Fake Media Detection Based on Natural Language Processing and Blockchain Approaches

Shahbazi, Zeinab; Byun, Yung-Cheol

doi:10.1109/access.2021.3112607

Cited by 39 publications

(17 citation statements)

References 34 publications

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“…The DF attainment involves steps of proofing the criminal cases regarding location, security, data, etc. [ 32 ]. The data provided from social media is more understandable and easy to access for users [ 33 , 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

Shahbazi

Byun

2022

IJERPH

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Social media evidence is the new topic in digital forensics. If social media information is correctly explored, there will be significant support for investigating various offenses. Exploring social media information to give the government potential proof of a crime is not an easy task. Digital forensic investigation is based on natural language processing (NLP) techniques and the blockchain framework proposed in this process. The main reason for using NLP in this process is for data collection analysis, representations of every phase, vectorization phase, feature selection, and classifier evaluation. Applying a blockchain technique in this system secures the data information to avoid hacking and any network attack. The system’s potential is demonstrated by using a real-world dataset.

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Section: Related Workmentioning

confidence: 99%

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

Shahbazi

Byun

2022

IJERPH

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“…Thus, from the analysis, the best classifier was selected for both higher accuracy and efficiency. In 2021, Shahbazi and Byun (2021) have implemented an integrated model for different criteria of natural language processing and block chain for applying machine learning approaches for detecting fake news and offered a better prediction on posts and accounts on fake users. They used reinforcement learning approach that was used for this process.…”

Section: Existing Fake News Detection Model Approachesmentioning

confidence: 99%

A systematic literature review and existing challenges toward fake news detection models

Shah

Ganatra

2022

Soc. Netw. Anal. Min.

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Emerging of social media creates inconsistencies in online news, which causes confusion and uncertainty for consumers while making decisions regarding purchases. On the other hand, in existing studies, there is a lack of empirical and systematic examination observed in terms of inconsistency regarding reviews. The spreading of fake news and disinformation on social media platforms has adverse effects on stability and social harmony. Fake news is often emerging and spreading on social media day by day. It results in influencing or annoying and also misleading nations or societies. Several studies aim to recognize fake news from real news on online social media platforms. Accurate and timely detection of fake news prevents the propagation of fake news. This paper aims to conduct a review on fake news detection models that is contributed by a variety of machine learning and deep learning algorithms. The fundamental and well-performing approaches that existed in the past years are reviewed and categorized and described in different datasets. Further, the dataset utilized, simulation platforms, and recorded performance metrics are evaluated as an extended review model. Finally, the survey expedites the research findings and challenges that could have significant implications for the upcoming researchers and professionals to improve the trust worthiness of automated fake news detection models.

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“…A genetic algorithm is mainly used in the detection process, reducing the computation cost and time in both the classification and identification processes. The genetic algorithm improves the accuracy rate of fake account detection, providing various security policies to other users [3]. Fake accounts are identified based on particular features and patterns publicly available to all users.…”

Section: Introductionmentioning

confidence: 99%

The Security System in Information Technology Detects Fake Accounts on The Youtube Platform

2023

IJIES

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YouTube platform provides paid/ unpaid video services for different users for promotions, education, learning, information sharing, etc. Accessing this platform's content/ channel-based advancements is based on user identification through registration/ credential sharing. In recent years, accessing information through fake accounts has increased other users' illegitimacy and security demands. The article introduces a temporal behavioral method (TBM) using the classified learning (CL) technique to address the impact of fake users on YouTube-like platforms. The proposed method eyes on user credentials, interactions, and access information under different usage series. The data used for the classification learning identifies the active, inactive, and false searches/ information accessed by the user. With the recent meetings removed, the static and inaccurate searches are segregated using the learning process from the last known access session. Rather than applying new rules to the identified account/user, false user flagging reported data sharing and illegitimacy is all avoided. Based on the different session logins and the recommendations by the learning process, the fraudulent users are restricted from communicating with other users and legitimate information. Maximizing data sharing and user detection is facilitated by the better recognizability afforded by the prolonged inactive session categorization following in succession. The suggested TBM-CL strategy addresses the impact of fake users on YouTube-like platforms and increases the maximums for user detection (14.93%), information sharing (11.3%), and classifications (15.93%) across all sessions. It decreases the erroneous ratio by 8.03% and the delay by 9.07% based on the dataset.

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Fake Media Detection Based on Natural Language Processing and Blockchain Approaches

Cited by 39 publications

References 34 publications

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

A systematic literature review and existing challenges toward fake news detection models

The Security System in Information Technology Detects Fake Accounts on The Youtube Platform

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