Social mining for terroristic behavior detection through Arabic tweets characterization

Alhalabi, Wadee; Jussila, Jari; Jambi, Kamal; Visvizi, Anna; Qureshi, Hafsa; Lytras, Miltiadis D.; Malibari, Areej; Adham, Raniah Samir

doi:10.1016/j.future.2020.10.027

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…For detecting and preventing terrorist events, Alhalabi et al [71] proposed an antiterrorism operation algorithm. They combined the text stream and image stream results and calculated the probability of suspicious acts that ultimately fulfilled their goal of detecting terroristic behavior.…”

Section: Results Of Rule-based Approachesmentioning

confidence: 99%

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A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

Mredula

Dey

Rahman

et al. 2022

Sensors

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Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by sensing social media data from the last decade. We summarize their event detection techniques, tools, technologies, datasets, performance metrics, etc. The reviewed papers mainly address the detection of events, such as natural disasters, traffic, sports, real-time events, and some others. As these detected events can quickly provide an overview of the overall condition of the society, they can significantly help in scrutinizing events disrupting social security. We found that compatibility with different languages, spelling, and dialects is one of the vital challenges the event detection algorithms face. On the other hand, the event detection algorithms need to be robust to process different media, such as texts, images, videos, and locations. We outline that the event detection techniques compatible with heterogeneous data, language, and the platform are still missing. Moreover, the event and its location with a 24 × 7 real-time detection system will bolster the overall event detection performance.

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Section: Results Of Rule-based Approachesmentioning

confidence: 99%

“…Rule-based approaches are popular in disruptive event detection. Alhalabi et al [71] used such an approach, and they proposed a system for detecting terrorist behavior. They also presented an algorithm for anti-terrorism operations.…”

Section: Rule-based Approachesmentioning

confidence: 99%

A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

Mredula

Dey

Rahman

et al. 2022

Sensors

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show abstract

“…Analisis peringatan dini bencana alam dan penilaian kerusakan akibat bencana dapat menggunakan dari data media sosial dan informasi geo-location dengan melakukan metode sentiment analysis, dan top hashtags and keywords frequency analysis [6]. Teks dan data mining dari media sosial digunakan untuk membuat mapping aktivitas rekreasi satwa liar [7]. Social media data mining digunakan untuk mendeteksi perilaku teoristik melalui karakteristik Tweet berbahasa Arab [8].…”

Section: Pendahuluanunclassified

“…Berikut ini adalah penjelasan tahapannya: (1) Pemilihan topik, yaitu menentukan topik untuk dijadikan bahan pembahasan dan review; (2) Studi literatur, literatur yang dicari dan dipilih adalah literatur yang relevan dan sesuai dengan topik yang dipilih; (3) Analisis dan sintesis literatur, dilakukan analisis dan sinstesis literatur yang menjadi poin penting dari penelitian yang dilakukan; (4) Organisasi penulisan review, membuat kerangka penulisan review dan kemudian memulai penulisan. Berdasarkan pada tabel 3.1 media sosial dapat digunakan diberbagai bidang penelitian yang berkaitan dengan big data, misalnya dalam bidang kesehatan [15], [13]; pembangunan kota [14], [5], [9] ; pariwisata [11], [10][12]; sosial [8]; bencana alam [5], [16]; biological conservation [7]. Semua penelitian tersebut digunakan untuk membantu dalam pengambilan keputusan.…”

Section: Metode Penelitian Gambar 21 Metode Penelitianunclassified

Analitik Big Data: Social Media Mining

Rahmala

Ardhito

Riska

2022

ceritaj

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he development of big data from year to year has become very rapid in line with the development of the Internet of Things (IoT). One of the most widely owned and frequently used devices is a mobile phone, especially a smartphone. Social media applications found on smartphones generate large amounts of data every second. These data are widely used forresearch in various fields of big data analytics. And the techniques used also vary depending on the dataset and the purpose of the research. This study aims to discuss big data analytics, especially social media mining, and some of its techniques for analysis. The research was conducted using a literature review method on journals and books discussing topics relevant to this research. The expected benefit of this research is that it can add insight and knowledge for readers who are interested in the topic of social media mining.

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“…Their proposed model gives an accuracy of 65% for a dataset with 2000 tweets. WadeeAlhalabiet al [10] presented a social mining technique known as artificial intelligence-supported Detection Terrorist Behaviour system(ALT-TERROS) to identify the terrorist behavior from the Arabic tweets. The social mining technique utilizes the distributed data present in the social media fully to identify the terrorist behavior patterns and visualize them for proper decision making.…”

Section: Related Workmentioning

confidence: 99%

A Sentimental Classification Approach to Predict the User Behavior Using an Optimized Centered Convolutional Restricted Boltzmann Machine

Karthik

Sethukarasi²

2021

Preprint

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Sentiment analysis uses different tools and techniques to extract informative data such as users' opinions or emotions from their textual feedback. The state-of-art sentiment analysis techniques offered lower performance due to the inability to handle both small and larger datasets. To overcome this problem this paper presents a deep learning technique known as Centered Convolutional Restricted Boltzmann Machines (CCRBM) for user behavioral sentimental analysis. However, this deep learning model's performance solely depends upon the parameter selection process. To overcome this problem and increase the classification accuracy a Hybrid Atom Search Arithmetic Optimization (HASAO) algorithm is used in this paper to select the parameters of the CCRBM architecture and offer optimal performance. The initial population quality and exploitation capacity of the Atom Search Optimization (ASO) algorithm is enhanced by hybridizing it with the Arithmetic Optimization(AO) algorithm. To investigate the effectiveness of the proposed HASAO optimized CCRBM architecture it is evaluated using four different datasets namely Reddit, Twitter, IMDB movie review, and Yelp dataset. The performance of the proposed model is analyzed by comparing it with four baseline models and the accuracy value above 90% for the four datasets proves the efficiency of the proposed technique.

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Social mining for terroristic behavior detection through Arabic tweets characterization

Cited by 10 publications

References 13 publications

A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data

Analitik Big Data: Social Media Mining

A Sentimental Classification Approach to Predict the User Behavior Using an Optimized Centered Convolutional Restricted Boltzmann Machine

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