Marco Minici

@marcominici.bsky.social

Researcher at ICAR-CNR I develop computational tools to identify threats to online users posed by malicious actors and algorithms that behave unpredictably. Personal Website: https://mminici.github.io

Do you believe that groups of users can coordinate on different social platforms to try to influence other people opinion? Well, in the new episode of targz @marcominici.bsky.social, from ICAR-CNR, describes the technique they used in their research to identify such malevolent groups. Link below! 👇

Our new article in @science.org enables social media reranking outside of platforms' walled gardens. We add an LLM-powered reranking of highly polarizing political content into N=1256 participants' feeds. Downranking cools tensions with the opposite party—but upranking inflames them.

screenshot of the title and authors of the Science paper that are linked in the next post

New paper in Science: In a platform-independent field experiment, we show that reranking content expressing antidemocratic attitudes and partisan animosity in social media feeds alters affective polarization. 🧵

Today, social media platforms hold the sole power to study the effects of feed-ranking algorithms. We developed a platform-independent method that reranks participants’ feeds in real time and used this method to conduct a preregistered 10-day field experiment with 1256 participants on X during the 2024 US presidential campaign. Our experiment used a large language model to rerank posts that expressed antidemocratic attitudes and partisan animosity (AAPA). Decreasing or increasing AAPA exposure shifted out-party partisan animosity by more than 2 points on a 100-point feeling thermometer, with no detectable differences across party lines, providing causal evidence that exposure to AAPA content alters affective polarization. This work establishes a method to study feed algorithms without requiring platform cooperation, enabling independent evaluation of ranking interventions in naturalistic settings.

What does coordinated inauthentic behavior look like on TikTok? We introduce a new framework for detecting coordination in video-first platforms, uncovering influence campaigns using synthetic voices, split-screen tactics, and cross-account duplication. 📄https://arxiv.org/abs/2505.10867

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We constantly ask our apps where to visit, eat or drink. AI tells us, and most of the time, we follow it. The loop continues. But do AIs favor certain places? How would we even know if we don’t own the platforms? We modeled this complex phenomenon, and results are fascinating! Spoiler: rich get…

arxiv.org

Can we effectively detect covert Information Operations (IOs) that attempt to manipulate socio-political debates on social media? This is the focus of our work, "IOHunter: Graph Foundation Model to Uncover Online Information Operations", just presented at the #AAAI #AAAI2025