This could curb manipulated posts
Researchers have developed an Artificial Intelligence (AI)-based system to detect Twitter bots after identifying differences in their short-term behaviour from humans on the social media platform. Bots are social media accounts which are controlled by artificial software rather than by humans and serve a variety of purposes from news aggregation to automated customer assistance for online retailers.
However, bots have recently been under the spotlight as they are regularly employed as part of large-scale efforts on social media to manipulate public opinion, such as during electoral campaigns.
"Remarkably, bots continuously improve to mimic more and more of the behaviour humans typically exhibit on social media," said study co-author Emilio Ferrara, Assistant Professor at the University of Southern California Information Sciences Institute in the US.
"Every time we identify a characteristic we think is prerogative of human behaviour, such as sentiment of topics of interest, we soon discover that newly-developed open-source bots can now capture those aspects," Ferrara said.
The new study published in the journal Frontiers in Physics revealed the presence of short-term behavioural trends in humans that are absent in social media bots. In this work, the researchers studied how the behaviour of humans and bots changed over the course of an activity session using a large Twitter dataset associated with recent political events.
The researchers found that humans showed an increase in the amount of social interaction over the course of a session, illustrated by an increase in the fraction of retweets, replies and number of mentions contained in a tweet.
But as the session progressed, the average length of tweets by humans decreased. These trends are thought to be due to the fact that as the sessions progress, human users grow tired and are less likely to undertake complex activities, such as composing original content.
Another possible explanation may be given by the fact that as time goes by, users are exposed to more posts, therefore increasing their probability to react and interact with content. In both cases, bots were shown to not be affected by such considerations and no behavioural change was observed from them, said the study.
The researchers used these behavioural results to inform a classification system for bot detection and found that the full model including the features describing session dynamics significantly outperformed the baseline model in its accuracy of bot detection, which did not describe those features.
These results highlight that user behaviour on social media evolves in a measurably different manner between bots and humans over an activity session and also suggests that these differences can be used to implement a bot detection system or to improve existing ones.
