Exploratory data analysis and deception detection in news articles on social media using machine learning classifiers

Sharma, Anu; Sharma, Manish; Dwivedi, Rakesh Kr.

doi:10.1016/j.asej.2023.102166

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…Exploratory data analysis (EDA) [31] is a method used to understand relationships in data. This statistical method is widely used in various fields including medicine, finance, social media, and fighting crime, to reduce the complexity of data and reveal underlying patterns [32][33][34][35]. EDA is an essential tool for understanding the structure of underlying data and enhancing model interpretability.…”

Section: Overview Of Exploratory Data Analysismentioning

confidence: 99%

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Karimai,

Liu,

Maruyama

2024

Applied Sciences

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Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has the potential to cause extensive damage to buildings and infrastructure through ground failure. During the 2011 Great East Japan Earthquake, Urayasu City in the Chiba Prefecture experienced severe soil liquefaction, leading to evacuation losses due to the effect of the liquefaction on roads. Therefore, developing quantitative predictions of ground subsidence caused by liquefaction and understanding its contributing factors are imperative in preparing for potential future mega-earthquakes. This research is novel because previous research primarily focused on developing predictive models for determining the presence or absence of liquefaction, and there are few examples available of quantitative liquefaction magnitude after liquefaction has occurred. This research study extracts features from existing datasets and builds a predictive model, supplemented by factor analysis. Using the Cabinet Office of Japan’s Nankai Trough Megathrust Earthquake model, liquefaction-induced ground subsidence was designated as the dependent variable. A gradient-boosted decision-tree (GDBT) prediction model was then developed. Additionally, the Shapley additive explanations (SHAP) method was employed to analyze the contribution of each feature to the prediction results. The study found that the XGBoost model outperformed the LightGBM model in terms of predictive accuracy, with the predicted values closely aligned with the actual measurements, thereby proving its effectiveness in predicting ground subsidence due to liquefaction. Furthermore, it was demonstrated that liquefaction assessments, which were previously challenging, can now be interpreted using SHAP factors. This enables accountable wide-area prediction of liquefaction-induced ground subsidence.

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Section: Overview Of Exploratory Data Analysismentioning

confidence: 99%

Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Karimai,

Liu,

Maruyama

2024

Applied Sciences

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“…;Briscoe et al 2014;Giasemidis et al 2016;Elyassami et al 2022;Kini 2022), random forest (RF)(Briscoe et al 2014;Giasemidis et al 2016;Elyassami et al 2022;Sharma et al 2023), decision tree (DT)(Castillo et al 2011;Giasemidis et al 2016;Kishwar and Zafar 2023) and logistic regression (LR)(Giasemidis et al 2016;Elyassami et al 2022;Kishwar and Zafar 2023). 2.…”

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confidence: 99%

Disinformation detection using graph neural networks: a survey

Lakzaei,

Haghir Chehreghani,

Bagheri

2024

Artif Intell Rev

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The creation and propagation of disinformation on social media is a growing concern. The widespread dissemination of disinformation can have destructive effects on people’s attitudes and behavior. So, it is essential to detect disinformation as soon as possible. Therefore, the interest in effective detection techniques has grown rapidly in recent years. Major social media and social networking sites are trying to develop robust strategies to detect disinformation and prevent its spread. Machine learning techniques and especially neural networks, have an essential role in this task. In this paper, we review different approaches for automatic disinformation detection, with a focus on methods that leverage graph neural networks (GNNs). GNNs are very suitable tools for detecting disinformation in social networks. Because on the one hand, graphs are the most comprehensive way to model social networks and on the other hand, GNNs are the best tool for processing graph data. We define different forms of disinformation, and examine the features used and the methods presented from different perspectives. We also discuss relevant research areas, open problems, and future research directions for disinformation detection in social media.

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