Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach

Halawi, Bahia; Mourad, Azzam; Otrok, Hadi; Damiani, Ernesto

doi:10.1109/access.2018.2877685

Cited by 22 publications

(14 citation statements)

References 30 publications

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“…Nevertheless, quite few researchers have analyzed fake accounts detection as based on a semantic approach and ontology engineering. A novel ontology-based method was proposed to identify dubious content on Twitter during instances or events where tweets are linked to ontologies of specific themes, eventually in order to verify the similarities between tweet texts and ontologies dealing with relevant subjects [17]. A new spam detection algorithm has been presented, called Social Event Detection, and it is based on ontology Multiple steps are introduced, beginning with the creation of the ontology, attribute extraction, the correlation of words to the current class context and ending with the identification of whether it is spam or not [18].…”

Section: Semantic Modelling Approachmentioning

confidence: 99%

Twitter Fake Account Detection and Classification using Ontological Engineering and Semantic Web Rule Language

Jabardi¹,

Hadi²

2020

Karbala International Journal of Modern Science

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Section: Semantic Modelling Approachmentioning

confidence: 99%

Twitter Fake Account Detection and Classification using Ontological Engineering and Semantic Web Rule Language

Jabardi¹,

Hadi²

2020

Karbala International Journal of Modern Science

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“…This means scholars using data obtained via API need to apply caution when drawing inferences from such data. In particular, it has been observed that the source of biases arise from the order connected nodes are returned [23] based on the age of the link created between two nodes and on the fixed time-frames used for selecting the nodes to be included in the sample APIs [41] .…”

Section: Related Workmentioning

confidence: 99%

“…In the present work, we propose a mining platform to help researchers and data collector to mine and directly analyze social networks, defining API-specific and budget-constrained strategies able to filter data collection based on concurrent sampling and ontology-enhanced filtering algorithms [23] . To test our approach we exploited it in creating a content-based recommender system.…”

Section: Introductionmentioning

confidence: 99%

Ontology based recommender system using social network data

Arafeh

Ceravolo

Mourad

et al. 2021

Future Generation Computer Systems

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Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis.

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“…This reduces replication and maintains consistency once an ontology is stable, but also means they can be leveraged by other models. For example, [87] generates separate ontologies within an evaluation model to categorise tweets and detect spam.…”

Section: Semantic Modeling Rdf/rdfs and Ontologies A Semantic mentioning

confidence: 99%