Selecting the best open source tools for collecting and visualzing social media content

Al‐Qurishi, Muhammad; Al-Rakhami, Mabrook; Alrubaian, Majed; Alarifi, Abdullah; Rahman, Sk. Md. Mizanur; Alamri, Atif

doi:10.1109/wswan.2015.7210346

Cited by 12 publications

(8 citation statements)

References 8 publications

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“…First, the profile information of information sources, such as registration age, is extracted to evaluate the degree of identity-based trust. Then, as a key part of direct evidence database, information sources' history posting together with its number of likes, shares, and comments, is collected in the form of real-time monitoring by streaming application program interface (API), search API, and other available tools [32]. In addition, information sources' social structure is acquired to evaluate the relation-based trust.…”

Section: B Architecture Overviewmentioning

confidence: 99%

Info-Trust: A Multi-Criteria and Adaptive Trustworthiness Calculation Mechanism for Information Sources

Gao

et al. 2019

IEEE Access

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Social media have become increasingly popular for the sharing and spreading of user-generated content due to their easy access, fast dissemination, and low cost. Meanwhile, social media also enable the wide propagation of cyber frauds, which leverage fake information sources to reach an ulterior goal. The prevalence of untrustworthy information sources on social media can have significant negative societal effects. In a trustworthy social media system, trust calculation technology has become a key demand for the identification of information sources. Trust, as one of the most complex concepts in network communities, has multi-criteria properties. However, the existing work only focuses on single trust factor, and does not consider the complexity of trust relationships in social computing completely. In this paper, a multi-criteria trustworthiness calculation mechanism called Info-Trust is proposed for information sources, in which identity-based trust, behavior-based trust, relation-based trust, and feedback-based trust factors are incorporated to present an accuracy-enhanced full view of trustworthiness evaluation of information sources. More importantly, the weights of these factors are dynamically assigned by the ordered weighted averaging and weighted moving average (OWA-WMA) combination algorithm. This mechanism surpasses the limitations of existing approaches in which the weights are assigned subjectively. The experimental results based on the real-world datasets from Sina Weibo demonstrate that the proposed mechanism achieves greater accuracy and adaptability in trustworthiness identification of the network information. INDEX TERMS Multi-criteria, adaptive weight, trust calculation mechanism, information sources, social media.

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Section: B Architecture Overviewmentioning

confidence: 99%

Info-Trust: A Multi-Criteria and Adaptive Trustworthiness Calculation Mechanism for Information Sources

Gao

et al. 2019

IEEE Access

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“…From these components, the event engagement of user u ∈ U on topic p ∈ P is determined using Equation (7).…”

Section: User Popularity Measurementsmentioning

confidence: 99%

“…This information can be valuable for businesses, governmental agencies, and political parties for different purposes, including policy formation, decision‐making, advertising campaigns, and gauging people's awareness of governmental programs. In addition, users' future behavior can be predicted by analyzing data collected from OSNs . Moreover, OSNs can also be used in political campaigns, fund‐raising, and attracting new supporters all over the world.…”

Section: Introductionmentioning

confidence: 99%

Reputation‐based credibility analysis of Twitter social network users

Alrubaian

Al‐Qurishi

Al-Rakhami

et al. 2016

Concurrency and Computation

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Summary This paper addresses the problem of finding credible sources among Twitter social network users to detect and prevent various malicious activities, such as spreading false information on a potentially inflammatory topic, forging accounts for false identities, etc. Existing research works related to source credibility are graph‐based, considering the relationships among users to predict the spread information; human‐based, using human perspectives to determine reliable sources; or machine learning‐based, relying on training classifiers to predict users' credibility. Very few of these approaches consider a user's sentimentality when analyzing his/her credibility as a source. In this paper, we propose a novel approach that combines analysis of the user's reputation on a given topic within the social network, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. In particular, we propose a new reputation metric that introduces several new features into the existing models. We evaluated the performance of the proposed metric in comparison with two machine learning techniques, determining that the accuracy of the proposed approach satisfies the stated purpose of identifying credible Twitter users. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Machine Learning (ML) provides us one of the easiest ways to dig out some sense out of these large amounts of data in simple and efficient ways, specifically from the prediction point of view. ML helps in extracting the most significant data from the piles of data [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…With the technological advancement, the ease of usability for these OSNs is increasing which in turn results in an increase in a number of users. There are billions of active users monthly on these OSNs [1,10]. Instagram, Facebook, Twitter, WhatsApp, and YouTube are among the most famous online social networks which are actively being used by billions of people [11,12].…”

Section: Introductionmentioning

confidence: 99%

Performance Examination and Feature Selection on Sybil User Data using Recursive Feature Elimination

2019

IJITEE

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Machine Learning (ML) research greatly helps in predicting model-based outcomes with high levels of accuracy based upon the training and testing of the models through the datasets. The social networks constitute one of the domains where ML can be used effectively to ensure the authenticity and security of the valid users. With the increase in usage of Online Social Networks (OSNs), the cases of spam and malicious activities can be found in abundance and Sybil nodes pose one such kind of safety and security hazard. Sybil account detection is not an easy task since they mimic the actual behavior of human accounts up to a great extent. In this paper, we look at one such scenario of Sybil accounts on the OSN, Twitter where machine leaning models have been used to train the machine with the existing datasets so as to be able to detect these malicious users before they can bring harm to the normal communication of the genuine users. Since the datasets used are so vast, the process of feature selection has been carried on the datasets as part of pre-processing before the actual classification as it assists in enhancing the model performance. Support Vector Machine–Recursive Feature Elimination (SVM-RFE) and Logistic Regression–Recursive Feature Elimination (LR-RFE) techniques have been used in this study for the selection of significant features. The classification model is trained on the selected features using Random Forest (RF) and K-Nearest Neighbor (KNN) algorithms. We also analyzed the biasing effects of fake accounts on the human accounts datasets during the process of features selection and classification. It has been shown that the RF algorithm outperformed KNN on the feature sets selected through SVM-RFE and LR-RFE.

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Selecting the best open source tools for collecting and visualzing social media content

Cited by 12 publications

References 8 publications

Info-Trust: A Multi-Criteria and Adaptive Trustworthiness Calculation Mechanism for Information Sources

Info-Trust: A Multi-Criteria and Adaptive Trustworthiness Calculation Mechanism for Information Sources

Reputation‐based credibility analysis of Twitter social network users

Performance Examination and Feature Selection on Sybil User Data using Recursive Feature Elimination

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