Suspicious Activity Detection of Twitter and Facebook using Sentimental Analysis

Mansoori, Saeed Al; Almansoori, Afrah; Alshamsi, Mohammed; Salloum, Said A.; Shaalan, Khaled

doi:10.18421/tem94-01

Cited by 12 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This layer has been given a rate of 0.5, which represents the rate parameter for this layer. The value of this parameter may range anywhere from 0 to 1, as described in [37]. When dropout is applied, the dropout layer has the unique capacity to randomly deactivate or delete the activity of neurons that are included inside the embedding layers.…”

Section: Word Embeddingmentioning

confidence: 99%

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

Sultan,

Mendes,

Kassenkhan

et al. 2023

IJACSA

View full text Add to dashboard Cite

In the face of escalating cyberbullying and its associated online activities, devising effective mechanisms for its detection remains a critical challenge. This study proposes an innovative approach, integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN), for the detection of cyberbullying in online textual content. The method uses LSTM to understand the temporal aspects and sequential dependencies of text, while CNN is employed to automatically and adaptively learn spatial hierarchies of features. We introduce a hybrid LSTM-CNN model which has been designed to optimize the detection of potential cyberbullying signals within large quantities of online text, through the application of advanced natural language processing (NLP) techniques. The paper reports the results from rigorous testing of this model across an extensive dataset drawn from multiple online platforms, indicative of the current digital landscape. Comparisons were made with prevailing methods for cyberbullying detection, demonstrating a substantial improvement in accuracy, precision, recall and F1-score. This research constitutes a significant step forward in developing robust tools for detecting online cyberbullying, thereby enabling proactive interventions and informed policy development. The effectiveness of the LSTM-CNN hybrid model underscores the transformative potential of leveraging artificial intelligence for social safety and cohesion in an increasingly digitized society. The potential applications and limitations of this model, alongside avenues for future research, are discussed.

show abstract

Section: Word Embeddingmentioning

confidence: 99%

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

Sultan,

Mendes,

Kassenkhan

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…In recent years many researchers have used social media data to conduct studies on various topics like market analysis, election result prediction, stock market pre-diction… etc. [16][17][18]. In this research, a detailed literature review was conducted to identify the application of social media data for professional and personal purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Analyzing Tweet Data to Identify the Impact of Demonetarization

Unnithan

Salloum

Shaalan

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

Self Cite

View full text Add to dashboard Cite

In this century, we have witnessed tremendous growth in the application of technology in our day-to-day life. One such life influencing factor is social media. In a manner or other, each individual's life is influenced by social media. Individuals and organizations rely more on social media to interact with people, acquire knowledge, promote business, gather information, news telecast, and for many other professional and personal purposes. Researchers use social media as one of the primary data sources for studies related to people's behavior and for various prediction purposes. This empirical study identifies the importance of social media data in recognizing any event's influence on the public. In October 2016 Indian government had demonetarized some active currency, and it created mixed reactions in public. For this research, tweet data was used to determine the impact of demonetarization on India's people. The study results demonstrate that tweet data can be relied on identifying or extracting general public opinions on any given topic or event.

show abstract

“…Unique features derived from users' profile and contents were extracted and used for training and testing of the model, performance evaluation conducted by experiment on the model using synthetic and real datasets from social network shows 98% accuracy. (Mansoori et al 2020) proposed "Suspicious Activity Detection of Twitter and Facebook using Sentimental Analysis". The model was designed using Natural [24] proposed "Social Media Cyberbullying Detection using Machine Learning" the model leverage on the machine learning capability to detect the language patterns of bullies on social media network which will be used to generate a model that can automatically detect cyberbullying actions on the network.…”

Section: Intrusion Detection Mechanismsmentioning

confidence: 99%

Computing Advances & Trends

2021

View full text Add to dashboard Cite

The goal of this conference series is to bring together researchers and practitioners from academia and industry to focus on understanding computer science and information technology and to establish new collaborations in these areas. Authors are invited to contribute to the conference by submitting articles that illustrate research results, projects, survey work and industrial experiences describing significant advances in all areas of computer science and information technology.

show abstract

Suspicious Activity Detection of Twitter and Facebook using Sentimental Analysis

Cited by 12 publications

References 12 publications

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

Analyzing Tweet Data to Identify the Impact of Demonetarization

Computing Advances & Trends

Contact Info

Product

Resources

About