plant Morphology, Faculty of Agriculture, Universidad Nacional del Nordeste, Corrientes, Argentina, and Professor of Biology, Universidad Nacional del Chaco Austral, Saenz Peña, Chaco, Argentina. She is a postgraduate lecturer in master\\'s and doctoral courses in the Faculty of Agricultural Sciences, where she teaches numerous courses on anatomy, histological techniques, and bioimaging. Dr. Gonzalez has published 88 articles and more than 160 abstracts presented at national and international congresses.",institutionString:null,position:null,outsideEditionCount:null,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"National University of the Northeast",institutionURL:null,country:{name:"Argentina"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"117985",title:"Dr.",name:"María",middleName:"Victoria",surname:"Rodriguez",slug:"maria-rodriguez",fullName:"María Rodriguez",profilePictureURL:"https://mts.intechopen.com/storage/users/117985/images/system/117985.jpg",biography:"María Victoria Rodriguez currently works at the Facultad de Ciencias Bioquímicas y Farmaceúticas (FBIOYF), Rosario National University. She is an assistant researcher at the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) of Argentina. She is an adjunct professor of Pharmacobotany for Pharmaceuticals students and Cell Differentiation and Structure of Vascular Plants for bachelor students in Biotechnology. Dr. Rodriguez has presented more than sixty works at scientific-technological events and has twenty-eight publications on topics related to plant anatomy, pest biocontrol, bioinformatics, and botany (medicinal plants). Dr. Rodriguez has twenty-five postgraduate courses and/or trainings on the subjects of her specialization. She is a reviewer of international journals and book chapters related to plant anatomy, medicinal plants, and biocontrol.",institutionString:"Universidad Nacional de Rosario",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National University of Rosario",institutionURL:null,country:{name:"Argentina"}}},coeditorTwo:{id:"235232",title:"Dr.",name:"Nihal",middleName:null,surname:"Gören Sağlam",slug:"nihal-goren-saglam",fullName:"Nihal Gören Sağlam",profilePictureURL:"https://mts.intechopen.com/storage/users/206317/images/system/206317.jfif",biography:"Nihal Gören Sağlam has obtained her Bsc degree (1995-2000), Msc degree (2001-2004) and PhD (2004-2009) at Istanbul University Department of Biology (Plant Physiology). From 2002 till 2009 she has been Research Assistant at Istanbul University, Department of Botany. From 2010 till 2011 she was a Post-Doctoral researcher (Warwick University, School of Life Sciences, UK). From 2009 she has been Research Assistant Dr. at Istanbul University Department of Botany. Her research experience/interests are: Plant Physiology, Senescence hormon interactions, Stress senescence interactions, Microarray, gene expression, Protein-DNA interactions (Yeast-1-Hybrid), Confocal microscopy\r\n\r\nShe has had following International Training:\r\n1. Post-Doctoral Researcher (2010-2011)– School of Life Sciences, Warwick University, UK, (Tubitak Grant)\r\n2.Academic Visitor (2008, 6 months)- Warwick HRI, UK\r\n3.“Molecular Aspects of Salt and Drought Tolerance in Crops (Plants)” 2007, University of Cairo, Giza, Egypt (ICGEB Grant)\r\n4. Academic Visitor (2006, 3 months)- Warwick HRI, UK",institutionString:"Istanbul University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Istanbul University",institutionURL:null,country:{name:"Turkey"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"41",title:"Plant Biology",slug:"agricultural-and-biological-sciences-plant-biology"}],chapters:[{id:"68924",title:"Phloem: Cell Types, Structure, and Commercial Uses",doi:"10.5772/intechopen.88162",slug:"phloem-cell-types-structure-and-commercial-uses",totalDownloads:1603,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:1,abstract:"Phloem is the vascular tissue in charge of transport and distribution of the organic nutrients. The phloem is also a pathway to signaling molecules and has a structural function in the plant body. It is typically composed of three cell types: sieve elements, parenchyma, and sclerenchyma. The sieve elements have the main function of transport and typically have lost their nuclei and other organelles in the course of their specialization. Hence, the sieve elements rely on specialized neighboring parenchyma cells to sustain all of their physiological function and activities. All cell types of the phloem may vary morphologically and in their distribution in the tissue, and this diversity is taxonomic and functionally informative. The phloem can be of primary or secondary origin, being derived from either procambium or cambium, respectively. Some vascular plant lineages have exclusive primary phloem, such as the lycophytes, ferns, and the monocotyledons, and the sieve elements will be long living in these taxa. In plants with secondary growth, the secondary phloem is formed, and typically the primary phloem collapses. Because new secondary phloem is constantly formed, the longevity of sieve elements in the secondary plant body is much more reduced. In this chapter, the structure of the phloem and its cell types are described in detail and also some of the known commercial uses of this tissue.",signatures:"Marcelo R. Pace",downloadPdfUrl:"/chapter/pdf-download/68924",previewPdfUrl:"/chapter/pdf-preview/68924",authors:[{id:"286705",title:"Dr.",name:"Marcelo",surname:"Pace",slug:"marcelo-pace",fullName:"Marcelo Pace"}],corrections:null},{id:"65075",title:"Phytohormone-Mediated Homeostasis of Root System Architecture",doi:"10.5772/intechopen.82866",slug:"phytohormone-mediated-homeostasis-of-root-system-architecture",totalDownloads:628,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Unlike animals, most of the plants are sessile. This may be a reason why they developed the powerful ability of organ generation throughout their lifetime, which is distinct from the animals, whose generation potential is restricted in a certain period during development. Half part of the plant body, the root system, is hidden under the ground, where there is a competition of resources, for example, water and nutrients or biotic stresses and abiotic stresses surrounding the root system. With its strong regeneration ability, the architecture of the root system is shaped by all of these environmental cues together with the internal developmental signals. In this process, phytohormones work as the regulatory molecules mediating the internal and external developmental signals, thus controlling the morphology and function of the root system architecture. This chapter introduces the development of root system regulated by various phytohormones, like auxin, cytokinin, etc.",signatures:"Dongyang Xu and Masaaki K. Watahiki",downloadPdfUrl:"/chapter/pdf-download/65075",previewPdfUrl:"/chapter/pdf-preview/65075",authors:[{id:"279724",title:"Associate Prof.",name:"Masaaki",surname:"Watahiki",slug:"masaaki-watahiki",fullName:"Masaaki Watahiki"},{id:"279725",title:"Dr.",name:"Dongyang",surname:"Xu",slug:"dongyang-xu",fullName:"Dongyang Xu"}],corrections:null},{id:"65082",title:"Cross Talk among Phytohormone Signal and Carbohydrate Metabolism Involving Regenerable Calli Induction under Osmotic Treatment",doi:"10.5772/intechopen.83556",slug:"cross-talk-among-phytohormone-signal-and-carbohydrate-metabolism-involving-regenerable-calli-inducti",totalDownloads:581,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Nonregenerable calli (NRC) derived from immature seeds of japonica rice were inoculated on MS medium containing 10 μM 2,4-D (MSD10). They turned to highly regenerable calli (HRC) when sorbitol was supplemented into the medium. Meanwhile, high levels of endogenous IAA and ABA were accumulated in HRC. Exogenous IAA precursor and ABA in MSD10 have the same effect to enhance regeneration ability. However, there are only partial effects if IAA precursor or ABA was supplemented, respectively. The regeneration ability is prominently decreased from 75% to 25% while an auxin transport inhibitor, 2,3,5-triiodobenzoic acid, was included in the medium. It suggested that endogenous auxin signal and ABA may involve in the induction of HRC. Furthermore, it showed higher contents of glucose, sucrose, and starch and higher expression levels of wall-bound invertase 1, sucrose transporter 1 (OsSUT1), and OsSUT2 genes in HRC than in NRC. The expression levels of PIN-formed 1 and LEA1 were also consistent with the trend of carbohydrate metabolisms. We thus concluded a flowchart for HRC induction by osmotic stress. According to the hypothesis, osmotic stress may regulate endogenous levels of auxin interacting with ABA, then affect carbohydrate metabolism to trigger callus initiation and further shoot regeneration in rice.",signatures:"Hsiang-Ting Lee and Wen-Lii Huang",downloadPdfUrl:"/chapter/pdf-download/65082",previewPdfUrl:"/chapter/pdf-preview/65082",authors:[{id:"275518",title:"Prof.",name:"Wen-Lii",surname:"Huang",slug:"wen-lii-huang",fullName:"Wen-Lii Huang"},{id:"287882",title:"Mr.",name:"Hsiang-Ting",surname:"Lee",slug:"hsiang-ting-lee",fullName:"Hsiang-Ting Lee"}],corrections:null},{id:"65965",title:"Jasmonates: An Emerging Approach in Biotic and Abiotic Stress Tolerance",doi:"10.5772/intechopen.84608",slug:"jasmonates-an-emerging-approach-in-biotic-and-abiotic-stress-tolerance",totalDownloads:545,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Plant hormones acts as key signaling compounds in plant stress responses and development under biotic and abiotic stresses. The potential roles of phytohormones had been considered so far and copious investigation is going on in finding the impending role of phytohormones in abiotic and biotic stresses. In the list of known classical plant hormones, Jasmonates, [jasmonic acid and its methyl ester (methyl Jasmonates)] have been recently added and shown as potential tool in enhancing tolerance of plants against various physiological processes. These are oxidized lipids (oxylipins) mainly derived from α-linolenic acids (α-LAs), play an active role in senescence through signaling, flower nectar secretion, Gprotein signaling, physiological activities and development in plants. Exogenous application of jasmonates on different plant parts have proved effective in improving plant abiotic stress tolerance particularly salinity, drought, and temperature (low/high) conditions and also in biotic stress tolerance like pathogen attack or wounding by production of defensive secondary metabolites, through the stimulation of phenyl-propanoid metabolism resulted from accumulation of JA in plant cells or tissues. Based on this prolific role of Jasmonates and its derivatives in different fields of biological sciences these phytohormones have opened new vistas and increasing interest in future Agricultural, Biotechnological and Physiological researches.",signatures:"Shivani Lalotra, Akhouri Hemantaranjan, Bhudeo Rana Yashu, Rupanshee Srivastava and Sandeep Kumar",downloadPdfUrl:"/chapter/pdf-download/65965",previewPdfUrl:"/chapter/pdf-preview/65965",authors:[{id:"270428",title:"Ms.",name:"Shivani",surname:"Lalotra",slug:"shivani-lalotra",fullName:"Shivani Lalotra"},{id:"291718",title:"Prof.",name:"A.",surname:"Hemantaranjan",slug:"a.-hemantaranjan",fullName:"A. Hemantaranjan"},{id:"291719",title:"Mr.",name:"Bhudeo",surname:"Rana Yashu",slug:"bhudeo-rana-yashu",fullName:"Bhudeo Rana Yashu"},{id:"291722",title:"Ms.",name:"Rupanshee",surname:"Srivastava",slug:"rupanshee-srivastava",fullName:"Rupanshee Srivastava"},{id:"291724",title:"Mr.",name:"Sandeep",surname:"Kumar",slug:"sandeep-kumar",fullName:"Sandeep Kumar"}],corrections:null},{id:"70467",title:"A Regulatory Circuit Integrating Stress-Induced with Natural Leaf Senescence",doi:"10.5772/intechopen.89498",slug:"a-regulatory-circuit-integrating-stress-induced-with-natural-leaf-senescence",totalDownloads:729,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Any condition that disrupts the ER homeostasis activates a cytoprotective signaling cascade, designated as the unfolded protein response (UPR), which is transduced in plant cells by a bipartite signaling module. Activation of IRE1/bZIP60 and bZIP28/bZIP17, which represent the bipartite signaling arms and serve as ER stress sensors and transducers, results in the upregulation of ER protein processing machinery-related genes to recover from stress. However, if the ER stress persists and the cell is unable to restore ER homeostasis, programmed cell death signaling pathways are activated for survival. Here, we describe an ER stress-induced plant-specific cell death program, which is a shared response to multiple stress signals. This signaling pathway was first identified through genome-wide expression profile of differentially expressed genes in response to combined ER stress and osmotic stress. Among them, the development and cell death domain-containing N-rich proteins (DCD/NRPs), NRP-A and NRP-B, and the transcriptional factor GmNAC81 were selected as mediators of cell death in plants. These genes were used as targets to identify additional components of the cell death pathway, which is described here as a regulatory circuit that integrates a stress-induced cell death program with leaf senescence via the NRP-A/NRP-B/GmNAC81:GmNAC30/VPE signaling module.",signatures:"Otto Teixeira Fraga, Bruno Paes de Melo, Luiz Fernando de Camargos, Debora Pellanda Fagundes, Celio Cabral Oliveira, Eduardo Bassi Simoni, Pedro Augusto Braga dos Reis and Elizabeth Pacheco Batista Fontes",downloadPdfUrl:"/chapter/pdf-download/70467",previewPdfUrl:"/chapter/pdf-preview/70467",authors:[{id:"21426",title:"Prof.",name:"Elizabeth",surname:"Fontes",slug:"elizabeth-fontes",fullName:"Elizabeth Fontes"}],corrections:null},{id:"71539",title:"Leaf Senescence in Wheat: A Drought Tolerance Measure",doi:"10.5772/intechopen.89500",slug:"leaf-senescence-in-wheat-a-drought-tolerance-measure",totalDownloads:507,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The present study was conducted on the experimental site of INRAA, unit research of Setif. A set of 10 genotypes of durum wheat (Triticum durum Desf.) planted during four cropping seasons (2009–2013). The objectives of this study are to evaluate the performance of some durum wheat genotypes and tested the efficiency of using senescence parameters in screening under semi-arid conditions. The analysis of variance demonstrates significant effects of genotypes and years on the grain yield and senescence parameters. Based on the means comparison, the values of total mean grain yield (2009–2013) varied from 37.84 q/ha for Oued Zenati to 44.7 q/ha for Altar84 with general mean of 42.71 q/ha. The mean rankings based on the mean grain yield demonstrate that the genotypes Mexicali75, Hoggar, and Sooty have the best ranking with highest grain yield. The mean values over years of Sa% varied between 47.91% for the genotype Oued Zenati and 59.45% for Waha. The genotypes with highest values for the parameter mid-senescence (Σ50s) are the most tolerant and adapted genotypes.",signatures:"Hafsi Miloud and Guendouz Ali",downloadPdfUrl:"/chapter/pdf-download/71539",previewPdfUrl:"/chapter/pdf-preview/71539",authors:[{id:"99819",title:"Dr.",name:"Ali",surname:"Guendouz",slug:"ali-guendouz",fullName:"Ali Guendouz"},{id:"307654",title:"Prof.",name:"Miloud",surname:"Hafsi",slug:"miloud-hafsi",fullName:"Miloud Hafsi"}],corrections:null},{id:"66996",title:"Ethiopian Common Medicinal Plants: Their Parts and Uses in Traditional Medicine - Ecology and Quality Control",doi:"10.5772/intechopen.86202",slug:"ethiopian-common-medicinal-plants-their-parts-and-uses-in-traditional-medicine-ecology-and-quality-c",totalDownloads:3944,totalCrossrefCites:3,totalDimensionsCites:8,hasAltmetrics:1,abstract:"The main purpose of this review is to document medicinal plants used for traditional treatments with their parts, use, ecology, and quality control. Accordingly, 80 medicinal plant species were reviewed; leaves and roots are the main parts of the plants used for preparation of traditional medicines. The local practitioners provided various traditional medications to their patients’ diseases such as stomachaches, asthma, dysentery, malaria, evil eyes, cancer, skin diseases, and headaches. The uses of medicinal plants for human and animal treatments are practiced from time immemorial. Stream/riverbanks, cultivated lands, disturbed sites, bushlands, forested areas and their margins, woodlands, grasslands, and home gardens are major habitats of medicinal plants. Generally, medicinal plants used for traditional medicine play a significant role in the healthcare of the majority of the people in Ethiopia. The major threats to medicinal plants are habitat destruction, urbanization, agricultural expansion, investment, road construction, and deforestation. Because of these, medicinal plants are being declined and lost with their habitats. Community- and research-based conservation mechanisms could be an appropriate approach for mitigating the problems pertinent to the loss of medicinal plants and their habitats and for documenting medicinal plants. Chromatography; electrophoretic, macroscopic, and microscopic techniques; and pharmaceutical practice are mainly used for quality control of herbal medicines.",signatures:"Admasu Moges and Yohannes Moges",downloadPdfUrl:"/chapter/pdf-download/66996",previewPdfUrl:"/chapter/pdf-preview/66996",authors:[{id:"249746",title:"Ph.D.",name:"Admasu",surname:"Moges",slug:"admasu-moges",fullName:"Admasu Moges"},{id:"297761",title:"MSc.",name:"Yohannes",surname:"Moges",slug:"yohannes-moges",fullName:"Yohannes Moges"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"10772",title:"Parasitic Plants",subtitle:null,isOpenForSubmission:!1,hash:"31abd439b5674c91d18ad77dbc52500f",slug:"parasitic-plants",bookSignature:"Ana Maria Gonzalez and Héctor Arnaldo Sato",coverURL:"https://cdn.intechopen.com/books/images_new/10772.jpg",editedByType:"Edited by",editors:[{id:"281854",title:"Dr.",name:"Ana Maria",surname:"Gonzalez",slug:"ana-maria-gonzalez",fullName:"Ana Maria Gonzalez"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6277",title:"Physical Methods for Stimulation of Plant and Mushroom Development",subtitle:null,isOpenForSubmission:!1,hash:"33dff71e3489403e273057ae36bd0dbd",slug:"physical-methods-for-stimulation-of-plant-and-mushroom-development",bookSignature:"Mohamed El-Esawi",coverURL:"https://cdn.intechopen.com/books/images_new/6277.jpg",editedByType:"Edited by",editors:[{id:"191770",title:"Dr.",name:"Mohamed A.",surname:"El-Esawi",slug:"mohamed-a.-el-esawi",fullName:"Mohamed A. 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The proposed methodology is a dual compound method. It utilizes the arrangement of “bullying” or “non-bullying” class and also it utilizes link analysis to locate the most dynamic users as predators and victims. Each step can be explained in detail as follows. The feature selection is an essential phase in denoting data within component space to the categorizers. Mostly the data available from social network are noisy. So, there is a need to apply pre-processing techniques in order to obtain the research data with better quality followed by successive systematic steps; Moreover, sparsity in feature space increases with the count of documents. Nevertheless, the following types of features generated through the B-LDA topic model and weighted B-TF-IDF scheme. In the initial step, semantic highlights are related for locating harassing, abusive and offending posts. In pestering discovery the presence of pronouns in the nuisance post was represented. Essentially in this work, three sorts of capabilities are utilized. They are depicted as follows: (i) all second individual pronouns “you,” “yourself,” and so forth are considered one term; (ii) all other outstanding pronouns “he,” “she,” and so on., are viewed together as another element; (iii) foul words such as “fr**k,” “shit,” “moronic,” and so forth., which make the post merciless are assembled in another arrangement of highlights. The new harassing words lexicon was made in view of the accompanying essential sites like noswearing.com
Rahat et al. [1] presented a multi-stage cyber bullying detection results that radically decreases the classification period and give warning signals. The system is greatly scalable without forfeiting precision and highly approachable in raising signals. It also contained an active priority scheduler and a rising classification procedure by applying Vine data sets. The performance outcomes demonstrate that the model enhances the scalability of digital harassing discovery contrasted to non-priority model and also explained that the system could fully check Vine-scale networks. The results depict that this digital harassing detection is considerably more measurable and receptive than the present modern technology. Zhong et al. [2] proposed an investigation to find out cyberbullying in Instagram utilizing the improvement of early-warning methods to detect offensive images.
The research operated by obtaining a huge volume of pictures in the Instagram image sharing process along with messages. They studied new features of the topics acquired from the picture portrayal and trained using neural network technology, added with images and texts. The results got the potential objectives for harassing on the characterization of texts and images. Sherly [3] proposed research using supervised feature selection to select the characteristics from the tweets by the ranking method. Then extreme learning machine (ELM) classifier is applied to execute the cyberbullying detection and enhance the precision and reduce the performance period. The performance investigation of the SFS-ELM model observed that the accuracy is improved by 13% and executed using MATLAB. Micheline et al. [4] accomplished a study by using an unsupervised methodology to identify harassing messages in social networks, utilizing Growing Hierarchical Self Organizing Map. The research contains various features to find semantic and syntactic interactions of regular cyber tormentors. They conducted various trials on FormSpring, Twitter and YouTube networks by collecting real time datasets. The outcomes of the research show that the model attains the significant performance and also promotes permanent watching applications to alleviate the huge issues of harassing. Suchini et al. [5] applied a text classification model to categorize the text as insulting or not. Feature selection is performed using Chi-square test and then classification algorithms are utilized for segregating comments as insulting or non-insulting words. Various algorithms like SVM, Naive Bayes, Logistic Regression, Random Forest are applied and out of all algorithms, SVM gave better results.
Krishna et al. [6] proposed a model deployed for detecting abusive text and images in the social network. This automated system could find the offensive content in messages using the combination of a bag of visual word method, local binary pattern and SVM classifier. The offensive detection in the text messages are executed by a bag of word method with Naïve Bayes classifier and then the Boolean system is applied to classify the content. Javier et al. [7] have displayed automatic strategies for identifying erotic plundering in Chat rooms. They have effectively demonstrated that a learning-based technique is an attainable method to approach this issue and have proposed novel sets of highlights to determine the classification of chat partakers as exploiters or non-exploiters. They exhibited that the arrangements of features used and the comparative weighting of the disarrangement expenditures in the SVMs are two fundamental factors that ought to be considered to upgrade execution.
Huang et al. [8] proposed normal text investigation using social network characteristics to classify harassing in Twitter and also considered the social connection between clients would betterment outcome for classification. Zhao et al. [9] applied a collection of features known as EBoW (Natural Language Processing method), containing a bag of words structure connected with Latent Semantic analysis and word embeddings by computing word vectors. They also used SVM to classify the data collection in Twitter which contains keywords like bully or bullying.
Chen et al. [10] researched existing content mining techniques in recognizing harassing texts for ensuring adolescent online safety. In particular, they proposed the Lexical Syntactical Feature (LSF) way to deal with hostile contents on the internet and further foresee a client’s potentiality to convey hostile contents. Their investigation has many commitments. To begin with, they essentially conceptualize the idea of online hostile contents and further recognize the contribution of pejoratives/obscenities and profanities in deciding offensive substance, and present hand creating syntactic standards in finding verbally abusing provocation. Second, they enhanced customary Machine-Learning strategies by not just utilizing lexical features to identify hostile dialect, yet in addition style feature, structure features, and content-specific features to better foresee a client’s possibility to convey hostile content in social media. Investigation result demonstrates that the LSF Sentence offensiveness forecast and client offensiveness estimate algorithm beat, customary learning-based methodologies in turns of precision, recall, and F-score. The LSF endures casual and incorrect spelling contents and it can possibly adjust to any forms of English written word styles.
LDA is an outstanding method of Bayesian multinomial mixture model in text analysis based on its ability to assemble, elucidate and semantically cogent topics. It uses the Dirichlet distribution to model the distribution of the topics for each and every one document. In LDA, each word is measured from a multinomial distribution over words particular to this topic. Since LDA is extremely modular and hierarchical, consequently, it can simply be broadened. Various expansions to basic LDA model have been recommended to incorporate document metadata. The easy process of integrating the metadata in generative topic models is to create both the words and the metadata concurrently specified unseen topic variables. The Author-Topic (AT) model resembles Bayesian network, in which every authors’ attractions are modeled with a combination of topics [11]. In this model an arrangement of authors, advertisements are watched and looked over from different documents depends on their topics. To create each word, an author x is picked at identical from this set, then a topic z is chosen from a topic distribution θx that is particular to the author, and after that, a word w is created by testing from a topic-particular multinomial distribution ϕz.
The proposed Bully-LDA (B-LDA) model is used for identifying bullying words used by authors. This model captures bullying-topics which are used in social networks like Twitter. In Twitter, one person sends tweets to many followers. Here in this model, the sender is considered as Predator, when he/she sends bullying words to their followers. The followers are represented as Victims. The B-LDA model is a generative process model and also encapsulates topics and the communication networks of Predators and Victims by conditioning the multinomial distribution over bullying topics distinctly on both the Predator and a Victim of a bullying message. Unlike other models, B-LDA model takes into concern both predator and victims distinctly. The motive of the predator is also considered in addition to this representation. Each motive is associated with a set of topics, and these topics may overlap. For example, the categories of motive can be racist, sexual, outrage, irrelevant. The sexual motive of predator contains the topics of crude, implicit/ambiguous language or an indecent proposal. The Racist category contains more abusive matters such as homophobia, extremism, slurs, etc. The outrage is a category, which specifies reactions that express contempt. The messages that do not contain any form of offensive language are considered to be irrelevant. Each predator has a multinomial distribution over motives. Thus, B-LDA model is a clustering model, in which appearances of topics are the underlying data, and sets of correlated topics are together gathered as clusters that denote motive. Predators and Victims are mapped to motive assignments, and then a topic is selected based on these motives. The intention of each and every predator has a multinomial distribution on topics, and every topic has a multinomial distribution on words. First, the motive assignments can be made separately for each word in a document. This model represents that someone can change motive during the exchange of the messages.
Author-Topic (AT) [11] model has been extended by incorporating a new set of variables like authors as Predators and Victims, the motivation of an author. In this generative process for each message, a Predator, pd and a set of Victims, vd are observed. To generate each word, a victim y is chosen at uniform from vd, and then a motive x for the Predator is chosen from multinomial motive distribution
Figure 1 is a schematic diagram of the B-LDA model.
Graphical model for B-LDA.
The generative procedure of this strategy is as follows:
for every motive m with m = 1,…..M, choose ψm ∼Dir(γ)
for each predator and victim pair (x,y) with x = 1,….,A and y = 1,….,A choose θx,y ∼ Dir (α)
for each topic t with t = 1,…..T, choose ϕt ∼ Dir(β)
for each message d
observe motive md
observe predator pd and the victims vd
for each word w in d
choose topic zdn ∼ θzd
choose word wdn ∼ ϕzdn
In this model for a particular message d, given the hyper parameters α, β, and γ, the predator pd, and set of victims vd, the connected dispersion of an author blend θ, a motive blend ψ, a topic blend ϕ, a set of Nd victims yd, and a set of Nd predator motives xd, a set of Nd topics zd and a set of Nd words wd is assigned by,
Integrating over γ, θ and ϕ and summing over yd, xd, and zd, the marginal distribution of a document is calculated as follows:
Then the product of the marginal probabilities of single documents, and the probability of a corpus is computed as,
The assumption on models in the LDA family cannot be carried out correctly. Three standard approximations have been occupied to acquire practical results: Variational methods [12], Gibbs sampling [13], and expectation propagation [14]. As Gibbs sampling is easy to implement, it has been applied here. There is a need to derive a formula to carry out the Gibbs sampling for P(zi,yi,xi|z-i,y-i,x-i), the conditional distribution of a topic and victims for w word given all other words topic and victim assignment, the motive of the predator, z-i, y-i, and x-i. In order to calculate P(z,y,x|w), the posterior distribution of topic, victim assignments and the motive of the predator given the words in the corpus.
The calculations begin with P(w|z,x), using P(w|z,x,Φ) in order to integrate out the unknown Φ distributions to obtain:
Reorganizing the product over the W word token exist in the corpus to collect words that are assigned to the same bullying topic,
where
In the same manner, P(z,y) is computed using a procedure analogous to that used for P(w|z,y). The collected terms of bullying words are assigned to the same topic and predator-victim pair and integrate out the Θ distributions corresponding to all the different predator-victim pairs, P:
where
Similarly can calculate P(z, x) using a procedure analogous to that used for P(w|z, x). Bullying words have been assigned to the same topic and the motivation of the predator can be computed as,
where
Hence the denominator cannot be calculated directly. The following equations are used to run a MCMC Gibbs sampling calculation by using the conditional distribution P(zi, yi, xi, wi|z-i, y-i, x-i, w-i).
where the victim, y is part of Predator-Victim pair,
In this chapter, the experimental results are discussed. The datasets used in these experiments are tweets from Twitter. An experiment has been conducted on tweets based on the architecture of an automatic cyber bullying detection system. Search is made in the Twitter stream for Tweets containing the strings that contain offensive words so as to particularly filter for tweets related to bullying. In total, more than 1,00,000 tweets are gathered between Jan 1st, 2015 and Jan 30th, 2016. A limit number of tweets are matching with the query. So, approximately 300 tweets are filtered per day. The statistics for training and the testing corpus is given in Table 1. Tweets were manually labeled as belonging to one of the different motives namely Sexual, Racist, Outrage, Irrelevant, and Unknown after the preprocessing. The examples of harassing comments posted on Twitter are listed below and depicted in Figure 2(a) and (b) and top bullying words which are extracted are given in Table 2 (Figures 3–5).
Date | Time | Tweets |
---|---|---|
01–13-15 | 12:16 | NefarioussNess Do not fuck with people’s hearts |
09–18-15 | 11:51 | TittyCityClay it’s always been a self respect thing. Shit like this is stupid as fuck lol |
05–13-15 | 10:11 | djkeneechi Nah kiss no one ass to stay in my life anymore im tired of that shit it’s time for me to man up |
Training corpus | Testing corpus | |
---|---|---|
Tweets | 3,18,14,716 | 97,35,537 |
Retweets | 76,20,335 | 2,87,567 |
URLs | 85,45,112 | 4,76,234 |
Usernames | 97,02,445 | 14,20,554 |
Hashtags | 79,85,956 | 3,56,778 |
Statistics of training and testing corpus.
(a) Bullying words with their probability, and (b) List of bullying words.
Word | Prob | Word | Prob | Word | Prob |
---|---|---|---|---|---|
Fuck | 0.0798 | Bitch | 0.0705 | Naked | 0.0588 |
Ass | 0.0767 | Freak | 0.0699 | Sexy | 0.0569 |
shit | 0.0752 | Fat | 0.0663 | Mood | 0.0547 |
Gay | 0.0738 | Dirty | 0.0643 | Lick | 0.0519 |
Dumb | 0.0722 | Bullshit | 0.0621 | Bed | 0.0508 |
Suck | 0.0711 | Kiss | 0.0604 | Piss | 0.0495 |
Extracted top bullying words.
Word cloud for bullying words.
Number of bullying tweets over time intervals.
Distributions of tweets per motive.
Bully-Latent Dirichlet Allocation model is an intended for pictorial representation of texts in a harassing message, given their predator and a pair of casualties. B-LDA got crucial enrichment to facilitate specification the per-bullying message topic dispersion mutually on the predator and individual victims. Every topic includes multinomial distribution on words and every Predator-Casualty pair has a distribution on topics. So, subsidiary dispersions in excess on bullying subjects accustomed exclusively on a predator, or solely on a recipient, can be computed easily. For example, corpus comprising 135 persons and 35 k bulling messages, and also on 5 months of sending and receiving messages of a predator, comprising 17 victims and 19 k messages. B-LDA turns up tremendously prominent topics, and grants support that it predicts predator’s motives. In the experiments, the hectic parameters α and β are fixed at 1 and 0.01 respectively. The number of topics T is also fixed at |T| = 5. For a 50 topic solution, Dataset from Twitter took 150 hours for 2000 iterations (5 min per iteration).
B-LDA proves the motive of the predator and track the activity of the predator with victims, using the following steps. First, the proportions of each predator contributing in each of the bullying topics are determined. Next the impacts of the predators throughout the time intervals on the bullying topics. The two users’ threshold ε and λ are empirically set to 3.2106 and 2.0457, respectively. From each of the documents, B-LDA generates 5 topics with predators associated with each. The distribution of the different bullying topics from the documents is displayed in Table 3. From the table, predator p1 has a probability of 0.0547 for bullying topic t5. There is a need to prove the bullying motive of the predator with victim using specific time intervals within bullying topics. It could be characterized as trails: A tweet message is a triplet (a, μ, т), representing a textual bullying message μ written by the predator “a” at time т. A document, denoted by d, is a sequence of bullying messages ordered by т. From this definition, time тd is associated with both message μd and predator ad.
MOTIVE = RACISM | |||||||||
---|---|---|---|---|---|---|---|---|---|
TOPIC 5 | TOPIC 10 | TOPIC 15 | TOPIC 20 | TOPIC 25 | |||||
Incorrect | 0.0271 | ColdSweat | 0.0265 | Shit | 0.0752 | Fuck | 0.0798 | Pussi | 0.0321 |
Improper | 0.0242 | Dread | 0.0254 | Bullshit | 0.0621 | Ass | 0.0767 | Dog | 0.0312 |
Indecent | 0.0231 | Fearful | 0.0235 | Piss | 0.0506 | Dumb | 0.0722 | Filthy | 0.0304 |
Ineligible | 0.0225 | Horror | 0.0223 | Aggrieve | 0.0254 | Blind | 0.0342 | Crow | 0.0294 |
Unfit | 0.0214 | Panic | 0.0212 | Tee toe | 0.0232 | Cracy | 0.0212 | Nitchie | 0.0276 |
Unsuited | 0.021 | Phobia | 0.0203 | Nose | 0.0215 | Daft | 0.0203 | Peckerwood | 0.0253 |
Room | 0.0197 | Scare | 0.0194 | Gotoofar | 0.0201 | Autism | 0.0167 | Cameljockey | 0.0238 |
Raffish | 0.0193 | Terror | 0.0187 | Rufflesb’s feathers | 0.0176 | Freak | 0.0154 | Nigger | 0.0221 |
Square peg | 0.0184 | Alarm | 0.0176 | Aggravate | 0.0154 | Gimpy | 0.0132 | Peckerwood | 0.0213 |
Unworthy | 0.0173 | Fright | 0.0169 | Burn | 0.0132 | Windowlicker | 0.0121 | Wigger | 0.0201 |
Predators: Victims | Prob | Predators: Victims | Prob | Predators: Victims | Prob | Predators: Victims | Prob | Predators: Victims | Prob |
P1: V1 | 0.0547 | P1: V1 | 0.0341 | P4: V4 | 0.0352 | P3: V5 | 0.0421 | P1: V6 | 0.0284 |
P2: V2 | 0.0367 | P2: V2 | 0.0288 | P1: V2 | 0.0254 | P2: V6 | 0.0325 | P5: V7 | 0.0257 |
P3: V3 | 0.0361 | P3: V3 | 0.0254 | P1: V3 | 0.0246 | P1: V3 | 0.0208 | P4: V5 | 0.0236 |
MOTIVE = SEXUAL | |||||||||
---|---|---|---|---|---|---|---|---|---|
TOPIC 30 | TOPIC 35 | TOPIC 40 | TOPIC 45 | TOPIC 50 | |||||
CRUDE LANGUAGE | IMPLICIT LANGUAGE | INDECENT PROPOSALS | UNREFINED LANGUAGE | SLANG WORDS | |||||
Gay | 0.0738 | Dirty | 0.0643 | Mood | 0.0547 | Bitch | 0.0705 | Pull | 0.0456 |
Suck | 0.0711 | Bed | 0.0508 | Lick | 0.0519 | Freak | 0.0699 | Bumpuglies | 0.0423 |
Naked | 0.0588 | Frequent | 0.0491 | Kiss | 0.0508 | Fat | 0.0663 | Fug | 0.0321 |
Sexy | 0.0569 | Sleep | 0.0282 | Hangnow | 0.0485 | Happyhappy | 0.0341 | Randy | 0.0307 |
Kickit | 0.0445 | Kneedeep | 0.0241 | Givebusiness | 0.0465 | Poundduck | 0.0324 | Juicy | 0.0284 |
FuckforOL’ | 0.0432 | Encounter | 0.0215 | Monkeylove | 0.0328 | Homerun | 0.0307 | Hempedup | 0.0245 |
Getdown dirty | 0.0421 | Donasty | 0.0208 | Sexytime | 0.0319 | Smack | 0.0284 | Jiffystiffy | 0.0209 |
Slap | 0.0316 | doublebag | 0.0165 | Intimacy | 0.0206 | Serve | 0.0271 | Ride | 0.0154 |
Hump | 0.0307 | Giveitup | 0.0154 | Cottage | 0.0191 | Jellosex | 0.0135 | Smush | 0.0124 |
Screw | 0.0201 | Getlucky | 0.0142 | Raunchy | 0.0147 | Score | 0.0104 | Trim | 0.0107 |
Predators: Victims | Prob | Predators: Victims | Prob | Predators: Victims | Prob | Predators: Victims | Prob | Predators: Victims | Prob |
P1: V1 | 0.0737 | P4: V4 | 0.0541 | P3: V5 | 0.0452 | P1: V 6 | 0.0595 | P3: V5 | 0.0354 |
P2: v2 | 0.0552 | P1: V2 | 0.0428 | P2: V6 | 0.0321 | P5: V7 | 0.0467 | P2: V6 | 0.0241 |
P3: V3 | 0.0324 | P1: V3 | 0.0367 | P1: V3 | 0.0276 | P4: V5 | 0.0354 | P1: V3 | 0.0211 |
MOTIVE = OUTRAGE | MOTIVE = IRRELEVANT | MOTIVE = UNKNOWN | |||||||
---|---|---|---|---|---|---|---|---|---|
TOPIC 60 | TOPIC 70 | TOPIC 90 | |||||||
Make out | 0.0267 | Outhouse | 0.0246 | ||||||
Bitterness | 0.0365 | Marquee | 0.0235 | Pant | 0.0232 | ||||
Hard | 0.0354 | Mate | 0.0223 | Pass out | 0.0214 | ||||
Storm | 0.0321 | Minor | 0.0215 | Patient | 0.0208 | ||||
Irritation | 0.0306 | Moot | 0.0209 | PC | 0.0179 | ||||
Wrath | 0.0268 | MP | 0.0201 | Period | 0.0165 | ||||
Fury | 0.0251 | MUM | 0.0189 | Plant | 0.0152 | ||||
Resent | 0.0237 | Nappy | 0.0154 | POP | 0.0143 | ||||
Rancor | 0.0209 | Natter | 0.0142 | Restroom | 0.0137 | ||||
Grudge | 0.0192 | Nick | 0.0126 | Rider | 0.0129 | ||||
Flap | 0.0163 | Nonce | 0.0118 | Sick | 0.0109 | ||||
Predators: Victims | Prob | Predators: Victims | Prob | Predators: Victims | Prob | ||||
P1: V7 | 0.0241 | P5: V2 | 0.0207 | P2: V6 | 0.0175 | ||||
P2: V4 | 0.0219 | P4: V5 | 0.0165 | P5: V8 | 0.0154 | ||||
P3: V3 | 0.0147 | P1: V3 | 0.0126 | P4: V5 | 0.0132 |
The distribution for the different bullying topics from the documents.
The predator time contributions during time interval have been evaluated by:
A predator is said to be active and his/her motive of bullying during the interval [тs, тf] for topic t if the probability of a predator participating in t, during that time period, exceeds the user-specified threshold, and
Predators activity for bullying topic 30.
The Perplexity of the model is used on test documents to estimate the execution of model and it is a customary measure for evaluating the operation of a probabilistic model. The adapted models are compared by means of perplexity on test datasets. Perplexity is extensively used in a probabilistic model for checking their quality. The perplexity of a couple of trial texts, (wd,pd) for d ϵ Dtest, is characterized as the exponential of the negative standardized predictive likelihood underneath the representation,
Better simplification functioning is designated by means of a lesser perplexity on a held-out document. The derivation of the likelihood of a collection of texts specified the predator is a uncomplicated computation in Bully-LDA model.
The term in the brackets is merely the probability for the word wm specified the pair of predators pd. The detailed results are exposed in Figure 7. These results indicate that B-LDA better generalizes performance than ATM and LDA. The improvement in generalization performance of B-LDA can be explained by its ability to better model when comparing with LDA and ATM model. If a word which has small probability in the bullying topics of training document, then it will cause an increase in perplexity. As the number of bullying topics increase, then the probabilities assigned to words get smaller in each bullying topic. Even though ATM models the roles of authors, does not show promising results and it is originally designed for the scenario where each document has multiple authors. It is clear that B-LDA achieves superior performances among all the adopted models. The perplexity of LDA, ATM, and B-LDA are closer and they decrease steadily with the increase of topics. According to human judgments, perplexity is not easy to correlate the results. So, it is necessary to compare the models using simple metrics like Precision, Recall, and F1 measure. The standard supervised classifier, i.e., Support Vector Machine (SVM), is adopted with B-LDA for classification. LibSVM was applied to the two-class classification problem using a linear kernel. Each post is an instance; positive classes contain bullying messages and negative classes contain non-bullying messages. A 10-fold cross-validation was performed in which the complete dataset was partitioned 10 times into 10 samples; in every round, nine portions were employed for exercising and the enduring section was applied for trial (Figure 8).
Comparisons of different models in terms of perplexity.
Classifier performances based on different feature reduction methods.
The functioning of the classifier was appraised on precision, recall and F-1 measure and these measures depend on the top-ranked features produced through B-LDA method against the truth set as tested on the datasets. Precision: The Aggregate number of accurately distinguished genuine harassing posts out of recovered tormenting cases. Recall: Number of effectively distinguished tormenting cases from an aggregate number of genuine harassing cases. F-1 measure: the equally weighted harmonic mean of precision and recall. Table 4 shows the classifier performance.
Method | Precision | Recall | F-Measure |
---|---|---|---|
DF + SVM | 0.8471 | 0.7770 | 0.8105 |
PCA + SVM | 0.8397 | 0.7870 | 0.8125 |
LDA + SVM | 0.8846 | 0.8554 | 0.8724 |
B-LDA + SVM | 0.9121 | 0.8901 | 0.9003 |
Classifier performances based on different feature reduction methods.
The weighted B-TFIDF method is compared with the work done in a content analysis in a web on four different datasets. The new feature selection method using weighted B-TFIDF proved that it is better than baseline. The outcomes are cataloged in Table 5 and also indicate a very high precision, recall and F-1 measure on Twitter. In Kongregate precision fell down at the top 2000 features. In most of the cases, the classifier performed almost similar, that is between 80 and 100%. On Myspace dataset recall is moderate nearing to 1. However, precision varies between 76 and 87% except at feature value 18,000 when it reaches 91%. Unlike other datasets, Slashdot performance is very low. Although recall is moderate, precision and F-1 measures decomposed while component set was low. Also, poor performance is observed at feature value 18,000. From this discussion, the performance of weighted B-TFIDF shows the best result (Figure 9).
Kongregate | Slashdot | MySpace | |||
---|---|---|---|---|---|
Baseline | Precision | 0.35 | 0.32 | 0.42 | 0.62 |
Baseline | Recall | 0.60 | 0.28 | 0.25 | 0.53 |
Baseline | F-1 measure | 0.44 | 0.30 | 0.31 | 0.57 |
Weighted TFIDF | Precision | 0.87 | 0.78 | 0.86 | 0.87 |
Weighted TFIDF | Recall | 0.97 | 0.99 | 0.98 | 0.75 |
Weighted TFIDF | F-1 measure | 0.92 | 0.87 | 0.92 | 0.81 |
Weighted B-TFIDF | Precision | 0.95 | 0.96 | 0.96 | 0.98 |
Weighted B-TFIDF | Recall | 0.93 | 0.84 | 0.93 | 0.96 |
Weighted B-TFIDF | F-1 measure | 0.94 | 0.90 | 0.95 | 0.97 |
Comparison of weighted B-TFIDF with baseline method on other datasets.
(a) Base line method, (b) weighted TFIDF method, and (c) weighted B-TFIDF method.
In order to identify cyber bullying predators and victims, there is need to determine the most active predators and the most attacked users. The most dynamic predators and victims, and look at the association of clients in a tormenting relationship as appeared in Table 6 and it demonstrates that now and again there is more than one user at a similar rank. In this manner, users with a similar rank are gathered together. So it is important to notice that predators hailed at Rank I are additionally recognized as a victim at Rank II. Additionally, Rank II predators are Rank VII victims as well (Figure 10).
Rank | I | II | III | IV | V | VI | VII | VIII |
---|---|---|---|---|---|---|---|---|
Number of users (predators) | 4 | 2 | 1 | 1 | 2 | 7 | 3 | 2 |
Number of users (victims) | 8 | 4 | 7 | 2 | 2 | 1 | 9 | 8 |
Performance of graph model: Predators and victims identification.
Predators and victims identification.
Sender | Recipient | ||||||
---|---|---|---|---|---|---|---|
U1 | U2 | U3 | U4 | U5 | …… | UN | |
U1 | 0 | 3 | 0 | 1 | 3 | …. | …. |
U2 | 1 | 0 | 0 | 0 | 0 | …. | …. |
U3 | 1 | 2 | 0 | 1 | 0 | …. | …. |
U4 | 0 | 1 | 0 | 0 | 1 | …. | …. |
U5 | 0 | 1 | 1 | 1 | 0 | …. | …. |
….. | …. | …. | …. | …. | …. | …. | …. |
UN | …. | …. | …. | …. | …. | …. | …. |
Cyber bullying matrix (W).
The major goal of a users’ communication network are considered to identify predators and casualties. Gephi [15], a graphical interface is employed to monitor a user’s link in the harassing posts in a network. Figure 11 delineates the bullying network and it represents that a group of users obtained depend upon on the tormenting messages by utilizing modularity theorem, in order to quantify the quality of segment of a system into sub-graphs or groups. Modularity is characterized as the summation of the weight of all the edges that sink inside the given subgroups less the expected part if edges were dispensed at arbitrary in a given graph.
Bullying network.
As appeared in Figure 11, nine groups or communities, delineated by various colors are formed by considering users that are thickly connected inside the group contrasted with between group by utilizing modularity algorithm. The density of post indicates the badness embedded inside the post and it is calculated for each post. The thickness of a post is computed as the aggregate count of the harassing words within the post separated by the aggregate number of the words in the post. The HITS algorithm is utilized in order to recognize the predators and related casualties and it is also helpful to calculate their scores. The objective behind the HITS strategies is that in a network, the good hub pages point to good authorized pages which are connected by the good hub pages. The search query enters through web pages to recognize potential hub and authority pages with respect to the individual scores. Likewise, this concept is used to rank predators and casualties in a communication network.
Assumption: One bullying message is considered for each user.
Predator: Person who has posted at least one bullying message.
Victim: User who has received at least one bullying message.
Objective: To identify and to rank the most dynamic user as Predator and Victim.
Presently, a ranking method using the HITS module is utilized to detect predators and casualties. A user may be a predator and a victim depends upon on the harassing messages he/she sends or receives. So, a user appointed as a predator and in addition with a casualty score. Predator and victim scores can be calculated by the following two equations.
Here, p(u) and v(u) are represented as the Predator and Victim scores respectively. u
Then there is a necessity to define the ranking methods to the predators and victims which is depicted in the network diagram in Figure 11. In order to explain a real scenario in a simple manner, only five users are selected as depicted in Figure 12 as an example and it depicts the recognition of the most dynamic predators and casualties in a bullying network. It is a weighted directed graph G = (U,A) with a set of nodes are represented as |U| and a set of arcs are represented as |A| where,
Communication paths between predator and casualty.
Each node ui ϵ U is a user involved in the bullying conversation,
Each arc (ui,uj) ϵ A, is defined as a bullying message sent from ui to uj,
The weight of arc (ui,uj), denoted as wij, is defined as a summation of in-degrees.
Predators and victims are recognized by the directed graph G with weight. The victim can be recognized with many incoming arcs and the predator can be recognized with many outgoing arcs of the respective nodes. This method is helpful to observe the most dynamic predator or a casualty.
A cyber bullying matrix(w) is constructed to discover a predator and victim depends upon their individual scores. It is depicted in Table 7. It is formulated as a square adjacency matrix (it represents the incoming degrees and outgoing degrees of each node) of the subnet with entry w, which is a square adjacency grid of the sub collection with entry wij, where,
Since each client will have a casualty as well as a predator score, scores are represented as the vectors of n*1 dimension where ith coordinate of the vector represent both the scores of the ith user, say pi and vi respectively. To calculate scores, equations p(u) and v(u) are shortened as the casualty and predator renovating matrix–vector multiplication equations. For the preliminary iteration, pi and vi are started at 1. For every client (say, i = 1 to N) predator and victim notches are as follows:
When these equations congregate at a stable value (say k), it offers the final predator and casualty vector of each user. At last, to compute the eigenvector to acquire the predator and casualty scores.
Algorithm 1 gives a general framework of identification of the top-ranked most active predators and victims. In the algorithm N is a total number of users and Top is a threshold value, which is set manually.
Algorithm 1. Predators and casualty recognition.
Input: Set of consumer engaged in the chat with harassing post, N, Top. Output: Set of Top Casualty and Top Predator |
|
The new system is achieved by two commitments. First, a Novel Statistical Application, which is established on the new Bully-LDA with the weighted B-TFIDF strategy on bullying like attributes. It also efficiently and effectively finds latent bullying features to cultivate the accomplishment of the classifier and also to reduce the feature sparsity. Secondly, a Graph Model lends a hand to pinpoint the attackers and causalities in social networks. Such a system would encompass the following function: Tweets Crawling, Tweet Preprocessing and Tokenization, Feature extraction and Frequency extraction, Text Representation Model, Text Classification, Category of Texts, Performance Evaluation, and Results.
The Twitter corpus consists of text communications by way of metadata such user ID, dispatching time, etc. Tweets Crawling is performed using many classes and techniques in order to get the information of the users’ connected data and the details of the Tweets’ which is done using Twitter’s Application programming interface called “Twitter4j-core-4.02.jar.” Tweets are shown in entirely colloquial manner, with more amount noise and variation in linguistics. For example, tweets contain a hefty quantity of novel words, interjections, repetitions, short words such as acronyms, words with missing letters, words with phonetic spelling like
The proposed framework utilizing Bully-Latent Dirichlet Allocation through Support Vector Machine has been examined with Twitter messages. This system is based on a novel concept of applying text mining techniques to tweets for detecting Bullying messages and also to identify Predators. The weighted B-TFIDF function is used to enhance the execution of classification, in which bullying-like features are measured. The overall results using Bully-LDA + SVM and weighted B-TFIDF outperformed other models. This model has numerous benefits adding more accuracy, superior noise diminution, faster speed and greater automation. The results obtained were analyzed properly using different metrics. A range of performance measures for instance accuracy, recall and F1 measures were calculated. The analysis of results plainly displays that the system yields effective results in identifying bullying messages in a successful manner.
In this research, a methodology for cyber bullying recognition of the most operative predators and casualties are done powerfully and fruitfully. This chapter presents a framework for detecting cyber bullying in Twitter using Bully-Latent Dirichlet Allocation with support vector machine. The preprocessing procedures have pertained to tweets. First Bully-LDA, a statistical topic modeling is used on a massive Twitter Corpus, with the help of weighted B-TFIDF scheme to detect offensive words in tweets. Next, a graph representation is utilized to recognize the predators and casualties in Twitter.
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