Fake News Detection Using Recurrent Neural Networks and Distributed Representations for the Portuguese Language

2021 ◽  
Author(s):  
Guilherme Zanini Moreira ◽  
Marcelo Romero ◽  
Manassés Ribeiro
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Classification Performance ◽  
Fake News ◽  
Distributed Representations ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Media Channels ◽  
Damage Rule

After the advent of Web, the number of people who abandoned traditional media channels and started receiving news only through social media has increased. However, this caused an increase of the spread of fake news due to the ease of sharing information. The consequences are various, with one of the main ones being the possible attempts to manipulate public opinion for elections or promotion of movements that can damage rule of law or the institutions that represent it. The objective of this work is to perform fake news detection using Distributed Representations and Recurrent Neural Networks (RNNs). Although fake news detection using RNNs has been already explored in the literature, there is little research on the processing of texts in Portuguese language, which is the focus of this work. For this purpose, distributed representations from texts are generated with three different algorithms (fastText, GloVe and word2vec) and used as input features for a Long Short-term Memory Network (LSTM). The approach is evaluated using a publicly available labelled news dataset. The proposed approach shows promising results for all the three distributed representation methods for feature extraction, with the combination word2vec+LSTM providing the best results. The results of the proposed approach shows a better classification performance when compared to simple architectures, while similar results are obtained when the approach is compared to deeper architectures or more complex methods.

Optimized operation of a multireservoir system by means of Recurrent Neural Networks for inflow forecasting

2021 ◽  
Author(s):  
Tim Müller ◽  
Günter Meon
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Decision Support Tool ◽  
Support Tool ◽  
In Series ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Effective Water

<p>The operation optimization of interconnected reservoirs is crucial for effective water resources management. Therefore, a decision support tool for is developed based on the forecasts of natural inflows. Standard forecast procedures are often based on historical streamflows and hydrological modelling of flows using quantitative meteorological forecasts. In recent years, forecasting using deep learning methods and especially recurrent neural networks have gained attention. Compared to other approaches such as regression-based and time series models, artificial neural networks have proven to be more effective and flexible. We propose a long short-term memory network (LSTM) for forecasting inflow into reservoirs with a large watershed. It is trained with observed hourly streamflow and meteorological data and applicable to different forecast horizons. The novelty here is the inclusion of temperature, windspeed and snow into the forecast.</p><p>The Drin river cascade (11 830 km²) in Northern Albania was selected as a pilot hydraulic system, whereby the upper part of the Drin river basin covers also parts of North Macedonia, Kosovo and Montenegro. The cascade consists of three large dams in series. The reservoirs are primarily used for energy generation and, secondarily, for flood retention. The studied LSTM forecast horizons (6, 8, 12 hours; >12 hours) indicate that the Recurrent Neural Network provides a proper forecast of the natural inflows into the reservoir cascade and thus represents a valuable tool for the optimization of the operation of the Drin Cascade under multi-criteria conditions.</p>

scholarly journals Detecting overlapping speech with long short-term memory recurrent neural networks

2013 ◽  
Author(s):  
Jürgen T. Geiger ◽  
Florian Eyben ◽  
Björn Schuller ◽  
Gerhard Rigoll
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory
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