🕵️♂️ SM-OSINT: Collect a dossier on a person by username from thousands of sites
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Updated
Feb 21, 2024 - Python
🕵️♂️ SM-OSINT: Collect a dossier on a person by username from thousands of sites
Project focused on Social Media Analytics and Text Mining topics in the context of 2020 US Presidential Elections
Perpetuating Islamophobia in the US: Examining the Relationship Between News, Social Media, and Hate Crimes. Final project for Gov50 course. https://jannaramadan.shinyapps.io/USIslamophobia/
A Python utility for social media conversational analysis.
Sentimaster is a powerful sentiment analysis tool that leverages machine learning to provide deep insights into textual data, ideal for businesses and individuals
Working group for Communication Managers, Social Media Specialists, and Content Creators.
This project uses data from Twitter to identify the regions with different predominant opinions about vaccines in India.
Covid-19 Sentiment Analysis using NLP is a project that analyzes Twitter data to gain insights into public sentiment during the pandemic, implemented with NLTK, Vader, and TextBlob libraries.
A toolkit to parse and analyze contents from social media.
A large-scale podcast dataset of Rumble social media platform.
Queen's University Assignment
P-SN es un clasificador de noticias falsas que utiliza características independientes del lenguaje representadas como series de tiempo. \\ P-SN is a fake news classifier that uses language-agnostic features represented as time series.
A support vector regression model predicting lifetime engaged users on a cosmetic company's Facebook page. Data extracted from post metrics from over 500 posts in 2014. Achieved an accuracy of over 80%!
Specific-Aspect Summarization on News According to Social Sentiments on Twitter
FakeFind: A social media detection project employing ML to discern fake accounts by analyzing profile data, username attributes, and activity metrics.
Student project of the Social Media Analytics web-course by Fitech.io & Aalto University
A multiple linear model predicting lifetime engaged users on a cosmetic company's Facebook page. Data extracted from post metrics from over 500 posts in 2014. Achieved an accuracy of nearly 80%!
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