Data Science, Python, R, Machine Learning, Text Mining, Sentiment Analysis

This application serves to scrape tweets about a given subject in real time. Then, each Tweet text is analyzed using Natural Language Processing to detect whether the news are real or fake based on several features. Finally, the results are visualized in a graph and a pie chart.

spamming. The presence of spam on web services such as search engines, email providers or online networking services can be manifested in many ways including spam advertising, malicious links, fake news or fake friends but also manipulation attempts. For online social network, tracking and controlling spammers are of the upmost importance due to the security risk but also for the credibility of the information that they disseminate. The objective of this project is to study Twitter’s social spam by means of both data mining, machine learning and data analysis techniques (that you have learned so far in any course!) using a dataset containing information on 767 social spammers and legitimate users crawled from Twitter in November and December 2014 and July 2018. In this project you have firstly to solve the spam detection problem and secondly to analyse the dataset using methods presented during the lessons of data mining.

Random forest and XGBoost methods for fake news detection on Twitter posts. Feature engineering with sentiment analysis and emotion analysis included.

A human-centric explainable approach for fake news spreading detection on Twitter threads

Detection of Fake news on twitter using RNN and Propagation path

Fake news detection on Twitter using GNN models.

Twitter Fake News Detection, Traffic Sign Classification, Clustering Synthetic Data

Fake news is disseminated to intentionally persuade readers to accept biased or untrue beliefs by changing the way people interpret and respond to real news. Detecting fake news manually is relatively tedious especially with the rate at which information is been dispersed on Twitter, hence the need to leverage Machine Learning classifiers for this task. Search for a generally accepted COVID-19 dataset for fake news detection is still on, largely due to its novelty. A novel – more recent, and robust FND Dataset – was curated by scraping tweets from Twitter handles of some health organisations using Twitter API and socialscrapr. The dataset was preprocessed using Python libraries and Microsoft Excel after which it was split into train (80%), validation (10%) and test (10%) datasets and used on SVM, LR, DT baseline Machine Learning algorithms with SVM classifier obtaining the best result with 93.17% for both accuracy and F1 – score performance metrics.

Fake News Detection using ML The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

Detection of Fake News on Twitter using Propagation path and RNN

Academic Project (CS244-Introduction to data science)

AI-based fake news Detection using ML, DL and Flask API

No description available

Our end goal is to provide users a curated data of our analysis through a website using machine learning, emotional analysis, NLP, tweepy, Twitter API, etc in python to find correct news or information of different languages on Twitter and help users to make informed decisions.

twitter Fake News detection using Agentic AI Model

Master Thesis

Fake News Detection on Twitter using Graph Deep Learning

This includes all files in Twitter fake news detection using NLP

Hybrid GCN-Transformer model for fake news detection using Twitter datasets (MSc Thesis)

Fake News detection on Twitter regarding the reputation of users, its followers and followees.

Detection of fake news in twitter using machine learning techniques to increase the credibility and robustness of detection

Fake news detection on social media using machine learning and NLP. It proposes a model using Support Vector Machine (TF-IDF) to identify real and fake news. With 92% accuracy, the model aggregates and authenticates news from platforms like WhatsApp, Facebook, and Twitter.

A Django and asp.net based social media web application inspired by Twitter, featuring integrated machine learning models for fake news detection and hate speech recognition.

This is my first academic machine learning project. This project focuses on the detection of fake news which is common in social media. I utilized the scikit-learn library, a popular open-source library for the purposes of machine learning, to accomplish this project. I applied the concept of Term Frequency - Inverse Document Frequency (TF-IDF) to retrieve the keyword in the corpus. Anyone who is familiar with the concepts of Search Engine Optimization (SEO) will certainly comprehend the TF-IDF and its applications. TF-IDF is an information retrieval technique and a statistical measure used to determine how important a word is to a document in a corpus. The classification is done by the PassiveAggressiveClassifier algorithm.The dataset consists of nearly 6300 text documents extracted from various social websites like Twitter, Facebook, etc. 80% of the text documents are classified as 'training set' and the rest is as 'test set'. I'm glad to publish my project, which secured an accuracy of about 94%, in GitHub.

Implementation of SVM classifier to classify the tweet as SPAM/NON-SPAM using R language.Implemented both 2-dimensional(2D) and 3-dimensional(3D) SVM classifier for the same dataset.

No description available

Détermination de l'exactitude de tweet répandant des rumeurs à travers des modèles d'apprentissage automatique de Machine Learning

No description available

Exploring graph neural networks for fake news detection on Twitter. Using advanced ML, it classifies news graphs as real or fake, combating misinformation. Emphasizes graph analysis's role in understanding user-news relationships, aiding the fight against fake news.