Assessment declaration checklist

Please carefully read the statements below and check each box if you agree with the declaration. If you do not check all boxes, your assignment will not be marked. If you make a false declaration on any of these points, you may be investigated for academic misconduct. Students found to have breached academic integrity may receive official warnings and/or serious academic penalties. Please read more about academic integrity here. If you are unsure about any of these points or feel your assessment might breach academic integrity, please contact your course coordinator for support. It is important that you DO NOT submit any assessment until you can complete the declaration truthfully.

By checking the boxes below, I declare the following:

• I have not impersonated, or allowed myself to be impersonated by, any person for the purposes of this assessment

• This assessment is my original work and no part of it has been copied from any other source except where due acknowledgement is made. Due acknowledgement means the following:

  • The source is correctly referenced in a reference list
  • The work has been paraphrased or directly quoted
  • A citation to the original work’s reference has been included where the copied work appears in the assessment.

• No part of this assessment has been written for me by any other person except where such collaboration has been authorised by the lecturer/teacher concerned.

• I have not used generative “AI” tools for the purposes of this assessment.

• Where this work is being submitted for individual assessment, I declare that it is my original work and that no part has been contributed by, produced by or in conjunction with another student.

• I give permission for my assessment response to be reproduced, communicated, compared and archived for the purposes of detecting plagiarism.

• I give permission for a copy of my assessment to be retained by the university for review and comparison, including review by external examiners.

I understand that:

• Plagiarism is the presentation of the work, idea or creation of another person or machine as though it is your own. It is a form of cheating and is a very serious academic offence that may lead to exclusion from the University. Plagiarised material can be drawn from, and presented in, written, graphic and visual form, including electronic data and oral presentations. Plagiarism occurs when the origin of the material used is not appropriately cited.

• Plagiarism includes the act of assisting or allowing another person to plagiarise or to copy my work.

I agree and acknowledge that:

• I have read and understood the Declaration and Statement of Authorship above.

• If I do not agree to the Declaration and Statement of Authorship in this context and all boxes are not checked, the assessment outcome is not valid for assessment purposes and will not be included in my final result for this course.

#INTRODUCTION

In the last decade, the way young people interact and communicate has shifted dramatically. Social media platforms, messaging apps, and online gaming spaces have opened new possibilities for self-expression and connection but they have also introduced new risks. Among them, cyberbullying has become one of the most alarming digital-age challenges, directly impacting the mental health and emotional well-being of millions of students worldwide.

Cyberbullying differs from traditional bullying because it follows victims beyond school walls. It can happen any time of day, often in private, and can reach a wide audience instantly. Studies by the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) reveal a consistent link between online harassment and increased levels of anxiety, depression, and suicidal ideation among teenagers. In some countries, one in five adolescents reports being bullied online, and nearly the same proportion admits to experiencing feelings of sadness or hopelessness afterward.

This dashboard visualizes real public health data primarily from the CDC Youth Risk Behavior Surveillance (YRBS) datasets (1991–2023) to explore how cyberbullying correlates with mental health outcomes, including suicidal planning and attempts among adolescents. Using R-based visualizations, the analysis aims to show how these patterns have evolved over time and across genders, highlighting the urgent need for preventive strategies, digital education, and emotional resilience programs in schools.

Rather than just showing numbers, this story invites reflection on what lies behind the data the lived experiences of young people growing up in a hyper-connected world. Each percentage represents real individuals facing online hostility, emotional isolation, and sometimes life-threatening consequences. By turning data into visual narratives, this project seeks to raise awareness and encourage meaningful action toward safer digital communities.

Code

The following code was used to improve the original.

Rows: 500
Columns: 15
$ `Text Content`                                 <chr> "I hate you so much!", …
$ `Image Content`                                <chr> "Cyberbullying cartoon"…
$ `Video Content`                                <chr> "Harassment video", "In…
$ `User Metadata (Age, Gender, Location)`        <chr> "15, Female, XYZ city",…
$ `Engagement Metrics (Likes, Shares, Comments)` <chr> "174, 23, 47", "68, 89,…
$ Timestamp                                      <dttm> 2023-11-01 09:01:40, 2…
$ `Interaction Network (Friends, Followers)`     <chr> "256, 729", "119, 1052"…
$ `Sentiment Score`                              <dbl> -0.50, -0.73, -0.51, -0…
$ `Text Length`                                  <dbl> 122, 179, 74, 108, 81, …
$ `Image Features (Object, Color)`               <chr> "Middle Finger, Black",…
$ `Video Features (Action, Duration)`            <chr> "Threatening, 11 second…
$ `Content Type`                                 <chr> "Post", "Post", "Messag…
$ `Interaction Type (Language)`                  <chr> "Spanish", "Spanish", "…
$ `Reported Flag`                                <chr> "No", "Yes", "Yes", "No…
$ `Reporting Frequency`                          <dbl> 1, 6, 2, 2, 7, 1, 6, 7,…

1. Bar Chart – Cyberbullying vs Suicide Planning

Description: This chart compares the percentage of students who reported being cyberbullied with those who reported making a suicide plan, across recent survey years (2019–2023). The bars show a visible upward trend in both categories, suggesting that as online bullying becomes more frequent, more young people are also experiencing severe emotional distress. It’s not just data each rise in the bar represents growing pain behind the screens. This pattern highlights the urgent need for digital empathy, stronger anti-bullying education, and early mental health intervention in schools.

2. Line Graph – Suicide Attempts Over Time

Description: This line chart tracks the change in suicide attempt rates among high school students over time. The steady climb in the curve reflects a heartbreaking reality: despite greater awareness about mental health, suicide attempts among youth continue to increase in recent years. The upward line is more than a statistic it’s a reminder that conversations about mental health must go beyond hashtags and awareness weeks. It calls for consistent, real-world support systems for young people in distress.

3. Scatter Plot – Relationship Between Cyberbullying and Suicide Planning

Description: Each dot on this scatter plot represents a survey year, comparing the percentage of students cyberbullied against those who planned suicide. The diagonal pattern reveals a strong positive correlation as cyberbullying rises, suicide planning rates rise too. This visualization turns an abstract problem into a visible connection, making it clear that online cruelty doesn’t stay online; it seeps into mental well-being and can lead to real-life consequences.

4. Correlation Heatmap – Mental Health Indicators

Description: The heatmap visualizes the strength of relationships between key indicators cyberbullying, sadness, suicidal planning, and attempts. Darker shades represent stronger correlations, showing which factors tend to occur together. For example, higher levels of sadness or hopelessness often appear alongside increased reports of bullying and suicidal thoughts. This graph ties together the emotional and behavioral data, reminding us that cyberbullying is rarely an isolated experience it intertwines deeply with a student’s mental and emotional health.

• Conclusion

The visualizations in this dashboard reveal more than just trends they expose a painful truth about growing up in the digital era. Cyberbullying, once brushed off as “just online drama,” has shown a powerful and dangerous connection to mental health struggles among adolescents. The rising rates of sadness, suicidal thoughts, and attempts mirror the increasing time young people spend in digital spaces that can either nurture or harm them.

What stands out most in the data is the clear pattern: when cyberbullying rises, so does emotional distress. Behind every percentage lies a teenager struggling with self-worth, isolation, or silent pain. These numbers remind us that mental health isn’t abstract it’s deeply human.

At the same time, these insights also bring hope. The very tools that spread harm online can also spread kindness, education, and awareness. Schools, parents, and digital communities can use data like this to design preventive programs, early interventions, and peer support systems that turn technology into a source of connection rather than despair.

Ultimately, this project highlights a critical truth: data can speak for those who often remain unheard. By translating numbers into stories, we move closer to understanding and addressing the real-world impact of cyberbullying one visualization, one conversation, and one act of empathy at a time.

References

The reference to the original data visualisation choose, the data source(s) used for the reconstruction and any other sources used for this assignment are as follows:

Campisi, S. C., Carducci, B., Akseer, N., Zasowski, C., Szatmari, P., & Bhutta, Z. A. (2020). Suicidal behaviours among adolescents from 90 countries: a pooled analysis of the global school-based student health survey. BMC Public Health, 20(1). https://doi.org/10.1186/s12889-020-09209-z

John, A., Glendenning, A. C., Marchant, A., Montgomery, P., Stewart, A., Wood, S., Lloyd, K., & Hawton, K. (2018). Self-Harm, Suicidal Behaviours, and Cyberbullying in Children and Young People: Systematic Review. Journal of Medical Internet Research, 20(4), e129. https://doi.org/10.2196/jmir.9044

Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073–1137. https://pubmed.ncbi.nlm.nih.gov/24512111/

YRBSS | Youth Risk Behavior Surveillance System | Data | Adolescent and School Health | CDC. (2020, October 27). Www.cdc.gov. https://www.cdc.gov/yrbs