Peer Review Articles About What Causes Hate Groups

• Journal List
• PLoS One
• PMC6763199

PLoS I. 2019; 14(9): e0222194.

Mapping online hate: A scientometric assay on research trends and hotspots in research on online hate

Ahmed Waqas, Information curation, Formal analysis, Methodology, Writing – original draft,^{1, 2} Joni Salminen, Conceptualization, Supervision, Writing – original draft, Writing – review & editing,^{three, four, *} Soon-gyo Jung, Conceptualization,ⁱⁱⁱ Hind Almerekhi, Conceptualization, Writing – review & editing,⁵ and Bernard J. Jansen, Conceptualization, Projection assistants, Supervision, Writing – review & editing ³

Ahmed Waqas

ⁱ Academy of Liverpool, Liverpool, Uk

² CMH Lahore Medical College & Constitute of Dentistry, Lahore, Pakistan

Joni Salminen

³ Qatar Computing Research Institute, Hamad Bin Khalifa Academy, Doha, Qatar

⁴ Turku Schoolhouse of Economic science at the Academy of Turku, Turku, Finland

Soon-gyo Jung

³ Qatar Computing Research Institute, Hamad Bin Khalifa Academy, Doha, Qatar

Hind Almerekhi

^five Hamad Bin Khalifa University, Doha, Qatar

Bernard J. Jansen

³ Qatar Computing Enquiry Institute, Hamad Bin Khalifa University, Doha, Qatar

Vincenzo De Luca, Editor

Received 2019 May 26; Accepted 2019 Aug 24.

Abstract

Internet and social media participation open doors to a plethora of positive opportunities for the general public. Withal, in add-on to these positive aspects, digital engineering science as well provides an effective medium for spreading hateful content in the course of cyberbullying, discrimination, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of enquiry, research hotspots, and influential stakeholders such every bit organizations and contributing regions. For this, we apply scientometric techniques and collect research papers from the Web of Scientific discipline core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and clarify the information using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute nigh to OHR, with Western countries accounting for well-nigh of the publications, funded by North American and European funding agencies. We besides observed increased research activity post-2005, starting from more l publications to more than 550 in 2018. This applies to a number of publications besides every bit citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified 4 main clusters of OHR: (1) Cyberbullying, (ii) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (four) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated bug for individuals and society.

Introduction

The advent of the mod Net opens doors to a plethora of positive opportunities for the general public. These opportunities span beyond equity in instruction and general access to knowledge, modes of amusement, consumerism, and e-participation. However, in improver to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the grade of bigotry and hateful ideologies, as well every bit cyberbullying and harassment of individuals and groups on social media platforms [1,2]. Online hate, albeit conducted in the virtual earth, may have dire real-life consequences at both individual and population levels. For case, the cyberbullying among youth and student populations and subsequent links with poor mental wellness, depression, trauma, substance misuse, and a college risk of suicide are well-documented [3–6]. Recent estimates take placed exposure to online hate ranging from 31% to 67% beyond different study samples [seven]. Among New Zealanders, for example, eleven% of adults take been personally targeted by online detest [1], whereas, in the US, 41% of adults have experienced online detest speech and harassment [8]. Online hate has been shown to predominantly target and influence minorities, young age groups, people with disabilities, and the LGBTQ (Lesbian, Gay, Bisexual, Transgender, Queer) community [one].

Online detest spreading has also emerged equally a tool for politically motivated bigotry, xenophobia, homophobia, and excessive nationalism [9–12]. An example can be seen in the 2016 United states of america elections; the narrative of "Brand America Groovy Again" has empirically been shown to have amplified the online presence of white supremacists [9]. Social media platforms take granted a new spirit to radical nationalist groups including Klansmen and Neo-Nazis by ensuring anonymity or pseudonymity (i.east., disguised identity), ease of discussions, and spread of radical ideologies [ane]. Moreover, social media and online forums have provided hate-driven terrorist groups a medium for launching propaganda to radicalize youth globally [13]. These groups use images and Net videos to communicate their hateful intent, to trigger panic, and to cause psychological harm to the full general public [fourteen]. Every bit a prime example of cyberterrorism, the Islamic State of Iraq & Syria (ISIS) effectively used social media to recruit youngsters from Europe to participate in the Syrian disharmonize [12]. Their social media campaigns led to at to the lowest degree 750 British youngsters joining Jihadi groups in Syria [xiii]. Overall, these real-world phenomena highlight the very real negative impact of spreading online detest and suggest that online detest tin exist considered as a major public concern.

However, online hate is a complex phenomenon—with its definition depending on theoretical paradigms, disciplines, and forms of victimization [1,15]. Due to this complexity, online detest research (OHR) is a fragmented field with a growing number of research papers across disciplines, as the agin furnishings of online hate are more widely recognized in society and every bit new disciplines (east.thousand., computer science, psychology) are introducing their own approaches to study and solve the associated problems. Due to this increasing body of research, there is a demand for literature analyses that map the current land of OHR. While several testify-synthesis approaches have attempted to summarize and critically review the literature on online hate, these tend to be based on heterogeneous methodologies and restricted to a particular discipline or field of study [ix,ten,23,13,16–22]. For example, an elaborate effort by the British Institute of Human Rights sought to systematically map studies about initiatives against cyberbullying and inform legislative efforts by the European Union [21]. A qualitative approach by Awan sought to provide evidence regarding the use of social media platforms past ISIS by examining 100 Facebook pages and 50 Twitter users [13]. Country-specific efforts included Gagliardone et al.'due south efforts to map politically driven online hate in Ethiopia past reviewing relevant Facebook profiles, pages, and groups with more than than 100 followers [23], which provided a framework for analyzing online hate speech and explored the continuum between freedom of expression [23]. Cyber-bullying has too attracted attention from public wellness and mental wellness professionals. Most influential and cited work in this domain is attributed to Tokunaga, who critically reviewed and synthesized evidence on cyberbullying victimization [20].

However, none of the previous piece of work, to the cognition of authors, has focused on the mapping of full general enquiry indices, prevalent themes of inquiry, research hotspots, and influential stakeholders such as organizations and contributing regions regarding OHR. This undertaking is essential as such analyses assist to evaluate the field-specific impact of scholarly enquiry, as well as the bear upon of scientists, collaborative networks, and institutes. Therefore, we set out to map OHR using scientometric assay, defined every bit the "quantitative study of scientific discipline, communication in scientific discipline, and scientific discipline policy" [24]. Virtually importantly, scientometrics helps identify influential research studies resulting in the progress and evolution of a specific field of science [24]. By using reproducible statistical techniques, stakeholders tin quantize the research output, citation rates, influential funding agencies, journals, scientists, institutes, and regions involved in the progress of the scientific discipline [24]. By mapping these trends, researchers, policymakers, and funding agencies can determine areas where an increase or brake in inquiry work and funding is required [25–27]. Therefore, this investigation aims to accost this paucity of information using advanced scientometric techniques.

Methodology

Search strategy

We defined the focal topic of study equally online hate. We identified several definitions from the prior literature that helped united states of america understand the nature of the phenomenon and to collect a list of concepts that reverberate the multifaceted nature of OHR. Definitions of online hate vary, but a unifying factor is the use of technology for expressions that are harmful to individuals, groups, or order as a whole. An instance of a definition that encompasses this duality is that of Kaakinen et al., according to whom online hate has two defining characteristics: it is technology-mediated and intends to offend, discriminate and corruption a person or a group based on group defining characteristics such as gender, race, nationality, ethnicity, inability, or sexual orientation [7].

In the form of exploring the definitions, we compiled a list of keywords for the electronic search carried out to identify the body of research almost OHR (encounter Table 1).

Table 1

Key concepts in online hate enquiry, operationalized as search terms.

Concept	Definition
Online hate	Forms of hateful expressions disseminated on the Internet, typically targeting a specific group or private
Online detest speech	As higher up, but fulfilling the legal definition of hate spoken language (that may vary by state)
Online toxicity	Social media commenting that is likely to reduce an private's desire to participate in discussions due to fear of beingness ridiculed
Online abusive language	Use of slurs and vocabulary that is offensive to other Internet users
Cyberbullying	Systematically attacking a person or people via electronic channels; e.g., name calling, discrediting, shaming
Online harassment	Predatory and oppressive behavior on the Cyberspace; e.g., sending sexual messages to non-consenting individuals
Online firestorms	Inflammatory forms of online discussions ("fighting"), unremarkably taking identify in word forums between rivaling groups

In addition to operationalizing the concepts in Table 1 as search terms, nosotros defined a list of popular social media platforms that were also used as search terms, as several studies focus on hate taking identify in a specific social media platform. Using the Spider web of Scientific discipline cadre database, an electronic search was conducted to retrieve peer-reviewed research studies (published through March 2019) pertaining to online hate. Overall, this search strategy encompassed important concepts pertaining to online hate and popular platforms: "TS = (Hate OR toxicity OR cyberbullying OR bullying OR harass* OR firestorm* OR abuse OR calumniating OR 'abusive language' OR maltreat* OR oppress* OR persecut* OR taunt* OR great* OR bullies OR victim* OR 'hate speech') AND TI = (Online OR 'social media' OR web OR virtual OR cyber OR Orkut OR Twitter OR facebook OR Reddit OR Instagram OR snapchat OR youtube OR whatsapp OR wechat OR QQ OR Tumblr OR linkedin OR pinterest)". As mentioned, this search strategy was formulated based on an initial reading of the literature and identifying normally emerging terms in the studies about online hate. No restrictions were applied for year of publication or language.

The search procedure resulted in a total of three,371 research manufactures for a scientometric analysis. The data curated from the Web of Scientific discipline (core database) included the citation characteristics, commendation counts, and cited references. The Web of Science core database is i of the nigh ofttimes used databases for scientometric analyses. It was chosen primarily because information technology indexes detailed citations and total records of cited references that assist in elucidating co-citation relationships between related documents [28].

Operational definitions and inclusion criteria

The present mapping study is a broad overview of OHR. In line with our objectives, a broader interpretation of online hate was preferred, roofing all forms of expressions that spread, incite, promote, or justify hate against groups or individuals [21]. This interpretation was adjusted from the framework for online hate proposed by the British Institute of Human Rights [21]. All forms of expressions on a macro-level including racial hatred, xenophobia, anti-Semitism, ambitious nationalism, and hatred against minorities and migrants were included. On an individual level, diverse forms of expression, for instance, partner corruption likewise as cyber-bullying confronting school children owing to their racial, ethnic, sexual groundwork, and disabilities were included [21]. We acknowledge that there are culling definitions for online hate and online toxicity, the latter of which can be defined as rude, disrespectful, or unreasonable commenting that is likely to make one go out a discussion [29,30]. Most of these definitions perceive online hate every bit a conceptually wide phenomenon that touches many stakeholder groups. For that reason, we consider wide inclusion criteria to be relevant for this research.

Co-citation analysis and knowledge mapping

In the commencement phase, information curated from the Web of Science core database (WOS) was utilized for knowledge mapping based on the theory of document co-citation. According to this theory, when two documents are co-cited by i document, they are connected in a co-citation relationship [31].

Co-citation analyses were performed using CiteSpace software (northward = v4.0, Drexel University, Pennsylvania, US). The bibliographic records retrieved from WOS were fed into the CiteSpace software, and "sliced" into three-year slices, where each piece was represented by 50 documents with the highest cited frequency. Titles, abstracts, and keywords were used every bit terms sources while cited references were used as nodes.

Afterwards that, network assay was run using pathfinder network scaling while allowing for the pruning of sliced networks [25–27]. All bibliographic data were so visualized every bit merged and static networks/clusters. Articles were represented as nodes, while the human relationship between nodes was visualized as lines or edges. Two of import matrices were used to demonstrate the overall structural properties of the network: modularity and silhouette value. Note that a loftier value of modularity (close to 1) corresponds to a good network structure that is reasonably divided into loosely coupled clusters, and a high silhouette score represents an appropriately homogenized cluster. This technique allowed for the visualization of of import publications in a collaborative network based on their centrality values, besides identified as a tree band representing their history of citations and yr-wise patterns [25–27]. New theories and landmark studies with loftier between-ness centrality were identified as purple rings while citation bursts were visualized as red tree rings [25–27].

Citation bursts were defined equally articles attracting significant research activity in a given period. Clusters and themes of inquiry in this field were identified by running a cluster analysis that identified the publication record cited in a specific fix of publications, and the clusters were named using naming algorithms including TF*IDF; Mutual Information (MI) and Log Likelihood Ratio (LLR) [25–27]. Each cluster was also depicted by a year representing the mean year of publications of all included research studies. Out of these methods, LLR has been shown to exist the most accurate [25–27]. The beginning method, TF*IDF, utilizes terms that are weighted by term frequencies (TF) multiplying inverted document frequencies (IDF) [25–27]. Log-likelihood ratio tests choose the most advisable clustering characterization by assessing the force of the bond between a term and the cluster [25–27]. Generally, the college the LLR, the better the bear witness. Lastly, the mutual information method is used for feature pick in machine learning; yet, it works amend with larger datasets [25–27].

Results

Enquiry activity

The search process yielded a full of 3,371 publications that were included in the scientometric assay. These publications boasted an h-index of 82, eleven.23 citations per item, cited for a total of 37,848 times overall (n = 33,721 excluding self-citations). Increased publication and citation activities were observed post- 2005 starting from >50 publication to > 550 in the twelvemonth 2018 (Figs ​ 1 and ​ 2).

Rate of publications from the twelvemonth 2000 to 2018.

Rate of citations from the yr 2000 to 2018.

Top organizations, funders, and regions

The Usa (US) was the most frequent publisher in this domain with 1,205 publications, followed past England, Australia, China, Canada, India, Germany, Spain, the Netherlands, and Italia. Among universities, the University of London, U.k. was the most frequent contributor, followed by academy systems in the The states: the Academy of California Organisation, the Pennsylvania Commonwealth System of Higher Instruction, State University of Florida, the University of North Carolina, the University of Texas Arrangement, the Academy of Georgia, the Academy of Washington, Columbia Academy, and the University of Washington in Seattle. Superlative funders included United States Department of Health and Human Services (HHS)/National Institutes of Wellness (NIH), National Natural Scientific discipline Foundation of China, National Scientific discipline Foundation, Economic and Social Enquiry Quango, National Institute of Drug Corruption, European union, and Catalan Institution for Enquiry and Advanced Studies (ICREA). Collaborative networks of countries and institutes are presented as Figs ​ 3 and ​ 4, while frequencies of publications by top countries are presented in Table ii.

Table 2

Acme countries, institutes, and sources co-ordinate to the number of publications.

Country	n	Institute	n	Periodical	due north	Briefing	northward
The states	1,205	University of London, UK	70	Figurer in Human Behavior	76	IEEE ACM International Conference on Advances in Social Network Analysis and Mining	14
England	317	University of California, USA	68	Lecture Notes in Information science	46	Almanac International Conference on Education Inquiry and Innovation	4
Australia	194	Pennsylvania Commonwealth System of College Instruction	fifty	Cyberpsychology, Behavior & Social Networking	36	International Conference on World wide web	4
China	179	Country University of Florida	48	Journal of Medical Internet Enquiry	32	ACM Conference on Computer Supported Cooperative Work and Social Calculating	4
Canada	171	University of North Carolina Arrangement	36	Journal of Adolescent Wellness	25	Saudi Computer Society National Computer Conference	3
Bharat	169	Academy of Texas System	35	Journal of Youth and Boyhood	23	IEEE International Conference on Trust Security and Privacy in Computing and Communication Trustcom	3
Germany	145	University of Georgia	33	Procedia Social and Behavioral Sciences	21	ACM SIGSAC Briefing on Computer And Communications Security	3
Kingdom of spain	136	University of Washington	32	PloSOne	23	International Conference on Intelligence and Security Computer science Cybersecurity and Big Data	3
Netherlands	99	Columbia University	31	New Media Society	xx	-
Italy	96	University of Washington in Seattle	31	Child Abuse & Neglect	xviii	-

Collaborative networks based on countries.

Collaborative networks of institutes.

Top sources

Height sources included Calculator in Man Behavior, Lecture Notes in Computer science, Cyberpsychology, Beliefs & Social Networking, Periodical of Medical Internet Research, Journal of Adolescent Wellness, Journal of Youth and Boyhood, Procedia Social and Behavioral Sciences, PLOS 1, New Media Gild, and Child Abuse & Neglect. While most frequent conference proceedings were published by IEEE ACM International Briefing on Advances in Social Network Analysis and Mining, Annual International Conference on Teaching Enquiry and Innovation, International Briefing on Earth Wide Web, ACM Conference on Figurer Supported Cooperative Work and Social Computing, Saudi Estimator Order National Estimator Conference, IEEE International Conference on Trust Security and Privacy in Computing and Advice Trustcom, ACM SIGSAC Conference on Computer and Communications Security and International Briefing on Intelligence and Security Informatics Cybersecurity and Large Information. Frequencies of publications by top sources are presented in Table ii.

Fields of publication

Top ten fields of publication included informatics data systems (northward = 325), estimator science theory methods (n = 282), criminology (n = 263), advice (n = 221), multidisciplinary psychology (n = 193), electrical/electronic engineering (n = 187), information science interdisciplinary publications (northward = 183), psychiatry (n = 168), educational enquiry (n = 180) and clinical psychology (n = 154).

Top papers based on centrality in respective clusters

Acme papers were judged based on their values of centrality, where a value of 0.one indicates a central publication. In a collaborative and co-cited network of publications, a loftier centrality value reflects highly significant research studies. Nevertheless, in this analysis, none of the studies reached a axis value of 0.1, indicating no central publication in the respective cluster. However, top centrality value (> 0.01) was achieved by fourteen studies (Table 3 and Fig 5). The majority of these papers focused on cyberbullying amid adolescents. Tokunaga RS (2010) and Kowalski RM (2007) were found to exist nigh central to entities with centrality values of 0.04.

Table 3

Top articles based on centrality values.

Citations in WOS Core	Burst years*	Axis	Sigma	Author	Year	Source	Cluster
152	8.84	0.04	1.39	Tokunaga RS	2010	Comput Hum Behav	two
77	nineteen.59	0.04	two.14	Kowalski RM	2007	J Adolescent Health	1
122	20.5	0.03	1.99	Smith PK	2008	J Child Psychol Psyc	1
86	xi.35	0.03	1.34	Slonje R	2008	Scand J Psychol	1
44	fourteen.39	0.03	1.52	Raskauskas J	2007	Dev Psychol	1
41		0.03	1	Calvete E	2010	Comput Hum Behav	2
seven	4.06	0.03	1.15	Ybarra ML	2007	J Boyish Wellness	1
84	3.04	0.02	i.07	Hinduja S	2010	Curvation Suicide Res	1
lxxx	12.59	0.02	one.31	Juvonen J	2008	J School Health	1
46	three.86	0.02	1.09	Erdur-baker O	2010	New Media Soc	2
45	9.01	0.02	1.15	Dehue F	2008	Cyberpsychol Behav	1
27	7.34	0.02	1.15	Zweig JM	2013	J Youth Adolescence	10
20	5.74	0.02	1.xiii	Mitchell KJ	2007	Am J Prev Med	seven
vii		0.02	1	Borrajo E	2015	Comput Hum Behav	x
84	nineteen.02	0.01	ane.23	Kowalski RM	2014	Psychol Bull	two
59	9.05	0.01	i.08	Livingstone S	2011	Risks Safety Interne	half-dozen
53	xvi.98	0.01	1.09	Patchin JW	2006	Youth Violence Juv J	1
41	13.3	0.01	1.11	Li Q	2006	School Psychol Int	1
38	8.47	0.01	ane.xi	Kowalski RM	2013	J Adolescent Health	2
34	12.55	0.01	1.09	Williams KR	2007	J Boyish Wellness	1
33	4.86	0.01	1.04	Mesch GS	2009	Cyberpsychol Behav	half dozen
32	8.98	0.01	i.06	Reyns BW	2011	Crim Justice Behav	10
30	9.57	0.01	1.eleven	Ybarra ML	2007	J Adolescent Health	i
28	half-dozen.22	0.01	1.08	Gamez-guadix M	2013	J Adolescent Health	two
27	vi	0.01	1.03	Bauman S	2013	J Boyhood	2
25	7.nineteen	0.01	1.09	Ybarra ML	2007	Curvation Pediat Adol Med	7
22	8.06	0.01	ane.05	Beran T	2005	Periodical Of Educational Computing Research	1
x		0.01	ane	Kloess JA	2014	Trauma Violence Abus	6
9		0.01	ane	Mitchell KJ	2011	J Adolescent Health	half dozen
8		0.01	1	Montiel I	2016	Child Corruption Fail	6
8	iv.09	0.01	1.02	Mitchell KJ	2007	J Boyish Health	7
7		0.01	i	Perren Sonja	2010	Child Adolesc Psychiatry Ment Health	2
6		0.01	1	Staude-muller F	2012	Eur J Dev Psychol	six
6		0.01	i	Reyns BW	2012	Deviant Behav	10
six	3.67	0.01	ane.02	Mitchell KJ	2003	Youth Soc	1
6		0.01	i	Livingstone Southward	2010	New Media Soc	half-dozen
5	3.06	0.01	1.03	Fleming MJ	2006	Youth Soc	7
v	3.06	0.01	one.02	Erdur-baker O	2007	J Euroasian Ed Res	ane
4		0.01	1	Blais JJ	2008	J Youth Boyhood	1
4		0.01	1	Beran	2005	J Educ Comput Res	1
two		0.01	1	Appelman DL	1995	Law Internet	3

Influential authors in online hate.

Six publications, including Raskauskas and Stoltz [32]; Kowalski and Limber's as well every bit Smith et al.'s work from 2007 to 2008 [five] were one of the earliest studies that noted the prevalence and nature of electronic bullying, victimization, and perpetration among American pupils [5,32,33]. Dehue et al. [34] focused on youngsters' experience of cyberbullying as well as their parents' perception about information technology. They institute that parents exercise fix rules for the use of the Internet for their children simply are not witting of their perpetrating beliefs and also underestimate victimization experiences [34]. Slonje and Smith reported four types of cyberbullying—by text message, email, call, and video clip—and emphasized that bullying by video clips is perceived every bit most negative in the guild, and most of the pupils tell their schoolhouse friends about their experiences and non their parents [35]. Erdur-Bakery explained the risky use of the Net and its association with cyberbullying in Turkey and was one of the rarer studies conducted outside the Usa [36].

Tokunaga provided synthesized critical review testify of cyberbullying and provided an integrative definition of cyberbullying, differentiated it from traditional bullying, and linked information technology with serious psychosocial and affective bug [20]. His work besides outlined the areas of concern in research on cyberbullying and provided a framework for time to come enquiry [20]. In a similar vein, Junon and Gross [37] reported patterns of cyberbullying and their association with social feet among school going children [37]. Hinduja and Patchin provided the earliest link of cyber-aggression and increased take chances of suicide [4]. Ybarra et al. [38] associated cyberbullying to dominion-breaking behavior and aggression in real life in a dose-dependent way [38].

2 studies focused on the development of the most widely used psychometric questionnaires in cyberbullying. Calvete et al.'s [39] work was the earliest piece of work that led to the development and validation of the Cyberbullying questionnaire for profiling aggressors and cyberbullies [39]. They too reported that the use of proactive aggression, justification of violence, exposure to violence, and less perceived social back up of friends was prevalent among cyberbullies [39]. A cyber-dating abuse questionnaire assessed two latent constructs: straight aggression amid romantic partners and monitoring control, such as the use of personal passwords [40]. Another of the two studies reported teen dating abuse using an online medium and online sexual solicitations in conversation rooms and its risk factors including using chat rooms, using the Internet with a cell phone, talking with people met online, sending personal information to people met online, talking about sex online, and experiencing offline concrete or sexual corruption [41,42].

Domains of research: Cluster analysis

A full of 101 clusters of research emerged in the cluster analysis (Fig vi). These clusters were given names according to 4 methods: Latent Semantic Indexing (LSI), Term Frequency * Inverted Document Frequency (TF*IDF), loglikelihood ratio (LLR), and Mutual Information (MI). Nosotros study in parentheses which method was used to derive the proper noun for a given cluster; generally, information technology is not important to report all of them, as the outputs of each method were non always sensical. Detailed information regarding the top 10 clusters and their timelines have been presented as Figs ​ half dozen and ​ 7. This assay was based on 499 nodes and 906 lines or edges and yielded modularity of 0.86.

Clusters of research from the year 2000 to 2018.

Timeline view depicting clusters of enquiry bundled on a horizontal timeline from 2000 to 2018.

Clusters on cyberbullying

5 clusters focused on the theme of cyber-bullying. The first meaningful cluster (due north = 48, silhouette value = 0.91) emerged as a social networking site equally per TF*IDF, cyberbullying, internet harassment and sexual harassment and cyberbullying experience (MI) in 2006 (mean year of publication of included studies). In other words, there were 48 research manufactures with a similar theme that could be presented with the cluster title of "social networking site" by the TF * IDF method. These 48 manufactures were placed in this cluster considering all of them were cited by a similar group of publications, thus, representing a co-citation relationship. The most cited of this group was Mishna [43] who investigated cyberbullying behaviors among Canadian adolescents. They reported that bullying perpetrators perceived themselves as funny, popular, and powerful, admitting feeling guilty as well [43]. The second meaningful cluster included 48 studies with a silhouette value of 0.88 in 2011. It was named as general strain theory (TF*IDF), cyberaggression (LLR), and Australian youth (MI). The virtually active citer was Kowlaski et al. [44], who reported cyberbullying behavior among college students beyond multiple domains of life [44].

Cyberbullying and utilization of routine action theory were discussed in the seventh cluster with xv members, a silhouette value of 0.99 and the mean year 2004. It was termed every bit social networking site by TF*IDF method, internet user, utilizing routine action theory, potential factor by LLR method, and example written report by MI method. The most active citer of this cluster was Marcum et al. [45], who provided causal reasoning for cyber-victimization utilizing the framework of routine activity theory [45]. This theory posits that victimization requires iii factors: the presence of a likely offender, a suitable target, and the absenteeism of a capable guardian [45].

The 12^th cluster focused on the association of spending time in online communities (TF*IDF) with the mental wellness of adolescents and caregiver-child relationships (LLR and MI). This cluster included seven papers with a silhouette value of 1.00 in 2000. The most active citer of this grouping was Ybarra et al. in 2004, who focused on Internet harassment and its association with quality of child-caregiver relationship [46]. The sixteen^th cluster reported papers on an educational and creative intervention to prevent cyberbullying. Information technology was termed as virtual drama, the emergent narrative arroyo, and anti-bullying education (TF*IDF, MI, LLR), and emerged in 2005 [47]. The most active citer, Aylett et al. [47] presented evidence for virtual educational software to forestall cyber-bullying.

Clusters of sexual solicitation and intimate partner violence

A total of three important clusters focused on the theme of sexual solicitation, dating abuse, and intimate partner violence. The third cluster focused on social support (TF*IDF) sexual solicitation via electronic mail; seeking human service; social support (LLR and MI) and included 44 papers. The most active citer was Finn (2000), who described the dangers involved when women seek human services on the internet [48]. This cluster emerged in the year 1998, highlighting early years of research.

The sexual solicitation was the focus of some other cluster with 17 papers and a silhouette value of 0.94, emerging in the year 2012. It was termed as extent, situational factor (LSI); hate speech, network site, and online sexual solicitation (LLR, MI). It focused on the abuse of minors as well every bit online exposure amid the youth equally evident by its near active citers [49].

The 10th cluster focused on intimate partner violence by utilizing routines action theory, comprising ten papers in the twelvemonth 2011 and a mean silhouette value of 0.99. Information technology was labeled as information security; the extent of cyberbullying behavior (TF*IDF), cyber partner abuse, systematic review, routine activities theory, and empirical study (LLR, MI). The virtually active citer for this cluster was Arntfield (2015), who proposed a new framework for agreement cyber victimology using the Routines Activity Theory Framework [fifty]. The author stressed the role of victims equally both a facilitator and factor for predation [fifty]. The terms "systematic review" and "empirical study" refer to the report designs utilized by studies in these clusters.

Clusters on deep learning & automation

Deep learning and automation were studied in two important clusters. The 4th cluster focused on cyber defense (TF*IDF) and adaptive use and network-centric mechanism (LLR) and emerged in 2000. The most active citer was Atighetchi in 2000, whose work focused on defending against network-based attacks, and development of technologies augmenting an awarding'due south resilience against hackers [51]. The twenty^th cluster revealed deep learning models and text nomenclature equally a feasible source for identification of hate speech on Facebook groups in 2016 with a silhouette value of 1.0. The papers by Agrawal et al. [52] and Pitsilis et al. [53] were the most mutual citers of these clusters. Pitsilis et al. [53] proposed recurrent neural network models to discern hateful content on social media utilizing user-related information such as their tendency toward racism and sexism [53], while Agrawal et al. [52] showed that previous algorithms aiding in detection of cyberbullying take bottlenecks: specific platform, a specific topic of bullying, and thirdly, reliance on handcrafted features of the data. They proposed that deep learning models are feasible in all of these situations [52].

Clusters on extremist & online hate groups

This cluster (#5) emerged in the year 2002 and included 18 research items. It was named as extremist groups and mining communities (TF*IDF); online hate group, mining communities, attack tolerance (LLR, MI). The well-nigh active citing newspaper of this cluster was published by Chau et al. [10], who emphasized the importance of analyzing the trends of online hate communities and terrorist groups who share their ideologies to recruit new members. They proposed network analysis and mining techniques as important weapons in this loonshit [12]. The 14^th cluster revealed the use of soapbox theory and disquisitional theory as a framework for studying online Islamophobia (TF*IDF, MI). This cluster as well had studies focusing on feminism and compensatory manhood (LLR). The most active citer reported harassment and misogyny in online sexual marketplace places and dating websites such every bit Tinder [54]. The cluster besides includes papers on automated identification and classification of misogynistic languages on social media using NLP and auto learning methods [55]. Moreover, a paper on Islamophobia revealed eleven false Facebook pages run by Danish citizens posing as Muslims threatening to impale and rape Danish citizens, termed equally platformed antagonism [56].

Keyword analysis

Furthermore, we used keywords from titles, abstracts, and keywords sections of the research papers to construct keyword co-occurrence networks (meet Fig vi). Co-occurrence and frequency of occurrence of keywords provide a snapshot and a reasonable description of trends of inquiry in a specific area [26]. Also, analysis of burst items provides short periods of significant activeness in a particular domain or an emerging topic and research frontier [26]. Fig six presents the most oftentimes cited keywords, with larger rings presenting significant keywords. Co-ordinate to it, Internet, adolescents, victimization, social media, Facebook, Twitter, experience, gender, children, victim, victimization, youth, schoolhouse, toxicity, abuse, and risk almost frequently occurring items cited at least ninety times in the literature. Table 4 lists the top 25 cited keywords, and Fig eight presents co-citation relationship between keywords.

Table 4

Peak cited keywords.

Citations	Keyword	Mean year of citation of keyword
385	Internet	2002
305	Adolescent	2004
273	Social media	2012
241	Victimization	2007
178	Cyberbullying	2012
176	Beliefs	2004
167	Youth	2004
143	Corruption	2003
118	Risk	2004
116	Children	2004
115	Facebook	2012
115	Victim	2002
114	Toxicity	2000
110	Gender	2007
104	Feel	2003
104	Online	2009
103	Impact	2004
97	School	2004
97	Twitter	2012
95	Cyber bullying	2007
94	Prevalence	2007
92	Low	2004
91	Student	2007
87	Aggression	2004
82	Intervention	2007

Co-occurrence of keywords.

When burst items analysis was conducted, a total of 53 flare-up items were identified (see Fig nine). The fourth dimension interval of the scientometric assay (2000–2018) has been depicted as a blue line and the period that represents the burst action, as a cherry line [26,57]. Information technology presented four main themes of research hotspots in this field, including:

1. Cyberbullying: this hotspot focuses on the pattern of cyberbullying such equally cyber-victimization; cyber-bullying, harassment; privacy intrusion; sexual solicitation and involvement.

2. Social media platforms: focused on online communities and specific social media platforms for detection and prevention of hate oral communication using deep learning and automation.

3. Co-morbid disorders: this hotspot is characterized by keywords such as addiction; substance use; post-traumatic stress disorder; and Internet addiction, citing the importance of co-morbid mental health symptoms amidst aggressors and victims of cyberhate.

4. Profiling of aggressors and victims: It was characterized by keywords such as identity; school student; personality; gender differences; and identification and take chances assessment. These commendation bursts exhibit increased enquiry focused on psychological characteristics of both the aggressor and victims. This grouping likewise stratifies the population based on their demographic characteristics and increased chance of bullying behaviors.

Discussion

Summary of results

The present study highlights the trends of inquiry in the field of OHR. It revealed several clusters of OHR, innovative techniques to detect hate speech, sexual solicitation, exposure to pornography, Islamophobia, misogyny, and cyber-bullying along with its effects among the youth. The United states of america was the lead contributor to this field of research, and our analysis as well revealed a clear authority of Western universities equally well as funders from North America, Europe, and China. This global dominance and a college share of Western institutions have been noted in several empirical investigations [58–lx]. Moreover, our analysis revealed a major contribution from psychology-related fields, spanning across the study of man behavior, psychological profiling of aggressors and victims, and co-morbid disorders such equally depression and Internet addiction or pathological Net apply, equally well as the association between offline and online bullying behaviors. These studies are highlighting the negative consequences of online hate, such as the increased risk of suicide among the victims of cyberbullying [35–37,43]. Overall, there has been a pregnant increment in publication and citation tendency in OHR after the year 2005, which coincides with the proliferation of social media platforms and the Internet becoming a central arena for public and private discourse.

Strengths, limitations, and time to come work

There are several strengths and limitations to this study. This is a first concerted effort to map the research action on online hate. In contrast to previous studies designed as qualitative content analyses or literature reviews on a restricted topic, this report provides a broader assay of publications of online detest. Even so, in that location are a few limitations to this written report. Co-citation analyses is a quantitative technique to map research output in a field, and there are several other indicators such every bit the number of citations accrued or quality of a research article [61]. The role of citation frequency alone to map almost influential studies has been long debated [61].

Moreover, while our assay revealed a major contribution from psychology-related fields, this loftier representation of psychology-related contributions may be due to several reasons; for instance, the choice of WOS core equally the database. Its coverage may be geared towards health and social science disciplines rather than technology or computer sciences [62], thereby excluding some relevant research from these fields from the analysis. It may also be because there has been a mushroom growth and development in psychology-related publications, interdisciplinary and collaborative networks, as well as higher commendation rates, took place in this domain. While we defend the pick of the WOS core database because it is one of the few databases yielding records for cited references [25,28] and embodying a curated drove of over xx,000 peer-reviewed publications pertaining to 250 disciplines in scientific discipline, social sciences, and humanities [25,28], thereby being attainable for scientometric analyses, we acknowledge that there is a body of OHR literature that is not included in our analysis due to sampling limitations. Future research should aim at replicating or extending this report past accessing literature from other databases, such as ACM Digital Library.

Implications for enquiry and do

The primary lessons learned from this scientometric assay are as follows:

1. Most of the publications originate from the discipline of psychology and psychiatry with recurring themes of the prevalence of cyber bullying, psychiatric morbidity, and psychological profiles of bullies and victims, particularly among the youth. In later years, in that location was some focus on dating violence and harassment of women. The main implication is that policy makers, and funders demand to shift their focus on other fields, such as intervention and implementation sciences to design both technological and non-technological solutions to identify and curb online hate.

2. Almost all the influential studies have been conducted in the context of high-income countries. Research is needed in low and heart-income countries to justify the generalizability of OHR findings likewise equally to produce culturally applicable interpretations.

3. Equally far equally we are enlightened of, this is the offset concerted effort to map global enquiry output regarding OHR, spanning across scientific disciplines such as psychology, computer sciences, and the social sciences. However, the say-so of psychology related publications may have skewed the overall results. For this reason, nosotros also encourage subject field-specific scientometric studies because nearly of the studies published to date were i) discipline or population-specific, ii) simplistic literature reviews, and iii) lacked systematic search process and 4) reproducible data science techniques.

In conclusion, the increase in OHR is a reaction to the increased occurrences of hate speech, in all of its diverse forms, on the many social media and other online platforms. Online detest speech is, obviously, a complex societal problem that intersects many aspects of everyday life. The cross-disciplinary and multifaceted nature of OHR as a field of research is a witness to the circuitous result of online hate. The findings from enquiry and so far hint at the need for both technology and non- technology approaches to accost this increasingly pressing societal issue.

Supporting information

S1 Dataset

All data associated with this written report have been provided every bit a supplementary file named data supplement.cipher.

(ZIP)

Funding Argument

The author(s) received no specific funding for this work.

Data Availability

The data is provided in S1 Dataset.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6763199/

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