Difference between revisions of "Projects:2017s1-167d Twitterbots"
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| − | Automated accounts on Twitter known as Twitterbots are being used for malicious purposes. There is a particular concern in the use of Twitterbots in information warfare to influence politics or cause civil unrest. Research has been conducted into methods that could be used to identify Twitterbots with the aim of removing their presence from Twitter. The aim of this project is to evaluate the Twitterbot identification methods that have been proposed and to propose a new framework for Twitterbot identification. The techniques examined include human-based methods, supervised machine learning, activity correlation, monitoring user activity and clustering. In addition a botnet case study has been examined that shows that a targeted approach to Twitterbot identification can be effective | + | Automated accounts on Twitter known as Twitterbots are being used for malicious purposes. There is a particular concern in the use of Twitterbots in information warfare to influence politics or cause civil unrest. Research has been conducted into methods that could be used to identify Twitterbots with the aim of removing their presence from Twitter. The aim of this project is to evaluate the Twitterbot identification methods that have been proposed and to propose a new framework for Twitterbot identification. The techniques examined include human-based methods, supervised machine learning, activity correlation, monitoring user activity and clustering. In addition a botnet case study has been examined that shows that a targeted approach to Twitterbot identification can be effective. |
Latest revision as of 23:34, 5 December 2017
Automated accounts on Twitter known as Twitterbots are being used for malicious purposes. There is a particular concern in the use of Twitterbots in information warfare to influence politics or cause civil unrest. Research has been conducted into methods that could be used to identify Twitterbots with the aim of removing their presence from Twitter. The aim of this project is to evaluate the Twitterbot identification methods that have been proposed and to propose a new framework for Twitterbot identification. The techniques examined include human-based methods, supervised machine learning, activity correlation, monitoring user activity and clustering. In addition a botnet case study has been examined that shows that a targeted approach to Twitterbot identification can be effective.
