A study conducted in the United Kingdom in 2022 found that cyberstalking lasted for longer than two years for over 40 percent of victims. For 22 percent of victims, their experience of cyberstalking lasted between one and two years. Overall, other forms of online abuse continued for less than one month for a quarter of all victims in the UK. Furthermore, 75 percent of people reporting this crime were women.

In a January 2023 survey, when asked why they checked in on their current or former partner, ** percent of responding adults in the United States said it was because they did not trust them or noticed a change in their behavior. A further ** percent said they were just curious.

Stalking experienced by women and men, including numbers, type and personal characteristics, based upon annual findings from the Crime Survey for England and Wales.

During a January 2021 survey, it was found that ** percent of online harassment victims in the United States had been cyber bullied via Facebook. Twitter and Instagram ranked second with ** percent of responding victims stating that they had been harassed on each platform.

The most targeted age groups in online harassment Among adults, those aged between 18 and 29 years old are the most targeted group in cyber bullying. As of September 2020, more than ** percent of U.S. adults who use the internet reported having experienced some form of online harassment. Almost two-thirds of them were between 18 and 29 years old. More than ** percent of the respondents of that age group had experienced offensive name-calling online. According to the survey of U.S. adults, the next most targeted group in cyber bullying are people between 30-49 years old.

Reasons for online hate

There are various reasons why people bully others in the online environment. The most common one in the United States is political views. In 2020, ** percent of U.S. internet users who had experienced online hate and harassment said their political views were the reason. Physical appearance was among the most common reasons for online hate, ** percent of the time. Among other reasons were race and ethnicity, gender, religion, and occupation.

Who experiences harassment more often?

Among U.S. internet users, men tend to experience online harassment more than women. A survey from September 2020 showed that ** percent of male respondents had experienced some form of online harassment, whereas ** percent of female respondents reported having experienced at least one kind of online harassment. However, women experienced three times as much online sexual harassment compared to men. Similarly, women also experienced stalking more than men.

According to a survey conducted in the United Kingdom in 2022, the most common type of online abuse experienced by victims was cyberbullying, with 51 percent of respondents stating they had faced this type of harassment. Overall, 36 percent of respondents said that they had been trolled, and a third reported being victims of cyberstalking. Additionally, almost a fifth of those asked reported having experienced doxing.

We retrieved 7,547 tweets from comment section of Nigerian topmost influential twitter handles from July 12, 2022 to September 22, 2022. The acquired datasets of comments were then cleaned, keeping only the text (remove User handle, URL, emotional signs, etc.) and filtered to remove duplicated comments using Python.

Introduction

Social Media Mental Health Statistics: Social media has many uses, but it often causes the most harm to younger users. Teens face significant mental health issues due to social media, and the COVID-19 pandemic made things worse by increasing screen time and social media use. This created more opportunities for teens to encounter online problems, worsening the situation.

Teens also use social media to find communities and interest groups, watch live streams, and support good causes. It’s important to US teens that they feel welcome and safe online. Despite some problems, social media offers many chances for connection and entertainment. We shall shed more light on the Social Media Mental Health Statistics through this article.

As social media usage becomes increasingly prevalent in every age group, a vast majority of citizens rely on this essential medium for day-to-day communication. Social media’s ubiquity means that cyberbullying can effectively impact anyone at any time or anywhere, and the relative anonymity of the internet makes such personal attacks more difficult to stop than traditional bullying.

On April 15th, 2020, UNICEF issued a warning in response to the increased risk of cyberbullying during the COVID-19 pandemic due to widespread school closures, increased screen time, and decreased face-to-face social interaction. The statistics of cyberbullying are outright alarming: 36.5% of middle and high school students have felt cyberbullied and 87% have observed cyberbullying, with effects ranging from decreased academic performance to depression to suicidal thoughts.

In light of all of this, this dataset contains more than 47000 tweets labelled according to the class of cyberbullying:

Age; Ethnicity; Gender; Religion; Other type of cyberbullying; Not cyberbullying The data has been balanced in order to contain ~8000 of each class.

Trigger Warning These tweets either describe a bullying event or are the offense themselves, therefore explore it to the point where you feel comfortable.

This dataset aims to combat the significant rise of cyberbullying, which has been exacerbated by increased social media usage and global events such as the COVID-19 pandemic. Its primary purpose is to enable the creation of models that can automatically flag potentially harmful tweets and help identify patterns of online hatred. As social media is now an essential medium for communication across all age groups, cyberbullying can affect individuals anywhere and at any time, with the internet's relative anonymity making it particularly difficult to stop. The dataset contains over 47,000 tweets that have been carefully labelled according to various types of cyberbullying, including Age, Ethnicity, Gender, Religion, Other type of cyberbullying, and Not cyberbullying. The data has been balanced to contain approximately 8,000 entries for each class, making it suitable for robust model training. This resource is vital given the alarming statistics, where a substantial percentage of students have experienced or observed cyberbullying, leading to severe effects from decreased academic performance to suicidal thoughts.

Columns

• tweet_text: The full text content of the tweet.
• cyberbullying_type: The specific category of cyberbullying harassment identified within the tweet.

Distribution

The dataset comprises more than 47,000 individual tweets, with the data meticulously balanced to ensure approximately 8,000 entries for each defined cyberbullying category. While the typical file format for such data is CSV, a sample file would be available separately on the platform. The structure is designed for multi-class classification tasks, categorising tweets into distinct types of cyberbullying or marking them as not cyberbullying.

Usage

This dataset is ideally suited for a variety of applications, including: * Developing multiclassification models to accurately predict the specific type of cyberbullying present in a tweet. * Building binary classification models to effectively flag tweets that are potentially harmful. * Conducting exploratory data analysis to identify and understand the words, phrases, and linguistic patterns associated with each type of cyberbullying.

Coverage

The dataset's scope is global, reflecting the worldwide reach of social media. While a specific time range is not detailed, the data's context relates to an increased risk of cyberbullying during the COVID-19 pandemic, with the original research paper dating from late 2020, suggesting content from around that period. The data covers general social media discourse on Twitter and is relevant to anyone using social media platforms, though statistics highlight the impact on middle and high school students.

License

CC BY

Who Can Use It

This dataset is particularly valuable for: * Data scientists and machine learning engineers looking to train and evaluate models for natural language processing (NLP) tasks related to content moderation and sentiment analysis. * Academics and researchers focusing on social issues, digital humanities, and the impact of online communication. * Organisations and developers aiming to build tools or systems for identifying and combating cyberbullying on social media platforms.

Dataset Name Suggestions

• Cyberbullying Tweets Classification
• Social Media Cyberbullying Detector
• Twitter Bullying Classification
• Fine-Grained Cyberbullying Dataset

Attributes

Original Data Source: Cyberbullying Classification

The Report is divided into two parts: the first part of the report addresses the issue of cyberbullying and includes information relating to the scope of the problem, the impact of cyberbullying on victims, existing legislative and policy responses and options for Criminal Code reform to address the issue. The second part of the Report addresses the issue of the non-consensual distribution of intimate images and contains information about the scope of the problem, existing Criminal Code responses and options for a new Criminal Code offence. The Working Group recommends that the Criminal Code be amended to modernize certain existing offences to deal with harassment through electronic media, as well as the investigative powers for law enforcement, to ensure that all acts of cyberbullying carried out through the use of new technologies can be effectively investigated and prosecuted. The Working Group recommends that a new criminal offence addressing the non-consensual distribution of intimate images be created, including complementary amendments relating to, for example, the forfeiture of items used in the commission of the offence and restitution to permit the victim to be compensated for any costs associated with having the images removed from the Internet.

A December 2019 found that young U.S. adults were most likely to engage in cyber stalking activities and deem them to be harmless. Overall, ** percent of respondents aged 18 to 34 years reported that they had engaged in cyber stalking their ex or current partner. Additionally, ** percent of U.S. adults aged 35 to 44 reported the same thing. Only ** percent of U.S. adults aged 65 and older cyber stalked their ex or current partner online.

The Basque Youth Observatory is an instrument of the Basque Government that allows to have a global and permanent vision of the situation and evolution of the youth world that allows to evaluate the impact of the actions carried out in the CAPV by the different administrations in the field of youth.The Basque Youth Observatory regularly publishes more than 100 statistical indicators that can be consulted in euskadi.eus, along with other research and reports. Statistics are provided in various formats (csv, excel).

O presente protocolo de pesquisa tem como objetivo geral verificar quais traços de personalidade (Big Five e sombrios) e valores humanos podem explicar o comportamento de cyberstalking. Para tanto, foram propostos quatro estudos, que correspondem a quatro artigos distintos. O primeiro objetivou adaptar, reunindo evidências psicométricas da Intimate Partner Cyberstalking Scale (IPCS) em contexto brasileiro. Em dois estudos (N = 467), foram avaliados os parâmetros psicométricos da Intimate Partner Cyberstalking Scale (IPCS) por meio de diferentes técnicas [e.g., Análise Fatorial Confirmatória (AFC), Teoria de Resposta ao Item], além de propor uma versão curta da escala. Ambas as versões apresentaram uma estrutura unidimensional confiável e apresentaram correlações semelhantes com maquiavelismo, psicopatia e narcisismo. O segundo artigo objetivou adaptar a Assessment of Sadistic Personality (ASP) para o Brasil, averiguando as qualidades psicométricas do instrumento. Foram realizados dois estudos com participantes de diferentes estados brasileiros. Por meio de dois estudos 1 (n = 467), foi realizada a adaptação da ASP e executada uma análise fatorial exploratória e AFC, que sugeriram uma estrutura unifatorial. No Estudo 2, (n = 225) a análise fatorial confirmatória apontou indicadores adequados, além de reunidas evidências de validade convergente da ASP com a tríade sombria e o cyberstalking. O terceiro artigo propôs verificar o poder preditivo dos traços de personalidade, controlando a variável sexo, nos comportamentos de perpetração do cyberstalking. Participaram 267 pessoas de diferentes estados brasileiros (Midade= 22,83). Foram aplicados a IPCS, o Inventário dos Cinco Grandes Fatores da Personalidade e questões sociodemográficas. Os resultados mostraram, por meio da regressão múltipla hierárquica, que o traço de personalidade neuroticismo explicou o comportamento de perpetração do cyberstalking e que a variável sexo não demonstrou influenciar nessa relação; entretanto, demonstrou que pessoas com níveis elevados de instabilidade emocional podem exibir condutas de perpetração do cyberstalking. Por fim, o quarto artigo objetivou conhecer em que medida os valores humanos poderiam mediar a relação entre a personalidade sombria e o comportamento perpetrador de cyberstalking em relacionamentos íntimos. Participaram 316 pessoas de diferentes regiões brasileiras (Midade = 26,92). Estas eram em maioria mulheres (57,9%), que estavam namorando (42,1%) ou eram casadas (24,1%), que responderam os instrumentos: IPCS, Dark Triad Dirty Dozen, Assessment of Sadistic Personality, o Questionário de Valores Básicos e questões sociodemográficas. As correlações e regressões possibilitaram testar um modelo mediação, que apontou que valores pessoais foram mediadores (mediação parcial) da relação entre narcisismo e o comportamento perpetrador do cyberstalking. Reforça-se a importância dos traços sombrios da personalidade sombria (narcisismo) na explicação do cyberstalking em relacionamentos íntimos, sendo meritório considerar a inclusão dos valores pessoais (experimentação) para ampliar a rede monológica associada a esta conduta, visando entender de forma sistemática suas causas e consequências.

Using a multi-methods research design, this study classified the contents of cyberbullying messages, measured their frequency and associations with offline bullying, and examined whether and how peer groups in social networks promote these behaviors. Beginning in January 2015, 164 adolescents from 2 Iowa middle schools, grades 6 through 8, were surveyed. Two surveys, one at the start of the spring semester and one at the end of spring 2015, gathered self-reported information on perpetration, victimization, and witnessing of online and offline bullying and the structure of peer networks. A total of 77 students furthermore participated in an electronic capture period from January through May 2015. Participant smartphones were equipped with an application that collected incoming and outgoing text messages and Facebook and Twitter activity, and also surveyed them weekly about their bullying experiences. Demographic information collected included age, grade, gender, ethnicity, parents' marital status, household composition, religiosity, and socioeconomic status.

We present the first dataset for the Polish language containing annotations of harmful and toxic language. The dataset was created to study harmful Internet phenomena such as cyberbullying and hate speech, which have dramatically gained in numbers in recent years both on Polish Internet as well as worldwide. The dataset was automatically collected and annotated in two ways. Firstly, by two trained layperson volunteers under the supervision of a cyberbullying and hate-speech expert. To improve the quality of annotations, the second turn of annotations was performed by a group of trained expert annotators specializing in the annotation of cyberbullying and hate-speech data, which was also additionally supervised by an additional experienced expert annotator (or super-annotator). We initially utilize the dataset in the classification of cyberbullying in Polish. In particular, the dataset is utilized in two tasks: 1) binary classification of harmful and non-harmful messages, and 2) multi-class classification between two types of harmful information (cyberbullying and hate speech), and others. Apart from the dataset itself, we also share the classification model which achieved the highest classification results for the dataset to be freely applied by third parties in cyberbullying prevention architectures.

This new data in Excel file were from a study regarding cyberbullying victimization, resilience and depression among university students in Saudi Arabia. The data are valuable as it shows the trend of cyberbullying behaviour among university students and it relates to psychological problems such as depression. However, having resilience can buffer this negative effect. The data were collected using questionnaire. Results showed significant positive correlations between cyberbullying victimization and depression, and a significant negative correlation between resilience and depression. This means that if students have higher resilience, they obtain lower scores in depression.

This statistic illustrates the share of men experiencing different types of cyber bullying in the Netherlands in 2019, by type. In the mentioned period, 0.6 percent of men in the Netherlands indicated they have received online threats of violence.

Abstract Information and communication technologies are gaining prominence as influencers of cognitive performance, given the speed with which information is generated and disseminated, changing personal and professional relations. If, on the one hand, this provides evolution and transformation, on the other, it can cause harm, such as cyberbullying – violence that silently hurts people. Such scenario requires reflection and bioethical action. This study sought to understand the perception of cyberbullying in professional and personal daily life. This exploratory and quantitative research by convenience was carried out with 35 public servants from the Federal Institute of Education, Science and Technology of the South of Minas Gerais. Results were discussed based on the literature about information and communication technology, digital violence and, mainly, bioethics.

Cyberbullying is a problem with a high prevalence and can cause a wide variety of issues (anxiety, depression, suicidal ideation, loneliness, academic failure, among others). Given the scarcity of research in Peru, this study aims to explore the prevalence and the ways in which cyberbullying manifests itself in university students. The methodology was survey-based, adapting a reduced version (in Spanish) of the European Cyberbullying Intervention Project Questionnaire (ECIP-Q), applied to a non-probabilistic sample of 1,258 students. The analysis integrated descriptive statistics and an evaluation of the psychometric properties of the instrument. The results reveal a high prevalence of cyberbullying and a strong relationship between being both a victim and a perpetrator. The negative impact it has on students underscores the need for universities to be aware of and intervene in this issue.

How many people use social media? Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

Who uses social media? Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

How much time do people spend on social media? Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

What are the most popular social media platforms? Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.