Social Media Network Bots Identification and Analysis
With the rapid growth of Online Social Networks (OSN), their direct involvement in our daily lives and with the enormous number of users, OSNs have become the perfect target for people to use in a harmful way. The OSNs are injected with huge number of fake accounts or automated accounts (bots) that...
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| Format: | Dissertation |
| Language: | English |
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ProQuest Dissertations & Theses
01-01-2015
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| Online Access: | Get full text |
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| Summary: | With the rapid growth of Online Social Networks (OSN), their direct involvement in our daily lives and with the enormous number of users, OSNs have become the perfect target for people to use in a harmful way. The OSNs are injected with huge number of fake accounts or automated accounts (bots) that try to spread malicious links and harmful data or try to benefit others on their agenda.In this thesis, a framework for Twitter is proposed. Our proposed framework uses specific features extracted from Twitter profiles along with machine learning algorithms in order to identify bot accounts on Twitter. The extracted features can differ between human’s behavior on Twitter and bot’s. After extracting the necessary features, the machine learning algorithms produce a set of rules that can distinguish bot accounts from real accounts.Several tests has been conducted on a dataset collected by multiple methodologies for number of Twitter accounts. The framework shows an amazing performance by having an accuracy of 96% and above in detecting bot accounts. |
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| ISBN: | 9798535590820 |