Case NetInfo/Vesti.bg article recommendation — Team Army of Ones and Zeroes — Datathon 2020
Team Army of Ones and Zeros solving the “Netinfo/Vesti.bg” Case for Datathon 2020.
Team Army of Ones and Zeros solving the “Netinfo/Vesti.bg” Case for Datathon 2020.
This is a Leopards team’s submission for the Propaganda Detection datathon. Key findings: the best performing classifier is logistic regression, operating on Word2vec representations of the sentences plus several designed features like the proportion of sentiment-bearing words in the sentence.
The word propaganda is defined as designating any attempt to influence the opinions or actions of others to some predetermined end by appealing to their emotions or prejudices or by distorting the facts. We are fooled by propaganda chiefly because they appeal to our emotions rather than to our reason. They make us believe and do something we would not believe or do. And since it appeal more to our emotions; we often don’t recognize it when we see it.
The current political landscape is shaped by extreme polarization of opinions and by the proliferation of fake news.
Studies and surveys has found that rumour’s and fake news tend to spread six times faster than truthful information. This situation both damages the reputation of respectable news outlets and it also undermines the very foundations of democracy, which needs free and reliable press to thrive. Therefore, it is in the interest of the public as well as of the news organizations to be able to detect and fight disinformation in all its forms.
Here, we are trying to create a tool that can help identify propagandistic articles with the help of Predictive Analytics.
The main objectives are:
(i) to flag the article as a whole
(ii) to detect the potentially propagandistic sentences in a news article
(iii) to identify the exact type and span of use of propagandistic techniques
Business understanding A vibrant and dynamic lifestyle of a big city leads to a necessity its citizens to deal with particular drawbacks. In case […]
Hi Everybody, I have done a basic EDA of some of the data and summarized it in the below python Jupyter notebook. […]
Business Understanding Air pollution is one of the major global topics in every part of the modern life – politics, ecology, social […]
Week 1 – Kick off article. Feedbacks: First, Kudo’s for setting this up. Amazing kick off considering all the moving part. A […]
DATA DATA DATA !!
Kaufland Case – Team3 No content
Business Understanding In Sofia, air pollution norms were exceeded 70 times in the heating period from October 2017 to March 2018, […]
It is a very well known fact that Exploratory Data Analysis is cornerstone of Data Analysis.
On the analysis of data it is evident that Brass Raven Birdy as the most failed and the Metallic Raven Sunburst Polly is the most successful raven. Also Targeryan family has the most Raven fails whereas Baelish family has the least failures,and among the family of Baelish, Peter Baelish has the most failure rate and Euron has the least failures.
ARIMA model is used for predicting the number of failures for the next 4 days.