Rumor Detection in Social Media Using Machine Learning Methods

Bingol, Harun; Alataş, Bilal

doi:10.1109/ubmyk48245.2019.8965480

Cited by 15 publications

(11 citation statements)

References 10 publications

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“…Manually extracted rumor features cannot adapt to the complex environment of social development and change. H. Bingol et al [14] comprehensively evaluated the recognition effect of basic models such as OneR (One Rule), Naive Bayes, ZeroR, JRip, Random Forest, Sequential Minimal Optimization and Hoeffding Tree. It is not enough to mine the shallow features related to rumor posts, so E. Mao et al [7], S. Luo et al [15], G. Cai et al [16] and Z. Zeng et al [17] extracted the sentiment features of rumor texts and used random forest model to achieve good rumor recognition results.…”

Section: Methods Based On Traditional Machine Learningmentioning

confidence: 99%

“…The spatial attention mechanism is used to find the most important part of the input matrix for processing. The calculation formula is shown in Equation (14).…”

Section: ) Amentioning

confidence: 99%

“…The datasets presented in this study are available on https://github. com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/weibo_senti_100k/intro.ipynb (accessed on 2 April 2018), https://github.com/majingCUHK/Rumor_RvNN (accessed on 2 April 2018) and [14,18].…”

Section: Data Availability Statementmentioning

confidence: 99%

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Rumor Detection Based on Attention CNN and Time Series of Context Information

Peng

Wang

2021

Future Internet

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This study aims to explore the time series context and sentiment polarity features of rumors’ life cycles, and how to use them to optimize the CNN model parameters and improve the classification effect. The proposed model is a convolutional neural network embedded with an attention mechanism of sentiment polarity and time series information. Firstly, the whole life cycle of rumors is divided into 20 groups by the time series algorithm and each group of texts is trained by Doc2Vec to obtain the text vector. Secondly, the SVM algorithm is used to obtain the sentiment polarity features of each group. Lastly, the CNN model with the spatial attention mechanism is used to obtain the rumors’ classification. The experiment results show that the proposed model introduced with features of time series and sentiment polarity is very effective for rumor detection, and can greatly reduce the number of iterations for model training as well. The accuracy, precision, recall and F1 of the attention CNN are better than the latest benchmark model.

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Section: Methods Based On Traditional Machine Learningmentioning

confidence: 99%

“…The spatial attention mechanism is used to find the most important part of the input matrix for processing. The calculation formula is shown in Equation (14).…”

Section: ) Amentioning

confidence: 99%

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Rumor Detection Based on Attention CNN and Time Series of Context Information

Peng

Wang

2021

Future Internet

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“…This data cleaning and data preparation process greatly impacts the performance of the models. In this study, before determining whether the URLs in the dataset are spam, the unstructured data in the dataset should be provided in a structured form that can be understood by machine learning classifiers [19]. Data mining basic preprocessing should be applied to the URLs in the dataset at this stage.…”

Section: Datasetmentioning

confidence: 99%

Using and Comparing Machine Learning Techniques for Automatic Detection of Spam Website URLs

Yıldırım

2022

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

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With the developing technology, the issue of cyber security has become one of the most common and current issues in recent years. Spam URLs are one of the most common and dangerous issues for cybersecurity. Spam URLs are one of the most widely used attacks to defraud users. These attacks cause users to suffer monetary losses, steal private information, and install malicious software on their devices. It is very important to detect such threats promptly and to take precautions against these threats. Detection of malicious URLs is mostly done by using blacklists. However, these lists are insufficient to detect newly created URLs. In recent years, machine learning techniques have been developed to overcome this deficiency. In this study, URL classification was made using different machine learning techniques. In the study, 9 different classifiers were preferred for URL classification. The performances of the classifiers were compared in the URL classification process. In addition, similar studies in the literature have been comprehensively examined and these studies have been discussed. In addition, since the preparation of data sets in the natural language processing process has a great effect on the training of models, these steps are discussed in detail.

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“…It is a new subject of study discovered by computer and data scientists. There are many social media and networking problems in the literature such as sentiment analysis [1], fake news detection [2], rumor detection [3], cyberbullying detection [4], customer satisfaction detection [5], link prediction [6], etc. The most important feature that distinguishes hate speech from other problems is that people who post on social networks think that they use their freedom of expression deliberately or unintentionally.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Ant Lion and Moth Flame Optimization-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

Baydogan

Alataş

2021

IEEE Access

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In the online social networks, blogs, microblogs, social bookmarking services and sharing sites, and various web forum pages; the sharing of knowledge, opinions, ideas, etc. are spreading very quickly. This situation brings very dangerous problems in social networks. One of these problems is hate speech detection (HSD) problem which is covering issues such as insults, swearing, humiliation, discrimination, exclusion, detest, abhor, blast, damn, and intolerance. These can be reactions to a person, a group, an organization, an order, or an event. Although few machine learning methods have been used in the literature to solve this important problem in online social media, the performance of the HSD models in terms of many metrics needs to be increased. In this study, an automatic HSD system based on metaheuristic methodology was proposed for better results in this new and important problem. In the proposed optimization approach, Ant Lion Optimization (ALO) algorithm and Moth Flame Optimization (MFO) algorithm were designed for the HSD problem. This is the first attempt to use optimization algorithms as solution search strategies for automatic HSD. An efficient representation scheme and flexible fitness function were designed for this purpose. Many metrics can easily be embedded into the designed fitness function in order to be simultaneously optimized. Firstly, the basic natural language processing (NLP) steps were carried out. Feature extraction was performed using Bag of Words (BoW), Term Frequency (TF), and document vector (Word2Vec). Then, the performances of the proposed novel approaches were analyzed in detail on the three different real-world data. The obtained results were also checked against eight popular supervised machine learning algorithms, Social Spider Optimization (SSO) algorithm, and state-of-the-art Tunicate Swarm Algorithm (TSA). Considering the evaluation criteria for three sets of experiments, it was observed that the accuracy, sensitivity, precision, and f-score results of the ALO and MFO algorithms were superior to machine learning methods. As a result of the experimental studies, the highest accuracy value was 92.1% for ALO, while this value was 90.7% for MFO. Other numerical values obtained in the study were given in the experiments and results section with tables and graphics in detail. Due to the promising results of the proposed approaches, they are anticipated to be used in the solution of many social media and networking problems.INDEX TERMS Hate speech detection, metaheuristic optimization, natural language processing, social network analysis, text mining.

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Rumor Detection in Social Media Using Machine Learning Methods

Cited by 15 publications

References 10 publications

Rumor Detection Based on Attention CNN and Time Series of Context Information

Rumor Detection Based on Attention CNN and Time Series of Context Information

Using and Comparing Machine Learning Techniques for Automatic Detection of Spam Website URLs

Metaheuristic Ant Lion and Moth Flame Optimization-Based Novel Approach for Automatic Detection of Hate Speech in Online Social Networks

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