How foreign influence campaigns manipulate your social media feeds

We estimate that at least 10,000 accounts like these were active daily on the platform, and that was before X CEO Elon Musk dramatically cut the platform’s trust and safety teams. We also identified a network of 1,140 bots that used ChatGPT to generate humanlike content to promote fake news websites and cryptocurrency scams.

In addition to posting machine-generated content, harmful comments and stolen images, these bots engaged with each other and with humans through replies and retweets. Current state-of-the-art large language model content detectors are unable to distinguish between AI-enabled social bots and human accounts in the wild.

Model misbehavior

The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Therefore it is unclear, for example, whether online influence campaigns can sway election outcomes. Yet, it is vital to understand society’s vulnerability to different manipulation tactics.

In a recent paper, we introduced a social media model called SimSoM that simulates how information spreads through the social network. The model has the key ingredients of platforms such as Instagram, X, Threads, Bluesky and Mastodon: an empirical follower network, a feed algorithm, sharing and resharing mechanisms, and metrics for content quality, appeal and engagement.

SimSoM allows researchers to explore scenarios in which the network is manipulated by malicious agents who control inauthentic accounts. These bad actors aim to spread low-quality information, such as disinformation, conspiracy theories, malware or other harmful messages. We can estimate the effects of adversarial manipulation tactics by measuring the quality of information that targeted users are exposed to in the network.

We simulated scenarios to evaluate the effect of three manipulation tactics. First, infiltration: having fake accounts create believable interactions with human users in a target community, getting those users to follow them. Second, deception: having the fake accounts post engaging content, likely to be reshared by the target users. Bots can do this by, for example, leveraging emotional responses and political alignment. Third, flooding: posting high volumes of content.