https://github.com/mindful-ai-assistants/social-buzz-ai
Repository for the Integrated Project of the Social Networks and Marketing course at PUC-SP, focusing on AI-driven analysis and marketing strategies based on social media data
https://github.com/mindful-ai-assistants/social-buzz-ai
Last synced: 3 months ago
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Repository for the Integrated Project of the Social Networks and Marketing course at PUC-SP, focusing on AI-driven analysis and marketing strategies based on social media data
- Host: GitHub
- URL: https://github.com/mindful-ai-assistants/social-buzz-ai
- Owner: Mindful-AI-Assistants
- License: mit
- Created: 2025-08-07T12:13:03.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-08-07T18:04:38.000Z (3 months ago)
- Last Synced: 2025-08-07T19:23:26.084Z (3 months ago)
- Homepage:
- Size: 114 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
#
Social [Buzz AI]()
[**Course:**]() Humanistic AI & Data Science (4th Semester)
[**Institution:**]() PUC-SP
**Professor:** [Erick Bacconi]()
####
[](https://github.com/sponsors/Mindful-AI-Assistants)
## [What’s This ?]()
Welcome to [**Social Buzz AI**]() our MVP for cracking the code on social media trends and delivering laser-focused marketing hacks powered by data and AI. Built for the [Social Networks]() and [Marketing]() course at PUC-SP, this repo is the playground where we [prototype](), [test](), and evolve smart tools to help brands dominate digital spaces through real data-driven insights.
## Table of Contents
- [What’s This?](#whats-this)
- [Why It Matters](#why-it-matters)
- [Data-Driven Culture and CRM Data Quality]()
- [Project Objectives](#project-objectives)
- [What We’re Building](#what-were-building)
- [Repo Breakdown](#repo-breakdown)
- [Tech Stack & Tools](#tech-stack--tools)
- [Metrics & KPIs We Track](#metrics--kpis-we-track)
- [Getting Started — Move Fast, Start Simple](#getting-started--move-fast-start-simple)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Run](#quick-run)
- [Environment Setup](#environment-setup)
- [How to Use This Repo](#how-to-use-this-repo)
- [Important Notes & Best Practices](#important-notes--best-practices)
- [Scripts Overview](#scripts-overview)
- [`fetch_twitter_data.py`](#fetch_twitter_datapy)
- [`run_dashboard.py`](#run_dashboardpy)
- [`generate_reports.py`](#generate_reportspy)
- [Team Hustlers](#team-hustlers)
- [Contact & Support](#contact--support)
- [Appendix: Example Social Media Report Template (Summary)](#appendix-example-social-media-report-template-summary)
## [Why It Matters]()
Social networks shape consumer vibes daily. [Our mission ?]() Turn raw social data into actionable intel and hyper-targeted campaigns that scale fast, maximize reach, and boost [ROI]() (Return on Investment). By bridging social media analytics with AI, [we empower marketers]() with the intelligence to win attention, engagement, and conversions.
## [Repo Structure]()
```
social-buzz-ai/
│
├── README.md
├── requirements.txt
├── .env.example
├── .gitignore
│
├── config/
│ └── README.md
│
├── data/ # Raw and processed data (example: twitter_data.json)
│
├── docs/
│ ├── social_media_report_template.md
│ ├── dashboard_guide.md
│ └── auto_report.md # Generated by the generate_reports.py script
│
├── models/ # Serialized saved ML models
├── notebooks/ # Jupyter notebooks for exploratory analysis and modeling
│ └── exploratory_analysis.ipynb
│
├── presentation/ # Slides and presentation materials
│
├── scripts/
│ ├── fetch_twitter_data.py
│ ├── generate_reports.py
│ └── run_dashboard.py
│
└── tests/ # Unit and integration tests
```
## [Data-Driven Culture and CRM Data Quality]()
### Embracing a [True]() Data-Driven Culture
🚫 [**What NOT to do**]()
- Executives decide first, then look for data to support their decisions.
- Rely solely on instinct or gut feelings to make decisions.
✅ [**What TO do**]()
- Let data not only indicate *that* a decision is needed but also *which* decision is optimal, based on evidence.
- Managers and leaders must take responsibility to moderate and validate decisions using what data reveals, rather than personal biases or unsupported opinions.
A true data-driven culture transforms how decisions are made: it places evidence at the center of all strategic and operational choices. This means [building trust in data](), fostering [transparency](), and [encouraging teams]() to adopt analytic [thinking] as a core [mindset]().
🌱 Leaders play a critical role by modeling data-informed behavior and demanding accountability grounded in facts. When done right, data guides innovation, risk management, and performance, freeing organizations from guesswork and enabling faster, smarter responses to change.
### [Common Pitfalls to Avoid]()
[-]() Post-hoc justification with data ("data fishing") to back a preconceived decision.
[-]() Overconfidence in intuition ignoring analytical insights.
[-]() Lack of data literacy and accessibility, which leads to misuse or mistrust of data.
### [Data Quality Challenges in CRM Systems]()
Quality of CRM data critically affects the insights used to drive marketing, sales, and customer support efforts.
According to the 2022 study [*"The State of CRM Data Quality"*]() by Validity (a leading provider of email marketing intelligence and CRM data management):
- [**76%**]() of respondents rated the quality of their CRM data as "good" or "very good". Yet, many still see poor data quality as a barrier limiting new sales opportunities.
- [**79%**]() agree that data deterioration has accelerated at an unprecedented pace, driven notably by pandemic-related disruptions.
- [⚠️ **75%** 🙂]() admit [employees]() sometimes [fabricate data]() to tell the story [they want]() decision-makers to hear, rather than [reflect reality]().
- Meanwhile, [**82%**]() report pressure to find numbers supporting a specific narrative versus providing accurate, objective information.
These findings highlight a [paradox in CRM data management](): organizations recognize the importance of [data quality]() but [struggle]() with cultural and operational [challenges]() that [degrade trust]() and [utility]().
### [Key Takeaways for Social Buzz AI Project]()
[-]() Promote *data integrity* and discourage “storytelling” with selective or fabricated data.
[-]() Foster transparent data governance and regular audits to ensure CRM data accuracy and completeness.
[-]() Empower teams with tools and training to interpret CRM data critically, using it as a guide rather than a biased narrative.
[-]() Integrate data quality metrics in your dashboards to continuously monitor and improve CRM data health.
[-]() Align data-driven practices organization-wide—from executives to frontline users—to build a resilient, trustworthy foundation for marketing and AI initiatives.
### 🌱 By addressing [both]() the [behavioral shifts]() toward [authentic data-driven decision-making]() and acknowledging the persistent hurdles in [CRM data quality](), your project can help build [powerful, trustworthy]() tools that support smarter, evidence-based marketing strategies.
## [Contributing]()
Contributions welcome from psychoanalysts, AI researchers, linguists, and data scientists! Please:
- Suggest new simulations or visualizations
- Extend symbolic formalisms
- Develop educational materials or case studies
Submit issues or pull requests via GitHub.
## 💌 [Let the data flow... Ping Me !](mailto:fabicampanari@proton.me)
#### [Contact and Support]()
- For notebook files, detailed tutorials, or enhanced visualizations, please reach out.
- Interested in Python notebooks simulating these dynamics or advanced Humanistic AI models? Just ask!
####
🛸๋ My Contacts [Hub](https://linktr.ee/fabianacampanari)
###
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Copyright 2025 Mindful-AI-Assistants. Code released under the [MIT license.](https://github.com/Mindful-AI-Assistants/lacan-psychoanalysis-math-graphs/blob/28d9178584b831679dec129fb0aa040203ce0e9e/LICENSE.md)