The discourse around conspiracy theories is currently thriving amidst the rampant misinformation in online environments. Research in this field has been focused on detecting conspiracy theories on social media, often relying on limited datasets. In this study, we present a novel methodology for constructing a Twitter dataset that encompasses accounts engaged in conspiracy-related activities throughout the year 2022. Our approach centers on data collection that is independent of specific conspiracy theories and information operations. Additionally, our dataset includes a control group comprising randomly selected users who can be fairly compared to the individuals involved in conspiracy activities. This comprehensive collection effort yielded a total of 15K accounts and 37M tweets extracted from their timelines. We conduct a comparative analysis of the two groups across three dimensions: topics, profiles, and behavioral characteristics. The results indicate that conspiracy and control users exhibit similarity in terms of their profile metadata characteristics. However, they diverge significantly in terms of behavior and activity, particularly regarding the discussed topics, the terminology used, and their stance on trending subjects. In addition, we find no significant disparity in the presence of bot users between the two groups. Finally, we develop a classifier to identify conspiracy users using features borrowed from bot, troll and linguistic literature. The results demonstrate a high accuracy level (with an F1 score of 0.94), enabling us to uncover the most discriminating features associated with conspiracy-related accounts.

According to Facebook, over half of Instagram's roughly 1 billion users visit Instagram Explore to discover videos, photos, livestreams, and Stories each month. Predictably, building the underlying recommendation engine -- which curates the billions of pieces of content uploaded to Instagram -- posed an engineering challenge, not least because it works in real time. In a blog post published this morning, Facebook for the first time peeled back the curtains on Explore's inner workings. Its three-part ranking funnel, which the company says was architected with a custom query language and modeling techniques, extracts 65 billion features and makes 90 million model predictions every second. Before the team behind Explore embarked on building a content recommendation system, they developed tools to conduct large-scale experiments and obtain strong signals on the breadth of users' interests.

Over half of the Instagram community visits Instagram Explore every month to discover new photos, videos, and Stories relevant to their interests. Recommending the most relevant content out of billions of options in real time at scale introduces multiple machine learning (ML) challenges that require novel engineering solutions. We tackled these challenges by creating a series of custom query languages, lightweight modeling techniques, and tools enabling high-velocity experimentation. These systems support the scale of Explore while boosting developer efficiency. Collectively, these solutions represent an AI system based on a highly efficient 3-part ranking funnel that extracts 65 billion features and makes 90 million model predictions every second.