Reposted by Marco MiniciLuca Florio @florio.dev · 17/03/2026florio.dev/screaming-in...florio.devScreaming in the Void: Introducing my PodcastsIntroducing a series of posts about my podcasts 011
Reposted by Marco MiniciLuca Florio @florio.dev · 09/03/2026Do 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! 👇 121
Reposted by Marco MiniciGiovanni Mauro @gmauro10.bsky.social · 09/01/2026📢 New paper! We study urban location recommenders and their feedback with human mobility. Simulating this loop reveals a paradox: people explore more individually, yet city visits and encounters concentrate. Cities coevolve with AI, and inequality can grow. 📄 link.springer.com/article/10.1...link.springer.comClient Challenge 142
Reposted by Marco MiniciMichael Bernstein @mbernst.bsky.social · 01/12/2025Our 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. 14813
Reposted by Marco MiniciMartin Saveski @msaveski.bsky.social · 01/12/2025New 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. 🧵 515468
Reposted by Marco MiniciLuca Luceri @luceriluc.bsky.social · 19/05/2025What 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 2209
Reposted by Marco MiniciGiovanni Mauro @gmauro10.bsky.social · 11/04/2025We 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 131
Reposted by Marco MiniciFil Menczer @fil.bsky.social · 04/03/2025IU's Observatory on Social Media defends citizens from online manipulation – the opposite of censorship osome.iu.edu/research/blo...osome.iu.eduIU's Observatory on Social Media defends citizens from online manipulation – the opposite of censorshipWhen thousands of fake accounts controlled by an unknown actor flood social media with some story, and platform algorithms amplify these messages, real... 010650
Marco Minici @marcominici.bsky.social · 03/03/2025Preprint is available on arXiv arxiv.org/abs/2412.14663arxiv.orgIOHunter: Graph Foundation Model to Uncover Online Information OperationsSocial media platforms have become vital spaces for public discourse, serving as modern agorás where a wide range of voices influence societal narratives. However, their open nature also makes them vu... 010
Marco Minici @marcominici.bsky.social · 03/03/2025This work would not have been possible without the other amazing coauthors @luceriluc.bsky.social @frafabbri.bsky.social @emilioferrara.bsky.social Bonus Pic: myself beyond excited to stand next to my poster! 131
Marco Minici @marcominici.bsky.social · 03/03/2025Our work provides a scalable approach for online moderation teams, public institutions, and independent organizations to audit the health of online environments—especially crucial during political events such as election cycles. 110
Marco Minici @marcominici.bsky.social · 03/03/20252. We explore how our multimodal framework exhibits foundation model behavior in detecting online information operations. Our results show that pretraining IOHunter on past IO datasets enables it to generalize to new, emerging IOs with only a few labeled examples for fine-tuning. 100
Marco Minici @marcominici.bsky.social · 03/03/2025Key takeaways: 1. We propose a multimodal framework that effectively integrates textual and graph information using a cross-attention mechanism, which is then processed by a GNN. 100
Marco Minici @marcominici.bsky.social · 03/03/2025Can 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 272
Reposted by Marco MiniciFil Menczer @fil.bsky.social · 26/12/2024The three horsemen of social media: brain rot, anxiety and foreign interference voxeurop.eu/en/social-me...voxeurop.euThe three horsemen of social media: brain rot, anxiety and foreign interferenceWith so many media institutions bidding farewell to X, it's a good time to reflect on the relationship status of social media and European society at large. 4379
Marco Minici @marcominici.bsky.social · 23/12/2024Read our preprint available on arXiv at: arxiv.org/abs/2412.14663arxiv.orgIOHunter: Graph Foundation Model to Uncover Online Information OperationsSocial media platforms have become vital spaces for public discourse, serving as modern agorás where a wide range of voices influence societal narratives. However, their open nature also makes them vu... 010
Marco Minici @marcominici.bsky.social · 23/12/2024Our effort highlights the critical role of multi-modality in modeling malicious user behavior, the value of attention to weight the modalities, and how we can advance toward a GFM for the IO Detection task by pre-training our architecture on a dataset of previous IOs. 110
Marco Minici @marcominici.bsky.social · 23/12/2024Our work demonstrates how a multi-modal framework based on GNN+LM and massive pre-training produces a model that effectively generalizes to IOs not present in the original training dataset — the most realistic scenario for IO detection. 100
Marco Minici @marcominici.bsky.social · 23/12/2024Our model delivers substantial improvements over current IO detection methods across three learning tasks: 1️⃣ Supervised IO Detection 2️⃣ Scarcely-Labeled Supervised IO Detection 3️⃣ Cross-IO Detection (with minimal or no labeled data from emerging IOs) 100
Marco Minici @marcominici.bsky.social · 23/12/2024Maintaining the integrity of online discourse is essential for safeguarding fair democratic processes. Our multi-modal learning framework IOHunter integrates both content and contextual information to identify actors attempting to manipulate online discussions - i.e., IO Drivers 100
Marco Minici @marcominici.bsky.social · 23/12/2024"IOHunter: Graph Foundation Model to Uncover Online Information Operations" goes to AAAI'25! This is the result of an incredible collaboration with @luceriluc.bsky.social @frafabbri.bsky.social and @emilioferrara.bsky.social Read the entire thread for a summary and the link to the preprint. 151
Reposted by Marco MiniciRoss Dahlke @rossdahlke.bsky.social · 08/12/2024New evidence of cross-platform foreign interference on social media during the 2024 U.S. Election that drove the spread of highly-partisan, low-credibility, and conspiratorial content, from Cinus, Minici, @luceriluc.bsky.social @emilioferrara.bsky.social arxiv.org/pdf/2410.22716 17530