Ontology-based approach for identifying the credibility domain in social Big Data

Wongthongtham, Pornpit; Abu-Salih, Bilal

doi:10.1080/10919392.2018.1517481

Cited by 53 publications

(33 citation statements)

References 46 publications

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“…The growing popularity of social media articles and micro-blogging systems has created an enormous amount of content and is redefining the way that online information is extracted [33][34][35][36]. Usually, information is generated and shared by users who tend to have knowledge pertaining to a particular domain.…”

Section: Literature Reviewmentioning

confidence: 99%

Time-aware domain-based social influence prediction

et al. 2020

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Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

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Section: Literature Reviewmentioning

confidence: 99%

Time-aware domain-based social influence prediction

et al. 2020

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“…Other studies propose a verification of the consistency of the messages conveyed crossing information at post, user and domain level [1]. The trustworthiness of the social media data is indeed paramount [56]. In this aim, Abu-Salih et al [1,3] developed a credibility framework incorporating semantic analysis and temporal factors to measure the domain-based credibility of the users.…”

Section: Related Workmentioning

confidence: 99%

“…It is also directly linked to the capability to discard user's messages that are classified as spam i.e. unsolicited and repeated junk messages [2][3][4]56]. These tweets come usually from bots and have a malicious intention to create rumours and chaos [46].…”

Section: Related Workmentioning

confidence: 99%

“…A SA system is highly sensitive to the domain in which the data used to train are extracted. It can obtain poor results if the training dataset is not political [4,56]. Due to the lack of a sentiment lexicon for non-English languages, the creation of a new polarity lexicon is decided for the Spanish political event issuing from two different sources.…”

Section: New Polarity Political Lexiconmentioning

confidence: 99%

“…Data credibility may vary depending on the reputation of the data producer or his/her intention to create noise, fake or bad news. The domain of the users also needs to be verified to ensure the tweets can be properly counted for the specific issue studied [56]. This manuscript presents an approach to capture, analyse and predict voting shares of the 2019 President Spanish elections taking into account an analysis of the sentiments expressed by the users of Twitter.…”

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For more than a decade now, with the emergence of Internet 2.0, users have been able to generate their own content and share it publicly with relative ease. This boom has witnessed a huge surge in the popularity of Online Social Networks (OSN), in particular the Twitter microblogging platform which allows its users to share text messages of up to 140 characters with their family, friends and followers [12]. More than 500 million messages, commonly known as tweets, are posted every day [20, 34]. Given that the main reason behind these publications is to express the opinion of the users, they have proven to be great interest to be analysed [14, 38, 48]. This huge amount of content circulating in OSN has attracted the attention of marketing agencies that seek capitalize on the behaviour of clients (current or future) to adjust their online campaigns and even use the social content (number of likes, retweets, etc.) to predict consumer behaviour in the real world [45, 53]. Recently this type of analysis

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Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

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Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data‐mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data‐mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data‐mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge‐based methods or big‐data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

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Ontology-based approach for identifying the credibility domain in social Big Data

Cited by 53 publications

References 46 publications

Time-aware domain-based social influence prediction

Time-aware domain-based social influence prediction

Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections

Semantic data mining in the information age: A systematic review

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