Social media has change the way we all interact with each other across the world. It can be many different types of media from text to image, and even touch. It can also cover many different ranges from across the world to you’re friend sitting next to you. This flexibility is one of the traits which gives social media this appeal as it creates a piece of the internet for everyone.
The internet was initially created without security in mind – internet was a better place, it was a simplier place. As the internet grew, malicious intent grew through virus and exploits which resulted in countermeasures such as encryption. Malicious intent can be “physical” like DOS attacks and leaking of passwords, but it can also be social like astroturfing attacks.
Astroturfing is “the attempt to create an impression of widespread grassroots support for a policy, individual, or product, where little such support exists”. In layman’s terms, we are creating a “movement” for something through the seeding from a mass of people. The attack is similar to DOS, where a set of hacked or created accounts are used to post about a certain topic. It is becoming a very widespread and effective means of advertisement, but manipulates the social media leading to skewed impressions and misrepresentations.
Relating this back to graph theory, using graphs to represent different characteristics we are able to visualize relationships between these astrobots. In the diagram below, the edges represent a keyword relationship between astrobots, which are nodes. From this picture, we can see different communities from the clusters with represent different types of account, such as suspended accounts, used to as a bot. By using a signed colored graph, where a positive sign is given to nodes of a similar type and negative otherwise, we are able to develop communities in relationship the keyword attacks.
Countering astrobots can be a very hairy situation, since depending on the type of bot, there can be side-effects from actions such as suspsension and/or banning. For example, these bots can be affected without the user’s acknowledgement, therefore suspension would led to anger or fustration from the original owner. By using graph theory, we are able to learn about the motives and characteristics of astrobots from different perspectives. Using this information, we are able to prevent this from happening and apply the most appropriate action without affecting actual users.
References
Elmas, Tuğrulcan, et al. “Lateral Astroturfing Attacks on Twitter Trending Topics.” ArXiv.org, 17 Oct. 2019,
https://arxiv.org/pdf/1910.07783.pdf.
Bienkov, Adam. “Astroturfing: What Is It and Why Does It Matter? | Adam Bienkov.” The Guardian, Guardian News and Media, 8 Feb. 2012, www.theguardian.com/commentisfree/2012/feb/08/what-is-astroturfing.