Fake news identification on Twitter with hybrid CNN and RNN models

The problem associated with the propagation of fake news continues to grow at an alarming scale. This trend has generated much interest from politics to academia and industry alike. We propose a framework that detects and classifies fake news messages from Twitter posts using hybrid of convolutional neural networks and long-short term recurrent neural network models. The proposed work using this deep learning approach achieves 82% accuracy. Our approach intuitively identifies relevant features associated with fake news stories without previous knowledge of the domain

TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy

Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.Comment: MediaEval 2020 Fake News Tas

Topic Detection on Twitter Using Deep Learning Method with Feature Expansion GloVe

Twitter is a medium of communication, transmission of information, and exchange of opinions on a topic with an extensive reach. Twitter has a tweet with a text message of 280 characters. Because text messages can only be written briefly, tweets often use slang and may not follow structured grammar. The diverse vocabulary in tweets leads to word discrepancies, so tweets are difficult to understand. The problem often found in classifying topics in tweets is that they need higher accuracy due to these factors. Therefore, the authors used the GloVe feature expansion to reduce vocabulary discrepancies by building a corpus from Twitter and IndoNews. Research on the classification of topics in previous tweets has been done extensively with various Machine Learning or Deep Learning methods using feature expansion. However, To the best of our knowledge, Hybrid Deep Learning has not been previously used for topic classification on Twitter. Therefore, the study conducted experiments to analyze the impact of Hybrid Deep Learning and the expansion of GloVe features on classification topics. The total data used in this study was 55,411 datasets in Indonesian-language text. The methods used in this study are Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Hybrid CNN-RNN. The results show that the topic classification system with GloVe feature expansion using the CNN method achieved the highest accuracy of 92.80%, with an increase of 0.40% compared to the baseline. The RNN followed it with an accuracy of 93.72% and a 0.23% improvement. The CNN-RN Hybrid Deep Learning model achieved the highest accuracy of 94.56%, with a significant increase of 2.30%. The RNN-CNN model also achieved high accuracy, reaching 94.39% with a 0.95% increase. Based on the accuracy results, the Hybrid Deep Learning model, with the addition of feature expansion, significantly improved the system's performance, resulting in higher accuracy

Combined Deep Learning and Traditional NLP Approaches for Fire Burst Detection Based on Twitter Posts

The current chapter introduces a procedure that aims at determining regions that are on fire, based on Twitter posts, as soon as possible. The proposed scheme utilizes a deep learning approach for analyzing the text of Twitter posts announcing fire bursts. Deep learning is becoming very popular within different text applications involving text generalization, text summarization, and extracting text information. A deep learning network is to be trained so as to distinguish valid Twitter fire-announcing posts from junk posts. Next, the posts labeled as valid by the network have undergone traditional NLP-based information extraction where the initial unstructured text is converted into a structured one, from which potential location and timestamp of the incident for further exploitation are derived. Analytic processing is then implemented in order to output aggregated reports which are used to finally detect potential geographical areas that are probably threatened by fire. So far, the part that has been implemented is the traditional NLP-based and has already derived promising results under real-world conditions’ testing. The deep learning enrichment is to be implemented and expected to build upon the performance of the existing architecture and further improve it

A Survey on Various Methods to Detect Rumors on Social Media

Internet-based life stages have been utilized for data and newsgathering, and they are entirely significant in numerous applications. In any case, they likewise lead to the spreading of gossipy tidbits, Rumors, and phony news. Numerous endeavors have been taken to recognize and expose rumors via social networking media through dissecting their substance and social setting utilizing ML (Machine Learning) strategies. This paper gives an outline of the ongoing investigations in the rumor detection. The errand for rumor detection means to distinguish and characterize gossip either as obvious (genuine), bogus (nonfactual), or uncertain. This can hugely profit society by forestalling the spreading of such mistaken and off base data proactively. This paper is an introduction to rumor recognition via social networking media which presents the essential wording and kinds of bits of rumor and the nonexclusive procedure of rumor detection. A cutting edge portraying the utilization of directed ML algorithms for rumor detection via Social networking media is introduced. Keywords: Rumor Detection, Rumor Classification, Misinformation, News Events, Social Media, Machine Learning DOI: 10.7176/CEIS/11-4-01 Publication date:June 30th 202

TIB's visual analytics group at MediaEval '20: Detecting fake news on corona virus and 5G conspiracy

Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifi-cally, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The Fak-eNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection