People who consume information – news, blogs in real time – have come to expect and get pretty quick analysis on the deeper context behind this information. What makes news more interesting today is the speed at which aspects of the information is dissected and validated in channels like Twitter and blogs.
The one category that has experienced increasing strength on social channels is US politics. How many of you read the Mueller Report? Maybe you just want to read aspects of it? Then this blog post will help you mine information on the Mueller Report through Elastic Search. More importantly, with the upcoming U.S. elections in view, people like Nate Silver have gained fame in his political predictions. For those who are fans of Taleb’s "The Black Swan", this post that pits Taleb against Silver is one to read. And if you really want understanding @realdonaldtrump’s tweets, check this out!
We can’t talk about data-driven journalism without addressing truth in the news. We read anything and everything when it comes to trending information but how do we know what’s opinion, what’s parody and what’s real? This fake news detector post provides a great view into how to classify what we read everyday. We’re questioning information these days and the more data-driven analysis we consume, the more informed we are and the more we come to rely on it as a filter amid the plethora of information we read everyday.
Hessie Jones – Editorial Associate / Strategic Director at Global Privacy & Security by Design.
Why you should care about the Nate Silver vs. Nassim Taleb Twitter war
By Isaac Faber – 10 min read
How can two data experts disagree so much?
The Disappearing Poor
By Will Koehrsen – 10 min read
Exploring the incredible worldwide gains in prosperity
Who’s Tweeting from the Oval Office?
By Greg Rafferty – 18 min read
I’ve built a Twitter bot @whosintheoval which retweets each of Donald Trump’s tweets and offers a prediction for whether the tweet was written by Trump himself or by one of his aides.
Making the Mueller Report Searchable with OCR and Elasticsearch
By Kyle Gallatin – 6 min read
April 18th marked the full release of the Mueller Report – a document outlining the investigation of potential Russian interference in the 2016 presidential election.
How Does News Coverage Differ Between Media Outlets?
By Michael Tauberg – 5 min read
A comparative analysis of news media over the last month using R and Python
Using word2vec to Analyze News Headlines and Predict Article Success
By Charlene Chambliss – 17 min read
Can word embeddings of article titles predict popularity? What can we learn about the relationship between sentiment and shares? word2vec can help us answer these questions, and more.
I tracked my happiness each day of 2018
By Nhan Thanh Vu – 7 min read
Observing and analyzing trends in my own mental health
I Built a Fake News Detector Using Natural Language Processing and Classification Models
By Jasmine Vasandani – 6 min read
Analyzing open source data from Subreddits r/TheOnion & r/nottheonion.
We also thank all the great new writers who joined us recently, Noa Lubin, Ashley Binford, David Elsche, Henry Heberle, PhD, Pranjal Chaubey, Marguerite Siboni, Sundar V, Emily Jia, Dan Fritchman, Samuel Jefroykin, Julio Cezar Silva, Sean Carver, Vahe Tshitoyan, Fiona Chow, Eugen Hotaj, Ali Raza, Jaime Zornoza, Andreana Rosnik, Chien-Ping Lu, PhD and many others. We invite you to take a look at their profiles and check out their work.