Predicting Suicide Intuition in Online Social Network

ABSTRACT:

The project “Predicting Suicide Intuition in Online Social Network” aims to develop a social networking platform using Java, JSP, CSS, and JavaScript, with MySQL as the database. The platform incorporates basic social networking features, such as user profile management, following other users, and posting tweets. The primary objective of this project is to identify and flag posts that may indicate suicidal tendencies, thereby providing a preventive measure against potential self-harm.

Users on the platform can engage in typical social networking activities, including viewing profiles, following other users, and sharing posts. The admin interface is designed with enhanced capabilities to monitor user activities. Admins can access a comprehensive view of all users and their posts. Crucially, the system is equipped with a specialized feature that identifies posts containing suicide-related content. This functionality is achieved by matching user posts against a predefined list of suicide-related keywords stored in the database.

When a match is found, the post is flagged and classified as a potential suicide-related post, which is then displayed in a separate menu accessible to the admin. This classification allows for timely intervention and support for users expressing distress. The project’s integration of automated content monitoring with administrative oversight aims to create a safer online environment and potentially save lives by enabling early detection of suicidal expressions.

This project underscores the importance of mental health awareness and proactive measures within online communities. By leveraging technology to monitor and address suicide-related content, the platform aspires to contribute to the broader efforts of suicide prevention and mental health support in the digital age.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

The existing system for monitoring and managing online social networks encompasses a variety of platforms, each with their own unique set of features and functionalities. These systems generally provide users with capabilities to create profiles, follow other users, and post updates, similar to traditional social networking sites such as Facebook, Twitter, and Instagram.
In the existing systems, users can: Create and customize personal profiles, including photos, bios, and personal information. Connect with other users by following their profiles, sending friend requests, and forming social connections. Post updates, which can include text, images, videos, and links, to share their thoughts, activities, and interests with their network. Interact with other users’ posts through likes, comments, shares, and other forms of engagement.
In the existing system, there is no option of monitoring user activities to ensure compliance with community guidelines and terms of service. In the existing system, only if the user makes report of particular post for inappropriate or harmful behavior option is available.
While these existing systems are robust in terms of social networking functionalities and content moderation, they primarily focus on general platform safety and user experience. The monitoring of specific, sensitive content, such as suicide-related posts, may not be as specialized or prioritized in these platforms. This creates a need for more targeted approaches to identifying and addressing mental health issues within online communities.

DISADVANTAGES OF EXISTING SYSTEM:

Despite the robust features and functionalities provided by current social networking platforms, there are several notable disadvantages when it comes to addressing sensitive content such as suicide-related posts:
Generalized Content Moderation: Existing systems primarily employ broad content moderation strategies that focus on removing explicit content, hate speech, and general violations of community guidelines. These approaches may not effectively identify nuanced or indirect expressions of suicidal thoughts and behaviors.
Limited Detection Capabilities: While some platforms use machine learning and natural language processing to filter harmful content, these tools may not be specifically trained to detect suicide-related posts. This can lead to missed signals and insufficient intervention for users at risk.
Inconsistent Human Moderation: The reliance on human moderators can result in inconsistencies in identifying and handling suicide-related content. Moderators may lack specialized training in mental health, leading to variable responses and potential oversight.
Delayed Intervention: Due to the volume of content generated on social networks, there can be significant delays in detecting and responding to posts indicating suicidal intent. This lag can be critical in situations where timely intervention is essential.
Lack of Specialized Tools: Existing platforms may not provide specific tools or features for users to seek help or for administrators to manage mental health crises. This absence of dedicated resources limits the effectiveness of current systems in supporting users in distress.
Privacy Concerns: Enhanced monitoring and detection mechanisms can raise privacy issues. Users may feel uncomfortable with the level of surveillance required to effectively identify suicide-related posts, potentially leading to trust issues and decreased platform engagement.
Inadequate Support Structures: Current systems often lack integrated support structures such as direct links to mental health resources or crisis intervention services. Without these connections, users who are identified as at-risk may not receive the necessary support and assistance promptly.
Reactive Rather Than Proactive: Many existing systems are designed to react to reports and detected violations rather than proactively seeking out and addressing emerging signs of distress. This reactive approach may not be sufficient to prevent potential harm before it occurs.
These disadvantages highlight the need for more specialized and proactive systems designed to identify and respond to suicide-related content effectively, ensuring timely intervention and support for vulnerable users.

PROPOSED SYSTEM:

The proposed system, “Predicting Suicide Intuition in Online Social Network” aims to address the limitations of existing social networking platforms by integrating specialized features for detecting and managing suicide-related content. This system is developed using Java for backend functionality, JSP for dynamic web pages, CSS for styling, JavaScript for interactive elements, and MySQL for database management.
The proposed system platform provides a user-friendly interface where individuals can create profiles, follow other users, and post updates similar to conventional social networking sites. This includes viewing profiles, posting tweets, and engaging with other users’ content through likes and comments.
In the proposed system, the system includes a comprehensive admin dashboard that offers enhanced monitoring capabilities. Admins can view all user profiles and posts, ensuring they have a complete overview of the platform’s activity.
A core feature of the proposed system is its ability to identify and classify posts containing suicide-related content. This is achieved through a predefined list of suicide-related keywords stored in the database. When a user post matches these keywords, it is flagged and classified accordingly.
The system maintains a separate menu within the admin dashboard specifically for suicide-related posts. This dedicated monitoring area allows admins to focus on and review potentially harmful content promptly.
MySQL is used to manage user data, posts, and the list of suicide-related keywords. The database ensures efficient storage, retrieval, and matching of posts against the keyword list.
The platform employs an automated mechanism to scan and match user-generated posts with the suicide-related keywords. This process helps in quickly identifying posts that require further attention from the admins.
The proposed system is designed to be scalable, allowing it to handle an increasing number of users and posts without compromising performance. It also supports potential integration with external mental health resources and crisis intervention services.
By incorporating these features, the proposed system aims to provide a more focused approach to detecting and managing suicide-related content on social networking platforms, ensuring timely identification and intervention for at-risk users.

ADVANTAGES OF PROPOSED SYSTEM:

The proposed system “Predicting Suicide Intuition in Online Social Network” offers several significant advantages that address the shortcomings of existing social networking platforms and enhance the ability to detect and manage suicide-related content effectively.
Targeted Content Detection: The system’s focus on identifying suicide-related posts through a predefined keyword list ensures more precise detection of potentially harmful content. This targeted approach increases the likelihood of catching subtle or indirect expressions of suicidal thoughts that might be missed by general content moderation tools.
Timely Intervention: By automatically flagging and classifying posts with suicide-related keywords, the system enables quicker identification and response. This timely intervention is crucial in preventing potential self-harm and providing immediate support to at-risk users.
Dedicated Monitoring for Suicide-Related Content: The separate menu for suicide-related posts in the admin dashboard allows administrators to efficiently monitor and review flagged content. This dedicated focus ensures that critical posts receive the attention they need without being overlooked in the broader stream of user activity.
Enhanced Admin Capabilities: The comprehensive admin dashboard with a clear overview of user activities and flagged posts enhances the ability of administrators to manage the platform effectively. This centralized control supports better oversight and swift action when necessary.
Automated Matching Mechanism: The automated scanning and matching of posts against the suicide-related keyword list reduce the reliance on human moderators for initial detection. This automation improves efficiency and consistency in identifying concerning content.
Proactive Approach to Mental Health: The system’s proactive detection of suicide-related content signifies a commitment to mental health awareness and support. By actively monitoring for signs of distress, the platform can play a role in early intervention and prevention efforts.
Scalability: Designed to be scalable, the proposed system can handle an increasing number of users and posts without performance degradation. This scalability ensures that the platform remains effective as its user base grows.
Integration with Support Services: The potential for integrating with external mental health resources and crisis intervention services enhances the support available to users. When a suicide-related post is flagged, admins can take immediate steps to connect the user with appropriate help.
Improved User Trust and Safety: By implementing specialized features to detect and manage suicide-related content, the platform can foster a safer and more supportive online environment. This focus on user well-being can build trust and encourage more responsible and engaged community interactions.
Data-Driven Insights: The system’s ability to track and analyze suicide-related posts can provide valuable data for understanding trends and patterns in user behavior. These insights can inform further improvements in platform policies and mental health interventions.
Overall, the proposed system offers a comprehensive, proactive, and efficient approach to managing suicide-related content on social networks, prioritizing user safety and mental health.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED.
Input Devices : Keyboard, Mouse.
Ram : 4 GB.

SOFTWARE REQUIREMENTS:

Operating system : Windows 10/11.
Coding Language : JAVA.
Frontend : JSP, HTML, CSS, JavaScript.
JDK Version : JDK 21.
IDE Tool : Apache Netbeans IDE 20.
Tomcat Server Version : Apache Tomcat 9.0.84
Database : MYSQL.

Java Projects

Predicting Suicide Intuition in Online Social Network