AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users

Deverajan, Ganesh Gopal; Nagarajan, Senthil Murugan; Amanullah, Sardar Irfanullah; Mary, S. A. Sahaaya Arul

doi:10.1109/tcss.2023.3259480

Cited by 16 publications

(3 citation statements)

References 27 publications

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“…25 Bovet et al used causal models to reveal how false messages affect the presidential election. 32 These models and methods, whether they are just the fiery improvements of the infectious disease models or new methods, show that the research on information dissemination in OSNs has entered into the stage of enthusiastic development.…”

Section: Related Workmentioning

confidence: 99%

Modeling the dissemination of privacy information in online social networks

Zhu,

Li,

Pan

et al. 2024

Trans Emerging Tel Tech

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Driven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

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Section: Related Workmentioning

confidence: 99%

Modeling the dissemination of privacy information in online social networks

Zhu,

Li,

Pan

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…While current academics are focusing on social characteristics of the news, traditional detection approaches were centered around content analysis. A unique deep natural language processing (NLP) approach for artificial intelligence (AI)-assisted false news detection has been proposed [1]. The publisher layer, social media networking layer, enabled edge layer, and cloud layer are the four levels that define the suggested work.…”

Section: Nlp Model Analysismentioning

confidence: 99%

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Ajayaghosh

2024

IJRASET

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In an age of widespread misinformation, creating accurate machine learning algorithms is vital for ensuring the integrity of information sharing. This project aims to develop a machine learning model capable of distinguishing between fake and true news articles. [1]The model utilizes a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network architecture. Exploratory data analysis techniques are employed to gain in-sights into the characteristics and distributions within the dataset. Data visualization techniques aid in understanding patterns and relationships within the dataset. Additionally, unigram analysis is conducted to extract meaningful features from the text data. The datasets are then prepared for model training, involving preprocessing steps such as tokenization and vectorization. Fi-nally, the Bi-LSTM model is constructed, leveraging its ability to capture long-range dependencies in sequential data. The model is trained on the prepared datasets, optimized using appropri-ate techniques, and evaluated using metrics such as accuracy, precision, recall, and F1-score. The Bi-LSTM architecture offers the advantage of capturing long-range dependencies in sequential data, thereby enhancing the model’s ability to discern nuanced patterns in news articles. The primary objective of this project is to develop a robust and accurate system for automatically detecting fake news, thus playing a pivotal role in enhancing the dissemination of reliable information in the digital age.

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“…We compare the algorithms in precision, recall, F1-Score (as shown in Eqs. (19)(20)(21)) [32], the number of selected features and running time.…”

Section: Objective Functionmentioning

confidence: 99%

Equilibrium Optimizer for Emotion Classification From English Speech Signals

Yue,

Hu,

Chu

et al. 2023

IEEE Access

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Speech emotion recognition and its precise classification are challenging tasks that heavily depend on the quality of feature extraction and selection for speech signals. Many feature selection algorithms have been proposed to achieve recognition, however, their accuracy has not reached a satisfactory level. We introduce an improved equilibrium optimizer (iEO) algorithm and utilize mel frequency cepstral coefficients (MFCCs) and pitch features for emotion recognition. The transfer function is used to complete the binarization of iEO (BiEO), and the algorithm adopts multi-swarm and transfer functions to balance global search and local search. The performance of the proposed algorithm is verified using four English speech emotion datasets, eNTERFACE05, ryerson audio-visual database of emotional speech and song (RAVDESS), surrey audio-visual expressed emotion (SAVEE) and toronto emotional speech set (TESS). The experimental results illustrate that the proposed algorithm obtains an accuracy of 0.4923, 0.5581, 0,5575 and 0.9840 in eNTERFACE05, RAVDESS, SAVEE and TESS based on K-nearest neighbors, and an accuracy of 0.5279, 0.5862, 0.6752 and 0.9941 based on random forest.

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AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users

Cited by 16 publications

References 27 publications

Modeling the dissemination of privacy information in online social networks

Modeling the dissemination of privacy information in online social networks

AI Assisted Deceptive Content Analysis System Using Bi-LSTM

Equilibrium Optimizer for Emotion Classification From English Speech Signals

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