Improved Model for Detecting Fake Profiles in Online Social Network: A Case Study of Twitter

Ojo, Adebola K.

doi:10.9734/jamcs/2019/v33i430187

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…This finding highlights the significant contribution of this study towards improving bot detection using unsupervised learning algorithms. RandomForest algorithm supervised 95 [23] Random Forest Accuracy supervised 98.42 [24] deep-learning model supervised 95 [22] honeypot model With supervised 85 [25] SVM, Naïve Bayes and Improved Support Vector Machine (ISVM) supervised 90 [26] Neural Network (RNN) supervised 92 [27] k-Nearest Neighbors supervised 93 [28] Random Forest supervised 57.139 The proposed method unsupervised 99.86…”

Section: Comparing the Results With A Supervised Techniquementioning

confidence: 99%

Unsupervised Machine Learning for Bot Detection on Twitter: Generating and Selecting Features for Accurate Clustering

Al-azawi,

O. AL-mamory

2024

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Twitter is a popular social media platform that is widely used by individuals and businesses. However, it is vulnerable to bot attacks, which can have negative effects on society. Supervised machine learning techniques can detect bots but require labeled data to differentiate between human and bot users. Twitter generates a significant amount of unlabeled data, which can be expensive to label. Unsupervised machine learning techniques, specifically clustering algorithms, are crucial for managing this data and reducing computational complexity. Effective feature selection is necessary for clustering, as some features are more important than others. This study aims to enhance feature reliability, introduce new features, and reduce them to improve bot identification accuracy using clustering algorithms. The study achieved an accuracy rate of 0.99 in four clustering algorithms, including agglomerative hierarchy, k-medoids, DBSCAN, and K-means. This was accomplished by minimizing dataset dimensions and selecting essential features. By employing unsupervised machine learning techniques, Twitter can detect and mitigate bot attacks more efficiently, which can positively impact society

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Section: Comparing the Results With A Supervised Techniquementioning

confidence: 99%

Unsupervised Machine Learning for Bot Detection on Twitter: Generating and Selecting Features for Accurate Clustering

Al-azawi,

O. AL-mamory

2024

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“…However, the shortage of a corpus of deceptive news is the main challenge in this field for kinds of models to predict or detect. There are several ways to gather fake news: fake product reviews [21][22][23], fudged online resumes [24], opinion spamming [25][26][27], fake social network profiles [28][29][30], fake dating profiles [31], and forged scientific work. Some data are available but are restricted in content (e.g., to hotels and electronics reviews).…”

Section: Related Workmentioning

confidence: 99%

APSN: Adversarial Pseudo-Siamese Network for Fake News Stance Detection

Zhou

Yang²,

2023

Electronics

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Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic news quickly is an essential purpose for this research to enhance the trustworthiness of news in online social networks, which will be the task studied in this paper. The fake news stance detection can contribute to detect a startling amount of fake news, which aims at evaluating the relevance between the headline and text bodies. There exists a significant difference between news article headline and text body, since headlines with several key phrases are usually much shorter than the text bodies. Such an information imbalance challenge may cause serious problems for the stance detection task. Furthermore, news article data in online social networks is usually exposed to various types of noise and can be contaminated, which poses more challenges for the stance detection task. In this paper, we propose a novel fake news stance detection model, namely Adversarial Pseudo-Siamese Network model (APSN), to solve these challenges. With coupled input components with imbalanced parameters, APSN can learn and compute feature vectors and similarity score of news article headlines and text bodies simultaneously. In addition, by adopting adversarial setting, besides the regular training set, a set of noisy training instances will be generated and fed to APSN in the learning process, which can significantly enhance the robustness of the model. Extensive experiments have been conducted on a real-world fake news dataset, and the experimental results reveal that the presented model exceeds compared suspicious information detection models with significant advantages.

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“…It includes two types of algorithms namely, linear and nonlinear techniques. However, the ideal feature extraction-based dimensionality reduction methods are principal component analysis (PCA) [28,29], statistic methods [11,30,31], linear discriminant analysis (LDA) [32], and manually [18,19]. Here, seven out of twenty-six articles that include four parameters are discussed.…”

Section: The Features Extraction (Fe) Categorymentioning

confidence: 99%

“…Detecting fraudulent accounts in online social networks and creating a model that can precisely describe fake profiles was performed using supervised machine learning techniques and an improved Support Vector Machine (SVM) [28]. The developed method yielded 90% of accuracy in comparison to the Support Vector Machine and Nave Bayes (NB) which achieved 77.4% and 77.3% respectively.…”

Section: The Features Extraction (Fe) Categorymentioning

confidence: 99%

“…The reviewed studies focused on machine learning methods and the datasets that consisted of a combination of private and public accounts. Some researchers used publicly available datasets as a ground truth baseline for testing their techniques [11,28,34,51,54,62]. In general, most studies used the Twitter API to collect datasets, except for [49], which used their API called Twitter4j to collect data.…”

Section: Twitter Datasets Labeled By Usagementioning

confidence: 99%

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Feature extractions and selection of bot detection on Twitter A systematic literature review

Al-azawi

AL-mamory²

2022

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Automated or semiautomated computer programs that imitate humans and/or human behavior in online social networks are known as social bots. Users can be attacked by social bots to achieve several hidden aims, such as spreading information or influencing targets. While researchers develop a variety of methods to detect social media bot accounts, attackers adapt their bots to avoid detection. This field necessitates ongoing growth, particularly in the areas of feature selection and extraction. The study's purpose is to provide an overview of bot attacks on Twitter, shedding light on issues in feature extraction and selection that have a significant impact on the accuracy of bot detection algorithms, and highlighting the weaknesses in training time and dimensionality reduction. To the best of our knowledge, this study is the first systematic literature review based on a preset search-strategy that encompasses literature published between 2018 and 2021 which are concerned with Twitter features (attributes). The key findings of this research are threefold. First, the paper provides an improved taxonomy of feature extraction and selection approaches. Second, it includes a comprehensive overview of approaches for detecting bots in the Twitter platform, particularly machine learning techniques. The percentage was calculated using the proposed taxonomy, with metadata, tweet text, and merging (meta and tweet text) accounting for 37%, 31%, and 32%, respectively. Third, some gaps are also highlighted for further research. The first is that public datasets are not precise or suitable in size. Second, the use of integrated systems and real-time detection is uncommon. Third, detecting each bots category identified separately is needed, rather than detecting all categories of bots using one generic model and the same features' values. Finally, extracting influential features that assist machine learning algorithms in detecting Twitter bots with high accuracy is critical, especially if the type of bot is pre-determined.

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Improved Model for Detecting Fake Profiles in Online Social Network: A Case Study of Twitter

Cited by 5 publications

References 10 publications

Unsupervised Machine Learning for Bot Detection on Twitter: Generating and Selecting Features for Accurate Clustering

Unsupervised Machine Learning for Bot Detection on Twitter: Generating and Selecting Features for Accurate Clustering

APSN: Adversarial Pseudo-Siamese Network for Fake News Stance Detection

Feature extractions and selection of bot detection on Twitter A systematic literature review

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