Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Ahmad, Shakeel; Asghar, Muhammad Zubair; Alotaibi, Fahad Mazaed; Awan, Irfan-Ullah

doi:10.1186/s13673-019-0185-6

Cited by 97 publications

(94 citation statements)

References 22 publications

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“…Then it builds a new CNN model on the LSTM to extract the features of the input text sentences and improve the results of the classification accuracy. In our experiment, we follow the Hybrid framework for Text modeling using LSTM-CNN method applied in previous works [45][46][47]. Figure 2 shows the proposed LSTM-CNN combined model architecture for classifying the sentences with suicidal and non-suicidal content.…”

Section: Proposed Network Modelmentioning

confidence: 99%

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Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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Suicide ideation expressed in social media has an impact on language usage. Many at-risk individuals use social forum platforms to discuss their problems or get access to information on similar tasks. The key objective of our study is to present ongoing work on automatic recognition of suicidal posts. We address the early detection of suicide ideation through deep learning and machine learning-based classification approaches applied to Reddit social media. For such purpose, we employ an LSTM-CNN combined model to evaluate and compare to other classification models. Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results. Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.

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Section: Proposed Network Modelmentioning

confidence: 99%

“…Flatten Layer CNN flatten layer aims to transform a pooled feature map into a column vector which makes an input to the neural network of the classification task [47]. As the next step, the pooled feature maps are flattened through a reshape function to make the feature vector pulls concatenated.…”

Section: Convolutional Layermentioning

confidence: 99%

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

et al. 2019

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“…e study made an attempt to match the unattributed terrorist attack to known terrorist groups. In 2019, Ahmad et al [29] developed a method for detection and classification of social media-based extremist affiliations based on the sentiment analysis. e focus was to classify tweets into two categories: extremist and nonextremist classes.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

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“…There are two major goals: detect fakenews and detect physical or cyberattacks. Fakenews is one of the most relevant problems for security researches, there are many research challenges on the detection and reduction in the spread of fakenews [ 59 ]; also, there is a topics and threads that allow authorities to detect events and trending topics and eventually illegal events [ 60 , 61 , 62 , 63 ] or to detect patterns in post from social media in order to understand and detect users that control multiple social media accounts.…”

Section: Kolmogorov Complexity Application Scenariosmentioning

confidence: 99%

A Survey on Using Kolmogorov Complexity in Cybersecurity

Resende

Martins

Antunes

2019

Entropy

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Security and privacy concerns are challenging the way users interact with devices. The number of devices connected to a home or enterprise network increases every day. Nowadays, the security of information systems is relevant as user information is constantly being shared and moving in the cloud; however, there are still many problems such as, unsecured web interfaces, weak authentication, insecure networks, lack of encryption, among others, that make services insecure. The software implementations that are currently deployed in companies should have updates and control, as cybersecurity threats increasingly appearing over time. There is already some research towards solutions and methods to predict new attacks or classify variants of previous known attacks, such as (algorithmic) information theory. This survey combines all relevant applications of this topic (also known as Kolmogorov Complexity) in the security and privacy domains. The use of Kolmogorov-based approaches is resource-focused without the need for specific knowledge of the topic under analysis. We have defined a taxonomy with already existing work to classify their different application areas and open up new research questions.

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Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Cited by 97 publications

References 22 publications

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Prediction of Future Terrorist Activities Using Deep Neural Networks

A Survey on Using Kolmogorov Complexity in Cybersecurity

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