Context
In the age of information inflation, news is no longer produced or consumed in a centralized fashion. The media landscape has changed radically, mainly because of: i) the instantaneous rate at which individuals publish news-worthy content, ii) the vast reachability of this content by broad audiences, and iii) the lack of regulation and quality control. The modern news landscape consists of mainstream news outlets which are supported, complemented and often criticized by independent or alternative media channels. Although media companies are typically responsible for discovering and communicating news to the people, information paths are becoming increasingly convoluted with social networks acting as a diffusion medium.
Goal
News Teller is a real-time news analytics platform that provides a wide variety of tools for mapping the media landscape, monitoring the reach and the stance of news consumers, and providing quality indicators for millions of news articles and thousands of sources. In this project we will focus on the social media reactions on news articles. We will construct time-series of social media postings from which we will extract the sentiment and the stance. The methodology will be integrated and made immediately available in the platform.
Implementation Steps
- Familiarize with the data pipeline of News Teller
- Extend its functionality to support time-series of social media postings
- Implement a time-efficient sentiment analysis model
- Integrate the methodology in the platform
- Showcase new functionality with a demo
Requirements
- Programming skills in Python
- Experience in code-versioning platforms like GitHub
- Experience in Natural Language Processing
- Experience in Data Analysis
- Experience in Machine Learning
Contact
Panayiotis Smeros