Deceptive review detection using labeled and unlabeled data

Rout, Jitendra Kumar; Singh, Swaran; Jena, Sanjay Kumar; Bakshi, Sambit

doi:10.1007/s11042-016-3819-y

Cited by 77 publications

(47 citation statements)

References 26 publications

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“…Digital forensics, including multimedia forensics, is an increasingly important research focus due to the digitalization of our society [1,9,16]. This is also evidenced by the work of Yang et al [21], and Azfar et al [3] in this special issue.…”

Section: Forensicsmentioning

confidence: 87%

Guest Editorial: Multimedia Social Network Security and Applications

Zhang

Choo

2016

Multimed Tools Appl

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In the increasingly inter-connected society, multimedia social networks (MSN) have become a 'mainstream' tool used by online users to connect and share contents with other users 24/7 in real-time. It is, therefore, unsurprising that MSNs have become a salient area of inquiry by computer scientists and computer security researchers. For example, researchers need to design intelligent computing and soft computing technologies to improve multimedia system functions, efficiency and performance, as well as improving user's sharing experiences (e.g. using recommendation systems and more effective algorithms). Ensuring the security of users and data are also an ongoing topic of interest and importance due to the ease in producing and sharing user and multimedia content using MSNs. In recent years, we have seen advances in multimedia system security and soft computing for MSN applications, such as Bhard^security mechanisms (e.g. protocols and methodologies) and Bsoft^computing methods (e.g. machine learning and rough set), as well as research efforts in trust assessment, risk management and social factors to understand the trade-off between the effectiveness and security in MSNs.This special issue is dedicated to the reporting of state-of-the-art and recent advancements in this emerging area of enquiry. We received 26 submissions for this special issue, of which 12 were accepted for publication (i.e. 46 % acceptance rate). Each paper went through a rigorous peer review process, in addition to multiple follow-up rounds with the authors. A summary of the papers is categorized and presented below.

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Section: Forensicsmentioning

confidence: 87%

Guest Editorial: Multimedia Social Network Security and Applications

Zhang

Choo

2016

Multimed Tools Appl

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“…Therefore, the identification technology of spammers has been extensively studied. According to different areas, the current research areas on spammer identification technologies can be divided into the e-mail field, the field of social networks, the field of news media, the e-commerce field [20]- [22]. According to different research methods, it can be divided into spammer identification technologies based on user behavioral features, semantic features, and environmental features [28], [29].…”

Section: Related Workmentioning

confidence: 99%

An Effective Identification Technology for Online News Comment Spammers in Internet Media

Sun

Zhang

et al. 2019

IEEE Access

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With the development of mobile Internet, it is changing the way we communicate with others. Internet media have gradually become the main mobile crowdsourcing applications for information dissemination and user communication, including online news and social networks. However, the potential business opportunities have stimulated the emergence of a large number of spammers, who release false speech, advertisements, pornographic contents, and phishing websites on the media to gain commercial benefits, which seriously affects the experience of normal users. Therefore, in order to reduce the harm of false information, the research on the identification technology of spammers has been carried out extensively. However, the traditional technologies of identifying spammers involve high data costs and poor effects, and most of them are concentrated in the field of social networks, while less research is carried out in the field of online news. In this paper, we propose an effective technology of identifying online news comment spammers based on the label propagation algorithm (LPA), making full use of the user comment behaviors and contents. First of all, we collect a large amount of news and comments from NetEase News and label some users in the data as spammers or normal users manually to construct a labeled dataset. Then, a set of behavioral and semantic features are extracted and quantified from the user comment behaviors and comment contents by statistical analysis. Next, we propose the identification technology based on the LPA. Finally, the set of feature values is input into the proposed technology in different combinations, and experiments and evaluations are carried out to determine the most effective combination of features and improve the technology. The results show that the technology proposed in this paper involves a lower data cost but a better identification effect than some traditional technologies based on the supervised classifier. INDEX TERMS Spammer identification, Internet media, online news comment, label propagation algorithm, mobile crowdsourcing applications.

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“…Different techniques use different features. These can be divided into two main groups: features related to the review and features related to the reviewer (Jindal and Liu 2007;Li et al 2011;Rout et al 2017). Some previous work singles out quantity, specificity, diversity, non-immediacy, as well as task specific features such as affect, expressivity, complexity, uncertainty, and informality (Zhou et al 2004;Fuller et al 2006).…”

Section: Review Spam Detectionmentioning

confidence: 99%

Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets

Cocarascu

Toni

2018

Computational Linguistics

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The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information. Concretely, we define a deep learning method for relation–based argument mining to extract argumentative relations of attack and support. We then use this method for determining whether news articles support tweets, a useful task in fact-checking settings, where determining agreement toward a statement is a useful step toward determining its truthfulness. Furthermore, we use our method for extracting bipolar argumentation frameworks from reviews to help detect whether they are deceptive. We show experimentally that our method performs well in both settings. In particular, in the case of deception detection, our method contributes a novel argumentative feature that, when used in combination with other features in standard supervised classifiers, outperforms the latter even on small data sets.

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Deceptive review detection using labeled and unlabeled data

Cited by 77 publications

References 26 publications

Guest Editorial: Multimedia Social Network Security and Applications

Guest Editorial: Multimedia Social Network Security and Applications

An Effective Identification Technology for Online News Comment Spammers in Internet Media

Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets

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