Search Results (13)

This study aims to validate the Online Sexual Harassment Perpetration among Peers (OSHP-P) instrument for assessing online sexual harassment among adolescents in two different countries, Spain and Italy, considering both new forms of online sexual harassment and gender differences. The instrument was validated by means of a Confirmatory Factor Analysis (CFA) with a sample of 1041 Spanish (M_age = 15.0, SD = 0.88) and 1385 Italian (M_age = 14.8, SD = 0.87) adolescents, demonstrating factorial invariance across both country and gender. The best-fitting model was two-dimensional, with ambiguous and direct Sexual Cyber Perpetration (SCP) and Non-Consensual Sharing Perpetration (NCSP) factors. Co-involvement (i.e., involvement in both types of aggression) rates were 10.3% in Spain and 7.8% in Italy. No significant gender differences were found for involvement in either the overall scale (46.4% for girls, 44.1% for boys) or the NCSP subscale (3.0% girls vs. 2.2% boys), although significantly higher co-involvement was found among boys (7.7% girls vs. 10.1% boys). This study contributes to the existing body of research on online sexual harassment among peers in adolescence by presenting a new assessment tool that has been shown to be invariant between Spanish and Italian adolescents, as well as between boys and girls. Full article

With regard to negative consequences, cyberbullying is recognized as one of the most traumatic types of cyber aggression. The aim is to study the specific features of adolescents and youth’s cyberbullying experience in the role of an aggressor, victim or bystander, as well as awareness on the part of parents of adolescents. A total of 3395 adolescents, youth and parents filled out specially designed questionnaires. Older adolescents turned out to be at higher risk of cyberbullying. In two-thirds of cases, cyberbullying is related to real-life incidents. Aggressors are motivated by domination and entertainment, primarily employing strategies of social exclusion, harassment and denigration. As victims of cyberbullying, younger adolescents turn to their parents and friends for social support, whereas older adolescents and young adults are more likely turn to their peers. In the role of a bystander, almost half of younger adolescents and about a third of older adolescents and young adults choose the prosocial strategy of protecting a victim. The parents often underestimate the experience of encountering cyberbullying or find it difficult to assess such experience. The identified risk groups and strategies and the lack of parents’ awareness are important to take into account when drawing up cyberbullying prevention programs. Full article

Background: The pervasive use of technology, especially among adolescents, has enabled cyber communication and brought many advantages but also led to potential violence. The issue of cyber interpersonal violence (CIV) impacting young individuals is increasingly recognized as a matter of public health; however, little is known about adolescents’ perspectives of the phenomenon. This study explores adolescents’ perspectives on CIV. It seeks to understand their interpretations of abuse, victim impact and reactions, violence escalation, gender issues, victimization and perpetration patterns, and bystander roles. Methods: This qualitative study used fifteen focus groups to gather elementary school participants’ perspectives on cyber interpersonal violence. From four Portuguese schools, 108 participants (M = 12.87 and SD = 0.31) participated in the study. A thematic analysis uncovered three themes. The results evidenced adolescents’ perspectives about CIV. Due to the amount of time spent online, adolescents regularly encounter cyber harassment and recognize the importance of help-seeking. Mental health problems and their influence on the social and educational lives of adolescents is arising as a CIV problem. Conclusions: Parents play a crucial role in mitigating CIV as well as bystanders. Future programs should promote healthy relationships, raise CIV awareness, involve stakeholders, guide parents, integrate perpetrators into programs, and foster effective networking. Full article

The growing prevalence of cybercrime, particularly among young adults, necessitates the promotion of digital citizenship to educate students about responsible online behavior and to equip them with the skills to mitigate cyber risks. The specific objective of this study was to investigate the effect of digital citizenship skills on the prevention of cybercrime among higher education students. A mixed-method approach, including surveys and interviews, was employed to collect data from 652 students in Saudi Arabia. This study found that digital citizenship generally has a significant impact on students’ awareness and prevention of cybercrime through the development of responsible online behavior. Knowledge of digital law came first, followed by beliefs about digital manners. Digital communication skills came third, followed by digital rights, knowledge, and duties in fourth place. Then, digital commerce skills and digital health beliefs came fifth and sixth, respectively. This was followed by digital access skills, then digital security, and finally digital culture. The results also revealed a negative statistical relationship between digital citizenship and cybercrimes’ various forms including national, financial, banking, social, immoral, insulting, slanderous, defaming, threatening, and harassment in virtual learning environments. These findings have significant implications for the understanding of how higher education institutions can promote digital citizenship and prevent cybercrime by integrating digital citizenship education into their curriculum, providing training for educators, and establishing clear policies and guidelines for responsible online behavior. Full article

Background: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. Methods: This research compares traditional classifiers and ensemble learning in classifying virtual harassment in online social media networks by using both models with four different datasets: seven machine learning algorithms (Nave Bayes NB, Decision Tree DT, K Nearest Neighbor KNN, Logistics Regression LR, Neural Network NN, Quadratic Discriminant Analysis QDA, and Support Vector Machine SVM) and four ensemble learning models (Ada Boosting, Gradient Boosting, Random Forest, and Max Voting). Finally, we compared our results using twelve evaluation metrics, namely: Accuracy, Precision, Recall, F1-measure, Specificity, Matthew’s Correlation Coefficient (MCC), Cohen’s Kappa Coefficient KAPPA, Area Under Curve (AUC), False Discovery Rate (FDR), False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) were used to show the validity of our algorithms. Results: At the end of the experiments, For Dataset 1, Logistics Regression had the highest accuracy of 0.6923 for machine learning algorithms, while Max Voting Ensemble had the highest accuracy of 0.7047. For dataset 2, K-Nearest Neighbor, Support Vector Machine, and Logistics Regression all had the same highest accuracy of 0.8769 in the machine learning algorithm, while Random Forest and Gradient Boosting Ensemble both had the highest accuracy of 0.8779. For dataset 3, the Support Vector Machine had the highest accuracy of 0.9243 for the machine learning algorithms, while the Random Forest ensemble had the highest accuracy of 0.9258. For dataset 4, the Support Vector Machine and Logistics Regression both had 0.8383, while the Max voting ensemble obtained an accuracy of 0.8280. A bar chart was used to represent our results, showing the minimum, maximum, and quartile ranges. Conclusions: Undoubtedly, this technique has assisted in no small measure in comparing the selected machine learning algorithms as well as the ensemble for detecting and exposing various forms of cyber harassment in cyberspace. Finally, the best and weakest algorithms were revealed. Full article

Background: Research efforts are increasingly recognizing young girls’ experiences of technology facilitated sexual harassment, which includes sexual harassment via electronic technology and social networking sites. The current study aimed to qualitatively describe experiences of cyber sexual harassment (CSH), as well as its effects, among a sample of sexually active adolescent girls. Methods: Qualitative interviews (n = 25) were conducted among a sub-group of adolescent girls at risk for CSH (those who reported experiencing sexual or dating violence) who participated in a larger cross-sectional clinic-based study on sexual health. Participants were asked to describe their experiences or peers’ experiences of CSH. Interviews were transcribed, coded, and a thematic analysis approach was used to analyze qualitative findings. Results: Participants reported experiencing several different types of CSH, including (a) being forced or pressured to send sexual photos, (b) receiving unwanted sexual messages/photos, and (c) having sexual photos posted or shared without the sender’s permission. Findings also highlighted the consequences of experiencing CSH, including social isolation and negative effects on girls’ education. Conclusions: These scenarios of CSH described by participants highlight the multiple ways in which girls experience CSH. Our findings begin to inform the development of quantitative survey measures that reflect these specific types of CSH experiences reported by adolescents. The consistent use of such measures will be critical to establish the prevalence and consequences of CSH in future studies on this topic. Full article

Students, colleagues, and other members of society are increasingly using digital media. Students utilize digital media for a variety of reasons, including communication, gaming, making new friends, and simply being curious. However, there are some disadvantages to using digital media. Cyberbullying, cyberharassment, and cyberstalking are examples of useful digital media activities that can have a negative impact on digital media users and lead to societal issues. Surprisingly, limited studies have investigated cyberbullying in depth, utilizing a broad and varied sample of Middle Eastern institutions. As a result, the purpose of this study is to fill a research vacuum by questioning students’ use of digital media for cyber involvement. This research aims to create a model for assessing the ethical consequences of behaviors that directly impact students’ psychological health because of their use of digital media. The questionnaire looked at how people used digital media to engage in cyberbullying and cyber engagement, the ethical implications of bullying, and being harassed, stalked, and bullied. The study employed a quantitative questionnaire to collect data to achieve the research goal. It was given to 1012 students who are digital media users. Partial least squares (PLS) and structural equation modeling (SEM) were used to examine the data. Considering the empirical data, nearly half of the participants admitted to being harassed, stalked, or bullied on different digital platforms. The evaluation of discriminant validity is a prerequisite factor for examining possible variables’ relationships. The goodness-of-fit index indicates that the model is well-fit. Through the established model, decision-makers and school administration would be able to implement measures that would effectively reduce cyber harassment among students and improve the digital media usage experience. Full article

Cyber sexual harassment has been increasing and has become a major public health problem among youths. Therefore, this study primarily aimed to evaluate the effects of a blended learning program on knowledge, attitudes, intention to cope with cyber sexual harassment, behavioral coping strategies, and cyber sexual harassment among female high-school students in Bangkok, Thailand. A quasi-experimental study with a two-group design was conducted from May 2021 to October 2021. A total of 112 students were recruited into either an intervention (n = 56) or a control (n = 56) group. The intervention group participated in a blended learning program for 12 weeks. A self-administered questionnaire was completed by both groups at pre-intervention, post-intervention, and follow-up. Chi-square test, Fisher’s exact test, and repeated measures analysis of variance (ANOVA) were used for data analysis. The mean score of the intervention group in knowledge, attitude, intention to cope with cyber sexual harassment, behavioral coping strategies, and cyber sexual harassment after completing the program and follow-up were significantly different from baseline and the control group (p < 0.05). A positive effect of the blended learning program was observed among female students. Therefore, this program can be useful for victims, supporting their self-confidence with decreased frequency of cyber sexual harassment experiences. Full article

It is an undeniable fact that people excessively rely on social media for effective communication. However, there is no appropriate barrier as to who becomes a part of the communication. Therefore, unknown people ruin the fundamental purpose of effective communication with irrelevant—and sometimes aggressive—messages. As its popularity increases, its impact on society also increases, from primarily being positive to negative. Cyber aggression is a negative impact; it is defined as the willful use of information technology to harm, threaten, slander, defame, or harass another person. With increasing volumes of cyber-aggressive messages, tweets, and retweets, there is a rising demand for automated filters to identify and remove these unwanted messages. However, most existing methods only consider NLP-based feature extractors, e.g., TF-IDF, Word2Vec, with a lack of consideration for emotional features, which makes these less effective for cyber aggression detection. In this work, we extracted eight novel emotional features and used a newly designed deep neural network with only three numbers of layers to identify aggressive statements. The proposed DNN model was tested on the Cyber-Troll dataset. The combination of word embedding and eight different emotional features were fed into the DNN for significant improvement in recognition while keeping the DNN design simple and computationally less demanding. When compared with the state-of-the-art models, our proposed model achieves an F1 score of 97%, surpassing the competitors by a significant margin. Full article

The increase in digital practices and networking has introduced important changes to social interactions. The extensive use of technology among young people has allowed for cyber communication, which has numerous benefits but can also trigger violence in relationships. Interpersonal violence affecting young people is becoming more widely recognized as a public health issue. The aim of this scoping review is to map and systematize the published academic literature on Cyber Interpersonal Violence (CIV) amongst young people, following the methodological approach proposed by Arksey and O’Malley. Five databases were searched: PubMed, Scopus, CINAHL (EBSCOhost), Science Direct and Social Sciences Citation Index. Eighteen studies in English, Portuguese, Spanish and French, published from 2004 onwards, were included. Three main areas arose in the CIV: cyber dating abuse, cyberbullying and cyber-harassment. Investing in prevention is the key to preventing cyber violence. Full article

Cyberbullying is a growing and significant problem in today’s workplace. Existing automated cyberbullying detection solutions rely on machine learning and deep learning techniques. It is proven that the deep learning-based approaches produce better accuracy for text-based classification than other existing approaches. A novel decentralized deep learning approach called MaLang is developed to detect abusive textual content. MaLang is deployed at two levels in a network: (1) the System Level and (2) the Cloud Level, to tackle the usage of toxic or abusive content on any messaging application within a company’s networks. The system-level module consists of a simple deep learning model called CASE that reads the user’s messaging data and classifies them into abusive and non-abusive categories, without sending any raw or readable data to the cloud. Identified abusive messages are sent to the cloud module with a unique identifier to keep user profiles hidden. The cloud module, called KIPP, utilizes deep learning to determine the probability of a message containing different categories of toxic content, such as: ‘Toxic’, ‘Insult’, ‘Threat’, or ‘Hate Speech’. MaLang achieves a 98.2% classification accuracy that outperforms other current cyberbullying detection systems. Full article

As the contemporary phenomenon of school bullying has become more widespread, diverse, and frequent among adolescents in Korea, social big data may offer a new methodological paradigm for understanding the trends of school bullying in the digital era. This study identified Term Frequency-Inverse Document Frequency (TF-IDF) and Future Signals of 177 school bullying forms to understand the current and future bullying experiences of adolescents from 436,508 web documents collected between 1 January 2013, and 31 December 2017. In social big data, sexual bullying rapidly increased, and physical and cyber bullying had high frequency with a high rate of growth. School bullying forms, such as “group assault” and “sexual harassment”, appeared as Weak Signals, and “cyber bullying” was a Strong Signal. Findings considering five school bullying forms (verbal, physical, relational, sexual, and cyber bullying) are valuable for developing insights into the burgeoning phenomenon of school bullying. Full article

Cyberbullying is a relatively new form of bullying, with both similarities and differences to traditional bullying. While earlier research has examined associations between school-contextual characteristics and traditional bullying, fewer studies have focused on the links to students’ involvement in cyberbullying behavior. The aim of the present study is to assess whether school-contextual conditions in terms of teachers’ ratings of the school leadership are associated with the occurrence of cyberbullying victimization and perpetration among students. The data are derived from two separate data collections performed in 2016: The Stockholm School Survey conducted among students in the second grade of upper secondary school (ages 17–18 years) in Stockholm municipality, and the Stockholm Teacher Survey which was carried out among teachers in the same schools. The data include information from 6067 students distributed across 58 schools, linked with school-contextual information based on reports from 1251 teachers. Cyberbullying victimization and perpetration are measured by students’ self-reports. Teachers’ ratings of the school leadership are captured by an index based on 10 items; the mean value of this index was aggregated to the school level. Results from binary logistic multilevel regression models show that high teacher ratings of the school leadership are associated with less cyberbullying victimization and perpetration. We conclude that a strong school leadership potentially prevents cyberbullying behavior among students. Full article