Detecting Fake News Articles

Lin, Jun; Tremblay-Taylor, Glenna; Mou, Guanyi; You, Di; Lee, Kyumin

doi:10.1109/bigdata47090.2019.9005980

Cited by 32 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lin J. et al [ 50 ] presented a framework that extracted 134 features and built traditional machine learning models, such as XGBoost and Random Forest. They also developed a deep learning-based model, the LSTM with a self-attention mechanism.…”

Section: Cross-* Detection Methodologiesmentioning

confidence: 99%

Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

Xarhoulacos

Anagnostopoulou

Stergiopoulos

et al. 2021

Sensors

View full text Add to dashboard Cite

The world has been afflicted by the rise of misinformation. The sheer volume of news produced daily necessitates the development of automated methods for separating fact from fiction. To tackle this issue, the computer science community has produced a plethora of approaches, documented in a number of surveys. However, these surveys primarily rely on one-dimensional solutions, i.e., deception detection approaches that focus on a specific aspect of misinformation, such as a particular topic, language, or source. Misinformation is considered a major obstacle for situational awareness, including cyber, both from a company and a societal point of view. This paper explores the evolving field of misinformation detection and analytics on information published in news articles, with an emphasis on methodologies that handle multiple dimensions of the fake news detection conundrum. We analyze and compare existing research on cross-dimensional methodologies. Our evaluation process is based on a set of criteria, including a predefined set of performance metrics, data pre-processing features, and domains of implementation. Furthermore, we assess the adaptability of each methodology in detecting misinformation in real-world news and thoroughly analyze our findings. Specifically, survey insights demonstrate that when a detection approach focuses on several dimensions (e.g., languages and topics, languages and sources, etc.), its performance improves, and it becomes more flexible in detecting false information across different contexts. Finally, we propose a set of research directions that could aid in furthering the development of more advanced and accurate models in this field.

show abstract

Section: Cross-* Detection Methodologiesmentioning

confidence: 99%

Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

Xarhoulacos

Anagnostopoulou

Stergiopoulos

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In a study conducted by Escolà-Gascón et al (2021), it was discovered that critical thinking predicts reductions in stress levels among Spanish physicians and increases fake news identification. According to Lin et al (2019), a system was provided that extracts 134 features from news articles using machine learning models and deep learning models that simply rely on textual input. Afterwards, they must compare their models to the baseline model in order to determine which one performs better in the fake news detection task.…”

Section: Related Workmentioning

confidence: 99%

Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

Aljwari

Alkaberi

Alshutayri

et al. 2022

View full text Add to dashboard Cite

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that were made up were used. They are found by using Term Frequency-Inverse Document Frequency (TF-IDF). The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news.

show abstract

“…• LSTM-ATT (Lin et al, 2019): LSTM-ATT utilizes long short term memory (LSTM) with an attention mechanism. The model takes a 300-dimensional vector representation of news articles as input to a two-layer LSTM for fake news detection.…”

Section: Datasetmentioning

confidence: 99%

“…One possible explanation for this is that LIWC can capture the linguistic features in news articles based on words that denote psycholinguistic characteristics. The LSTM-ATT, which has extensive preprocessing using count features and sentiment features along with hyperparameter tuning (Lin et al, 2019), has similar performance compared with HAN in PolitiFact, however, outperforms HAN on GossipCop. One reason for this can be that the attention mechanism is able to capture the relevant representation of the input.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

Explainable Tsetlin Machine framework for fake news detection with credibility score assessment

Bhattarai,

Granmo,

Jiao

2021

Preprint

View full text Add to dashboard Cite

The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as Politi-Fact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and qualityassure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use the clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and Gossip-Cop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least 5% in terms of accuracy, with the added benefit of an interpretable logicbased representation. Further, our approach provides higher F1-score than BERT and XL-Net, however, we obtain slightly lower accuracy. We finally present a case study on our model's explainability, demonstrating how it decomposes into meaningful words and their negations.

show abstract

Detecting Fake News Articles

Cited by 32 publications

References 14 publications

Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

Multi-scale Machine Learning Prediction of the Spread of Arabic Online Fake News

Explainable Tsetlin Machine framework for fake news detection with credibility score assessment

Contact Info

Product

Resources

About