The militant Islamic State group, or ISIS, lost its physical territory in 2019, but it remains an active force on social media, according to researchers from the Penn State College of Information Sciences and Technology, who set out to better understand the group's online strategies.

In their work, which appeared in the journal Social Media Analysis and Mining, the researchers analyzed a large dataset of activity on X—formerly known as Twitter—to develop a predictive model to detect users and content related to Islamic State extremists.

They identified potential propaganda messages and their characteristics and developed an image classifier to find the most frequent categories of images attached to tweets about ISIS. They further collected a dataset of tweets from potential ISIS supporters to investigate their recent activities.

"The Islamic State group and its affiliates, sympathizers and followers continue to manipulate online communities to spread extremist propaganda," said Younes Karimi, a graduate student pursuing a doctorate in informatics and the first author of the paper.

"By studying their behavioral patterns and strategies and monitoring their online presence, we can help social media companies identify and eventually restrict such accounts in a timelier manner and abate their impact on online communities."

According to Karimi, the Islamic State group is increasingly relying on social media to spread propaganda, undermine its rivals and recruit sympathizers, despite countermeasures by websites like X to restrict its online activities. ISIS watch—an online channel that publishes daily updates on terrorist content banned on the cross-platform instant messaging service Telegram —reported the removal of nearly 5,000 terrorist bots and channels in the first 11 days of 2024.

The researchers' dataset included millions of tweets, spanning from 2009 to 2021, that were linked to the Islamic State group and its propaganda.

"The longitudinal perspective of the dataset is important because it includes data from before and after 2015, when a major crackdown by Twitter removed user accounts and content involving the Islamic State group," Karimi said. "In response, the extremists had to change their online strategy and move to other platforms, and little is known about their online whereabouts since that crackdown."

To identify potential ISIS supporters, the researchers began by building a user classifier using the old dataset. ISIS accounts identified before 2015 served as the labeled data for the study's ISIS users. The researchers used machine learning and natural language processing techniques to differentiate the types of users sharing the extremist group's content.

"The users in our dataset ranged from known members of the Islamic State group to retweeters and quoters to mentioners of ISIS," Karimi said.

"We believe that users who retweet or quote Islamic State group content are more likely to be affiliates or sympathizers, while those who just mention the content are less likely to be supporters. However, tweets posted by mentioners are still very likely related to ISIS and contain topics similar to ISIS tweets, which make mentioners suitable to be considered as our non-ISIS users and non-trivial counterparts to ISIS users."

More information: Younes Karimi et al, A longitudinal dataset and analysis of Twitter ISIS users and propaganda, Social Network Analysis and Mining (2024). DOI: 10.1007/s13278-023-01177-7