Twitter Fake Account Detection and Classification using Ontological Engineering and Semantic Web Rule Language
Nowadays, Twitter has become one of the fastest-growing Online Social Networks (OSNs) for data sharing frameworks and microblogging. It attracts millions of users worldwide where subscribers communicate with each through posts and messages known as "tweets". The open structure and behaviour of Twitter cause it to be vulnerable to attacks from fake accounts and a large number of automated software, known as 'bots'. Bots are regarded to be malicious as they send spam to users of social networks over the internet. Data security and privacy are among the most critical issues of social network users, as the protection and fulfilment of these requirements strengthen the network's interest and, ultimately, its credibility. To overcome these issues, we need to build an efficient model to detect and classify fake twitter accounts. This paper presents a new approach with dual functions, namely to identify and classify the twitter bots based on ontological engineering and Semantic Web Rule Language (SWRL) rules. Web Ontology Language (OWL), Semantic Web Rule Language (SWRL) rules, and reasoners are deployed to inductively learn the rules that distinguish a fake account (bot) from a real one, as well as to classify fake accounts into fake followers or spam bot. Our approach could properly identify the false account with an accuracy of (97%) in the first stage, after which these fake accounts were classified into spam or fake follower bots with an accuracy rate of (94.9%). Furthermore, it has been found that he ontology classifier is a more interpretable model that offers straightforward and human-interpretable decision rules, as compared to other machine learning classifiers.
JABARDI, MOHAMMED and Hadi, Asaad Sabah
"Twitter Fake Account Detection and Classification using Ontological Engineering and Semantic Web Rule Language,"
Karbala International Journal of Modern Science: Vol. 6
, Article 8.
Available at: https://doi.org/10.33640/2405-609X.2285
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