AI Fake News Detection System - 03/09/2026 05:18 EDT

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@glanegonsalves

Hi, Glane here. I can build this as an end to end Python fake-news detection system, using a transformer such as BERT/RoBERTa with PyTorch or TensorFlow, alongside Scikit-learn for evaluation and supporting data processing. I’ll set up the article ingestion/cleaning pipeline, train and evaluate the model with proper train/validation/test separation, and report accuracy, precision, recall, F1, confusion matrix, and class-wise performance rather than relying on accuracy alone. I’ll also provide a lightweight REST API or web demo via Streamlit that returns the predicted probability and interpretable signals/features behind the classification, with Docker support for deployment. The complete delivery will include the source code, trained weights, requirements, README, reproducible training/evaluation scripts, deployment configuration, and detailed performance report. I’ll pay particular attention to preventing data leakage and ensuring the reported ≥90% accuracy is measured on an independent validation/test set rather than an artificially favorable split. I can also share the proposed architecture and sample outputs before moving into the full implementation.

@vishal1145

Your 90% accuracy target will fail in production if the model cannot explain why it flagged an article as fake. Regulators and users demand interpretability - a black-box classifier that just outputs a probability score creates liability when it misclassifies breaking news during a crisis. Quick questions - are you planning to retrain the model periodically as misinformation tactics evolve, or is this a one-time deployment? And do you have labeled training data already, or do we need to curate a dataset from sources like LIAR, FakeNewsNet and ISOT? Here is the architectural approach: - TRANSFORMER FINE-TUNING: Deploy DistilBERT with LIME explainability so each prediction surfaces the exact phrases that triggered the fake-news score, meeting your transparency requirement while staying under 200ms inference time. - DATA PIPELINE: Build a Scrapy-based ingestion system with deduplication logic and text normalization that feeds PostgreSQL, ensuring your training set stays clean and your API can handle 1000+ articles per hour without choking. - REST API DEPLOYMENT: Package the model in FastAPI with Docker Compose so you can scale horizontally on AWS ECS or your own server, plus automated CI/CD tests that validate precision/recall thresholds before any new model version goes live. I've built similar NLP systems for a healthcare client detecting medical misinformation at 94% F1 score. Let's schedule a 20-minute call to align on dataset strategy before you commit to a build.

@pinkeegupta

Hi, I have reviewed your project requirements and I’m confident I can deliver accurate, data-driven, and scalable solutions for your needs. I bring 9+ years of combined experience in Python development, Data Science, Data Analytics, and Business Intelligence, helping clients turn raw data into meaningful insights and actionable dashboards. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju Logical Soft Tech Pvt Ltd, Indore(M.P)

@ziedb4

Hello, The stack you describe is what I build daily: Python ingestion pipelines, transformer fine-tuning, and FastAPI services shipped in Docker. Proposed structure: 1. Ingestion: scraper/ingest layer with cleaning and deduplication, storing articles in a structured schema ready for inference or retraining. 2. Model: fine-tuned transformer (DistilRoBERTa or similar) on a curated fake-vs-real corpus, with a held-out split plus a second out-of-source split to check it generalises. 3. API: FastAPI endpoint returning falseness probability with token-level attributions, so each flag is explainable rather than a bare score. 4. Delivery: Dockerfile, weights, reproducible train/eval scripts, and a report with confusion matrix, precision, recall and F1 per class. One technical point worth raising before you pick anyone. On the public fake-news datasets, 90%+ accuracy is trivial — models often exceed 98%. But much of that comes from source artefacts (formatting, punctuation, outlet-specific style), not from truthfulness, and accuracy drops sharply on freshly scraped articles from other outlets. So the number that matters is accuracy on the test set you'll actually evaluate with. Can you tell me where that test set comes from — the same dataset as training, or independently collected articles? I'd rather agree on a realistic, honest target upfront than promise 90% and have it fail on your real data.

@rohank2502

You need English articles scored for fakery, with visible reasons, and at least 90 percent accuracy on stories the system has never seen. I can start right now. Within 24-48 hours you get a live sample: paste a story, see the fake chance, the reasons, and a first accuracy snapshot. Then we add fresh-article intake, a simple page to try it, the files to rebuild the scorer, and a report with accuracy, precision, recall, and the mix-up table. Want me to score a first batch from the news sites you care about so the sample matches your feed?

@iterativeTech

As an AI/ML Specialist and Cloud Data Engineer, I bring a wealth of experience and a mission-driven approach to address your sophisticated problem. Over my career, I've driven measurable ROI for numerous organizations by leveraging robust AI/ML solutions complemented by scalable cloud data engineering - exactly what your project mandates. My skill-set aligns perfectly with your project needs: fine-tuning BERT-style models to combat misinformation, dealing with large datasets, and delivering production-ready code. My versatile technology stack - PyTorch, TensorFlow, Scikit-learn to name a few - assures a robust NLP model design that's efficient, interpretable, and optimized for real-time inference. Additionally, my experience with REST APIs and Docker will make the system installation and deployment process seamless. Crafting intelligent systems whose technical decisions align with business outcomes is my forte. I understand your goals of higher operational efficiency and cost reduction by identifying news that are factually incorrect. My approach is meticulous, from data ingestion to model training culmination in deployment scripts or Dockerfile. I guarantee you not just a powerful system but also uncompromised modularity in code allowing easy set-up or future enhancement.

@indreshpnk

Hello, I understand you need a production-ready AI/ML system that automatically analyzes English news articles and detects likely fake content with at least 90% validation accuracy. The goal is to deliver a reliable, explainable, and deployable solution with complete training, evaluation, and API workflow. Here’s what I can provide: Automated Python pipeline for article ingestion/scraping, cleaning, preprocessing, and structured storage. Transformer-based NLP model such as BERT/RoBERTa fine-tuned for fake-vs-real news classification using PyTorch or Scikit-learn. REST API/web demo returning falseness probability with explainable features and Docker-based deployment. I bring 4+ years of experience in Python, Machine Learning, NLP, Data Processing, Web Scraping, and REST API development, with a focus on building scalable and maintainable solutions. I’ll also provide reproducible training scripts, model weights, README, requirements, confusion matrix, precision, recall, F1, and accuracy evaluation. Just to clarify: Do you already have a preferred fake-news dataset, or should I curate one? Do you prefer BERT, RoBERTa, or should I select the best-performing model? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@camilob44

You want me for this because I own every layer a fake-news detector actually needs: the pipeline that scrapes and cleans the articles, the model that scores them, the API that serves the result, and the Docker setup to run it on your server. A notebook-only bidder hands you a metric; I hand you a service you can run yourself and re-train as the news shifts. I build this kind of pipeline end to end: a Go pipeline that parsed thousands of documents and emails into clean structured records in Postgres, and a ClickHouse job that collapsed 21.7M raw rows into 458K correct ones. The ingest-clean-store half of your brief is my daily work, paired with transformer fine-tuning in PyTorch. One straight point on the 90% target, because it decides the project. 90% on a held-out split of one dataset is very reachable; 90% on genuinely independent, freshly-scraped articles is a much harder bar, since these models overfit the quirks of their training source. So milestone one is a measured baseline: fine-tune a BERT-style model and report accuracy, precision, recall and F1 on a proper split and on fresh articles, so we see the real number before scaling. Rate is Rs 1,000/hr, and I'll share architecture and sample outputs early.

@abhilashr53

You want a production-ready fake news detector that hits 90%+ accuracy with full transparency on precision, recall, and F1, deployable on your own server. Here's the plan. Stack: Python, HuggingFace Transformers (RoBERTa fine-tuned on LIAR/ISOT datasets), FastAPI for the REST layer, SHAP for feature explainability, Docker for deployment. The pipeline scrapes via Newspaper3k, cleans and stores structured article data, runs inference, and returns falseness probability plus top contributing features per prediction. I've built NLP classification systems with transformer fine-tuning and explainability layers, and have packaged ML services in Docker with reproducible training scripts. Based on similar work, 90%+ accuracy on English fake news benchmarks is consistently achievable with RoBERTa fine-tuning. Delivery in three phases: ingestion pipeline and data prep (days 1-3), model training, evaluation report with confusion matrix/F1 (days 4-8), API plus Docker deployment with README (days 9-12). Total: approximately 12 days. One question before I scope the final quote: do you have a preferred news source list for scraping, or should I design the ingestion layer to be configurable for any RSS/URL feed?

@webmaster020

I can build your production-ready AI Fake News Detection System using Python, PyTorch, and a fine-tuned transformer architecture like BERT to achieve your target of 90 percent accuracy. The solution includes an automated ingestion pipeline to scrape, clean, and structure English news articles, a robust model training and evaluation script that reports precision, recall, F1-score, and confusion matrices, and a lightweight REST API demo returning falseness probabilities alongside decision transparency features. The project will be delivered with a complete requirements file, a clear setup guide, and a Dockerfile so you can easily run and deploy the service on your own server. Every component will be modular, thoroughly commented, and fully optimized to guarantee seamless performance on your independent validation dataset from day one.

@gncdev

Hi — here is exactly how I would hit the 90% requirement, plus one honest caveat. Approach 1) Data: combine LIAR, FakeNewsNet and the Kaggle real/fake corpora, dedupe by title+body hash, and hold out a SOURCE-DISJOINT validation set (no publisher appears in both train and validation). This matters: models trained on the usual random split score 99% and then collapse on unseen outlets. 2) Model: fine-tune DistilBERT/RoBERTa in PyTorch, class-weighted loss, early stopping on validation F1. You get accuracy, precision, recall, F1 and the confusion matrix per class, not just one headline number. 3) Transparency: the API returns the falseness probability plus the top contributing tokens (SHAP / attention), so every verdict is inspectable. 4) Pipeline: Python ingest (requests + trafilatura) -> cleaning -> structured store -> FastAPI /predict -> Dockerfile, so "docker compose up" is the only setup command. README with pinned requirements and scripts to reproduce training and evaluation. Honest note: >=90% on a held-out split of the same distribution is realistic and I will commit to it. On outlets never seen in training, expect lower — I will report both numbers rather than show you only the flattering one. I build production Python scraping and API services every week. Before you commit, I can send the architecture sketch and a first confusion matrix so you are judging results, not promises.

@renoa99

Hi, I can build your end-to-end fake-news detection system in Python, from article ingestion and preprocessing through transformer-based classification, evaluation, and API deployment. I have experience with Python, NLP, PyTorch, Scikit-learn, data processing, web scraping, machine learning, REST APIs, and production ML workflows. What I’ll deliver: * Automated news ingestion/scraping pipeline * Text cleaning, preprocessing & structured storage * BERT-style transformer fine-tuning * Curated fake-vs-real dataset preparation * Train/validation/test split with reproducible training * Accuracy, precision, recall, F1 & confusion matrix * Probability score and explainable prediction features * REST API or lightweight web demo * Dockerfile and deployment configuration * Complete source code, model weights & README I’ll avoid data leakage and validate the model on an independent test set. The **90%+ accuracy target will be measured on the agreed validation dataset**, with precision and recall reported transparently rather than overstating performance. I can start immediately and provide the initial architecture and model approach for review. Best regards, Reno

@karthikresonite

Dear Client, At Resonite Technologies, we understand the critical need for an AI/ML solution that effectively detects fake news. Our proven team specializes in developing production-ready systems tailored to client requirements. With extensive experience in NLP and machine learning, we can deliver the AI Fake News Detection System that meets your specifications. We propose a streamlined workflow: 1. Automated Pipeline: We'll create a robust pipeline to scrape, clean, and structure content from news websites. 2. NLP Model: Leveraging transformer-based architectures, we will fine-tune a model on a curated dataset, ensuring accuracy and reliability, with precision and recall metrics reported. 3. User Interface: A REST API or web demo will be developed to provide transparency, returning falseness probabilities and key influencing features. Deliverables will include a comprehensive codebase, trained model weights, detailed performance reports, and deployment scripts for seamless implementation. We are excited to collaborate on this project and look forward to sharing initial architecture ideas with you. Best regards, Karthik B Resonite Technologies

@nikkibraiam

Hi, I'm a Software Architect with 11+ years building production ML pipelines and NLP systems at scale (Python, PyTorch/Scikit-learn, FastAPI microservices). I understand the real challenge here: getting a fake-news classifier past 90% accuracy isn't just training a model - it's building a reproducible pipeline (scraping to cleaning to labeling) and a transparent inference layer that explains why an article was flagged, not just a black-box score. What I'll deliver: - End-to-end pipeline: scraper/ingest, text cleaning, structured dataset - Fine-tuned transformer (BERT-style) with precision/recall/F1/accuracy report >= 90% - REST API returning probability plus key influencing features - Dockerfile + README for one-command deploy - Clean, modular, PEP8 code ready for future extension I've built fine-tuned BERT classifiers and fact-checking pipelines before. I work fast, ask minimal questions mid-project, and deliver exactly what's scoped. Let's build this right.