Botfarms: How you can counter them and use them to your own advantage

The general social perception of a bot farm or troll farm is an organized group of people, usually employing bots or algorithms, who write information or posts profitable to or in the interests of a paying customer, and post these on the Internet using inauthentic, special-purpose accounts. Bot farms have come to the attention of cybersecurity researchers and scholars worldwide, including research organizations such as the Oxford Internet Institute and Facebook’s cybersecurity department. They have increased in profile due to recurring scandals involving the use of bot farms during political campaigns (often to affect the outcome of elections) and as active methods of unfair competition used to weaken or destroy adversaries in politics, business, and other fields.

The Kyiv office of the European Academy of Sciences of Ukraine has similarly hosted a closed meeting under the auspices of the Information Security Institute on the subject of bot farms. The focus of the meeting was on practical measures available to individuals and companies to counteract bot farms.

At this event, information security expert Andrey Frantsev spoke on the subject of counteracting bot farms. Dr. Frantsev suggests that the impact of bot farms can be minimized by constructing an “information shield”. To develop such a shield, it is important to understand how reputation and public opinion are formed. Professor Massimo Introvigne, an expert on the shadowy tactics of religious groups, also presented his model of how public opinion is formed and how this is exploited by malicious actors. This model suggests that public opinion is usually built from the top down, through a pyramid of opinion leaders influencing followers at different levels. An information shield is formed by introducing barriers to information attack at all levels of the pyramid. If relevant materials appear regularly at all levels, it is easy to neutralize the impact of a bot farm or troll farm. This model is also well-suited for modeling and classifying information attacks and countermeasures against them.



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