Facebook Profile Credibility Detection using Machine and Deep Learning Techniques based on User’s Sentiment Response on Status Message

Afify, Esraa A.; Sharaf, Ahmed I.; Ayman, E

doi:10.14569/ijacsa.2020.0111273

Cited by 6 publications

(18 citation statements)

References 10 publications

(9 reference statements)

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“…Unsupervised Learning: It does not require labeled data and can be processed by clustering methods. Clustering is a typical example of unsupervised learning that finds visual classifications that match the hypothesis [14]. The goal of clustering is to find similarities, regardless of the kind of data.…”

Section: Methods and Implementation Of The Casementioning

confidence: 99%

“…The authors in [14], [15], [16] implemented an analysis model using machine learning classifiers, to measure and predict user profile credibility, the implemented model was evaluated on two different datasets with term frequency and three inverse document frequency variables. Similarly, in [17], [18], [19] identified features that can be useful for predicting whether a tweet or comment is a rumor or information, using a rule-based approach involving regular expressions to categorize sentences.…”

Section: Related Workmentioning

confidence: 99%

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Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Sierra-Linan³

et al. 2022

IJACSA

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Today, web content such as images, text, speeches, and videos are user-generated, and social networks have become increasingly popular as a means for people to share their ideas and opinions. One of the most popular social media for expressing their feelings towards events that occur is Twitter. The main objective of this study is to classify and analyze the content of the affiliates of the Pension and Funds Administration (AFP) published on Twitter. This study incorporates machine learning techniques for data mining, cleaning, tokenization, exploratory analysis, classification, and sentiment analysis. To apply the study and examine the data, Twitter was used with the hashtag #afp, followed by descriptive and exploratory analysis, including metrics of the tweets. Finally, a content analysis was carried out, including word frequency calculation, lemmatization, and classification of words by sentiment, emotions, and word cloud. The study uses tweets published in the month of May 2022. Sentiment distribution was also performed in three polarity classes: positive, neutral, and negative, representing 22%, 4%, and 74% respectively. Supported by the unsupervised learning method and the K-Means algorithm, we were able to determine the number of clusters using the elbow method. Finally, the sentiment analysis and the clusters formed indicate that there is a very pronounced dispersion, the distances are not very similar, even though the data standardization work was carried out.

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Section: Methods and Implementation Of The Casementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Iparraguirre-Villanueva¹,

Guevara-Ponce²,

Sierra-Linan³

et al. 2022

IJACSA

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“…SML, a subfield within artificial intelligence, centers on the creation of algorithms and models capable of autonomously learning from training data and making predictions or decisions, as depicted in Figure 1 [13]. SML finds widespread application across various domains, including detection natural language processing.…”

Section: Supervised Machine Learning (Sml)mentioning

confidence: 99%

“…Sentiment-related features: These features delve into a user's expression of their opinions, feelings, or assessments of products, events, information, or services through online text [5,[11][12][13].…”

mentioning

confidence: 99%

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

Abid-Althaqafi,

Alsalamah

2024

Electronics

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In the era of digital information, online platforms play a crucial role in shaping public opinion. However, the extensive spread of misinformation and fake news poses a significant challenge, largely fueled by non-credible users. Detecting user credibility is vital for ensuring the reliability of information on these platforms. This study employs supervised machine learning algorithms, leveraging key user features to enhance credibility detection. Feature selection methods, specifically SelectKBest and correlation-based algorithms, are explored for their impact on X-Platform user credibility detection. Utilizing various classifiers, including support vector machine, logistic regression, and XGBoost, experiments are conducted on the ArPFN dataset, which is a labeled, balanced, publicly available dataset. The evaluation includes measures like accuracy, precision, recall, and F1-score to assess efficiency. This research considers feature categories and selection methods with SML to detect their impact on the accuracy of X-Platform user credibility detection, making this research a reference for researchers and practitioners working in the field of SML, feature engineering, and social media analysis. We aim to advance the field’s understanding of effective strategies for mitigating the spread of fake news. The novelty of this study lies in the comprehensive exploration of feature selection methods and their influence on credibility detection, contributing valuable insights for future research in this domain.

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“…Different researchers interested in the field of NLP are concerned with applying and studying deep learning algorithms in order to explore many results for the reasons of development and improvement [31]. Deep learning is a www.ijacsa.thesai.org coherent and integrated set of algorithms that interpret and link data to each other in order to achieve the greatest degree of accuracy in order to identify and extract new information that was previously unknown [33]. However, the method of learning these algorithms is a representation of the way human brain cells work in transmitting and interacting signals.…”

Section: Introductionmentioning

confidence: 99%

A Proposed Deep Learning based Framework for Arabic Text Classification

Sayed¹,

Abdelkader²,

Khedr³

et al. 2022

IJACSA

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Deep learning has become one of the crucial trends in the modern era due to the huge amount of data that has become available. This paper aims to investigate and improve a generic framework for Arabic Text Classification (ATC) with different deep learning techniques. Besides, it deals directly with a word in its original style as a basic unit of modern Arabic sentence and on a different level of N-grams versus a combination of Intersected Consecutive Word proposed method (ICW). However, it aimed to discuss the results of the different experiments for the enhancements of the proposed method on different deep learning algorithms such as Scaled Conjugate Gradient (SCG) and Gradient descent with momentum and adaptive learning rate backpropagation (GDX) on ATC. The results showed that the proposed framework applied with the SCG algorithm and TF-IDF outperforms the GDX algorithm with an accuracy ratio of 90.65%.

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Facebook Profile Credibility Detection using Machine and Deep Learning Techniques based on User’s Sentiment Response on Status Message

Cited by 6 publications

References 10 publications

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

Sentiment Analysis of Tweets using Unsupervised Learning Techniques and the K-Means Algorithm

The Effect of Feature Selection on the Accuracy of X-Platform User Credibility Detection with Supervised Machine Learning

A Proposed Deep Learning based Framework for Arabic Text Classification

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