News Credibility Measure Utilizing Ontologies &amp; Semantic Weighing Schemes (NCMOSWS)

Negm, Tarek M; Rezqa, Mohamed A.; Hegazi, Abdel Fatah

doi:10.1109/worlds4.2018.8611614

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…Five experts gave same opinion for the placement order of OSN posts while 3 experts came with different opinion, a conflict was resolved through conflict resolution 5 and consensus techniques. Different semantic matching approaches has proposed in literature [6,8,9,30] to calculate semantic similarity among the verifiable post (VP) and pool of posts (PO). But, it is not defined evidently that how to calculate the semantic similarity among the verifiable post (VP) and pool of posts (PO).…”

Section: Resultsmentioning

confidence: 99%

“…In 2016, Jing Yuan Xu et al [9] proposes an approach to discover an appropriate learned function to answer query by using an OWL ontology. Another study in 2018 [8] introduced an approach through building NCMOSWS (News Credibility Measure Utilizing Ontologies & Semantic Weighing Schemes), a model which uses well created weighing semantic approaches along with ontology to evaluate news authenticity. The proposed model is applied on RSS of a set of news organizations and illustrate that model is reliable to measure partial trustworthiness of news.…”

Section: Rumors Identification In Osns Using Ontologymentioning

confidence: 99%

“…Existing studies [6] [7] focused on the conceptual criteria that result in no ambiguity among shared data but weak ontology criteria. For instance, several ontologies [8] [7] [9] presented for rumors verification, considers only a few requirements for instance concept of roles and categorization [6] [7]. However, ontology-based systems should consider roles, rating, confidence level, location, and post categorization requirements while decision-making about any OSN post.…”

Section: Introductionmentioning

confidence: 99%

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Ontology-based rumor identification system using social network data

Sarfraz

Ahmad

Zeshan

et al. 2022

Preprint

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With the tremendous expansion of online social networks (OSNs) such as Twitter and Facebook, have become not only one of the utmost effective modes for viral advertising. But also, the main source of news among people nowadays. Besides these encouraging features, however, comes the risk of rumor dissemination. Due to the enormous users of OSN, there is a considerable challenge to proficiently contain the viral spread of rumors. In addition, because of a huge number of news on OSN, the manual detection of rumors is not possible. So, we need an auto-detection and authentic knowledge sharing method in a machine-readable as well as the human-readable format for OSN. In this regard, several researchers, doing research in this domain. But no method deals with uniform, authentic, and machine-readable as well as human-readable knowledge sharing among OSN users. To address this issue, ontology-based systems can be used because ontology-based systems deal with all such kinds of issues very effectively. However, for effective categorization of OSN posts, ontology-based systems should consider roles, rating, confidence level, location, and post categorization while decision making about OSN posts. Whereas, several ontology-based methods presented for rumors verification, consider only a few requirements resulting in low accuracy of authenticated posts. Experimental results verify that the proposed ontology-based approach can produce accurate results according to human understanding.

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Section: Resultsmentioning

confidence: 99%

Section: Rumors Identification In Osns Using Ontologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ontology-based rumor identification system using social network data

Sarfraz

Ahmad

Zeshan

et al. 2022

Preprint

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“…Credibility in OSNs can be modeled by two methods: classification-based and credibility propagation. A classification-based approach uses supervised learning algorithms [130]. On the other hand, the credibility propagation approach constructs a network to propagate credibility scores among users, tweet contents, events and activities [88].…”

Section: ) Credibility-based Modelsmentioning

confidence: 99%

“…Negm et al [130] used 5Ws (i.e., who, what, when, where, and why) credibility to distinguish credible news and RSS (Rich Site Summary) files from news agencies to extract publication dates, headlines, contents, and locations to feed into different algorithms to calculate the credibility of a news agency. The compared algorithms include TF-IDF, TF-IDF with location, Latent Semantic Index (LSI), and TF with LSI and log entropy.…”

Section: ) Credibility-based Modelsmentioning

confidence: 99%

Online Social Deception and Its Countermeasures: A Survey

et al. 2021

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We are living in an era when online communication over social network services (SNSs) have become an indispensable part of people's everyday lives. As a consequence, online social deception (OSD) in SNSs has emerged as a serious threat in cyberspace, particularly for users vulnerable to such cyberattacks. Cyber attackers have exploited the sophisticated features of SNSs to carry out harmful OSD activities, such as financial fraud, privacy threat, or sexual/labor exploitation. Therefore, it is critical to understand OSD and develop effective countermeasures against OSD for building trustworthy SNSs. In this paper, we conduct an extensive survey, covering (i) the multidisciplinary concept of social deception; (ii) types of OSD attacks and their unique characteristics compared to other social network attacks and cybercrimes; (iii) comprehensive defense mechanisms embracing prevention, detection, and response (or mitigation) against OSD attacks along with their pros and cons; (iv) datasets/metrics used for validation and verification; and (v) legal and ethical concerns related to OSD research. Based on this survey, we provide insights into the effectiveness of countermeasures and the lessons learned from the existing literature. We conclude our survey with in-depth discussions on the limitations of the state-of-the-art and suggest future research directions in OSD research.

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