Datathon – HackNews – Solution – DataExploiters

Posted 5 CommentsPosted in Datathons Solutions

This article describes our submission for the Hack the News Datathon 2019 which focuses on Task 2, Propaganda sentence classification. It outlines our exploratory data analysis, methodology and future work. Our work revolves around the BERT model as we believe it offers an excellent language model that’s also good at attending to context which is an important aspect of propaganda detection.

What is Propaganda?

Posted Leave a commentPosted in News

The Institute for Propaganda Analysis in 1938 defined propaganda as: “The expression of an opinion or an action by individuals or groups deliberately designed to influence the opinions or the actions of other individuals or groups with reference to predetermined ends”. – Institute for Propaganda Analysis The point of view, highlights, and storytelling expressed in […]

Datathon – HackNews – Solution – data_monks

Posted 6 CommentsPosted in Datathons Solutions

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

Hack News Datathon Mentors’ Guidelines – Propaganda Detection

Posted Leave a commentPosted in Learn

In this article, the mentors give some preliminary guidelines, advice, and suggestions to the participants for the Hack the News Datathon case. Every mentor should write their name and chat name at the beginning of their texts so that there are no mix-ups with the other mentors. Introduction to NLP Natural Language Processing (NLP) is […]

Datathon – HackNews – Solution – PIG (Propaganda Identification Group)

Posted 9 CommentsPosted in Datathons Solutions

@Mentors: preslav @preslav alberto @alberto giovanni @giovanni laura @laura vsenderov @vsenderov   Team Members (datachat user name) Thomas Arnold (thomasarnold) Gisela Vallejo (gvallejo) Yang Gao (yg211) Tilman Beck (tbtuda) Nils Reimers (reimers) Jonas Pfeiffer (jopfeiff) Toolset: Keras – Github Pytorch – Site ScikitLearn – Site Flair – Paper Github BERT – Paper Github Stanford Politeness API: Github […]

Datathon-HackNews-Solutions-Data Titans

Posted 4 CommentsPosted in Datathons Solutions

  Team Name : Data Titans Team Members : M.HEMANTH KUMAR, A.PAVAN SHANKAR, B.MANOHAR, V. LITHIN CHOWDARY,  E.V.S.SAI RAM PROBLEM STATEMENT : Hack the news whether it is propaganda or Non-Propaganda INTRODUCTION: Propaganda is a view which can mislead us to certain false assumptions, So here we got a chance to Identify the Propaganda in the […]

Team Cherry. The Kaufland Case. Fast and Accurate Image Classification Architecture for Recognizing Produce in a Real-Life Groceries’ Setting

Posted 6 CommentsPosted in Image recognition, Learn, Team solutions

Our best model (derived from VGG) achieved 99.46% top3 accuracy (90.18% top1) with processing time during training of 0.006 s per image on a single GPU Titan X (200s / epoch with 37 000 images).

The teams vision is for the team members to see where they stand compared to others in terms of ideas and approaches to computer vision and to learn new ideas and approaches from the other team-mates and the mentors.

Therefore the team is pursuing a pure computer vision approach to solving the Kaufland and/or the ReceiptBank cases.