ohmyilya / social-media-threats

Social Media Threat Analysis Automation - This is a research project. Please exercise caution when using automation tools, as automation may go against the terms of service of the platform. Always follow platform-specific guidelines and use automation responsibly.

Disclaimer

This is a research project. Please exercise caution when using automation tools, as automation may go against the terms of service of the platform. Always follow platform-specific guidelines and use automation responsibly.

Social Media Threat Analysis Automation

This repository provides a step-by-step guide to automate the process of monitoring and reporting concerning social media content from students in your district. This automation aims to identify behaviors such as mental health concerns, fights, cyberbullying, threats, drugs, weapons, inappropriate behavior, inappropriate in-school photos, etc., with a focus on maintaining student privacy and complying with relevant regulations.

Table of Contents

1. Data Collection
2. Content Filtering
3. Natural Language Processing (NLP)
4. Entity Recognition
5. Data Storage
6. Alerts and Notifications
7. Reporting
8. User Interface (Optional)
9. Compliance and Privacy
10. Regular Maintenance and Updates

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1. Data Collection

• Utilize social media APIs (e.g., Twitter API, Facebook Graph API) to gather publicly available posts from students in your district.
• Implement web scraping techniques for platforms without official APIs.
• Explore third-party social media monitoring tools for data collection.

2. Content Filtering

• Develop a list of keywords and phrases related to concerning categories.
• Implement content filtering algorithms to flag posts containing these keywords or exhibiting suspicious patterns.

3. Natural Language Processing (NLP)

• Utilize NLP libraries (e.g., NLTK, spaCy) to analyze sentiment within the content.
• Apply machine learning models for text classification to identify specific behaviors or threats accurately.

4. Entity Recognition

• Implement named entity recognition (NER) to identify partial or full student names within the content.
• Consider using NER libraries like spaCy or Stanford NER.

5. Data Storage

• Securely store the collected and analyzed data in a database (e.g., MySQL, PostgreSQL, MongoDB) for further analysis and reporting.

6. Alerts and Notifications

• Set up an alerting system to notify relevant staff or authorities when concerning content is detected.
• Notifications can be sent via email, SMS, or a dedicated dashboard.

7. Reporting

• Generate automated reports summarizing findings, including flagged content, student names, categories of concern, and observed trends.
• Utilize data analysis and visualization tools (e.g., Pandas, Matplotlib) for reporting.

8. User Interface (Optional)

• Develop a user-friendly interface (e.g., web application or dashboard) for authorized staff to review and take action on flagged content.

9. Compliance and Privacy

• Ensure the automation system complies with privacy regulations and guidelines.
• Establish clear policies for handling flagged content and protecting student privacy.
• Consult legal experts if necessary to ensure compliance with laws and regulations.

10. Regular Maintenance and Updates

• Continuously update the keyword list and improve NLP models to adapt to evolving online trends and language.
• Monitor system performance and ensure it remains effective in identifying and reporting concerning content.

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Please proceed with caution, ensuring ethical and privacy considerations are a top priority throughout the automation process. Be mindful of student privacy and adhere to all relevant legal requirements.