Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST

Training a neural network model from small size of high quality labeled dataset for fake news detection

[zur Übersicht]
Ipek Baris

Fact-checking is the task of assessing the truthfulness of a factual claim made in written or spoken statements. Only small percentage of the set of stories/claims spreading over time could be covered by human fact-checkers. One reason is that fact-checking requires labour intensive research. For example, fact-checkers might contact with person/organizations mentioned in the claim, they might speak to experts if they lack background knowledge, investigate sources of facts, etc. To optimise fact-checking task, it is necessary to prioritise the documents which would be potentially false and have higher impacts in the society.

In this talk, I will focus on task of detecting potentially fake news articles, namely fake news prediction. First I will present the problems of popular datasets for a neural network based model for fake news detection. Then, I will briefly explain two deep semi supervised approaches [1][2] which are widely used in computer vision tasks, and the experimental settings for the task.

[1] Tarvainen, Antti et al. “Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.” Advances in neural information processing systems. 2017.[2] Lee, Dong-Hyun. “Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks.” Workshop on Challenges in Representation Learning, ICML. Vol. 3. 2013.


09.01.20 - 10:15
B 016