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
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
Preprint is available on arXiv arxiv.org/abs/2412.14663
IOHunter: Graph Foundation Model to Uncover Online Information Operations
Social 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...
arxiv.org
March 3, 2025 at 5:24 PM
Preprint is available on arXiv arxiv.org/abs/2412.14663
This 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!
Bonus Pic: myself beyond excited to stand next to my poster!
March 3, 2025 at 5:23 PM
This 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!
Bonus Pic: myself beyond excited to stand next to my poster!
Our 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.
March 3, 2025 at 5:23 PM
Our 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.
2. 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.
March 3, 2025 at 5:23 PM
2. 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.
Key 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.
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.
March 3, 2025 at 5:23 PM
Key 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.
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.
Read our preprint available on arXiv at: arxiv.org/abs/2412.14663
IOHunter: Graph Foundation Model to Uncover Online Information Operations
Social 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...
arxiv.org
December 23, 2024 at 2:02 PM
Read our preprint available on arXiv at: arxiv.org/abs/2412.14663
Our 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.
December 23, 2024 at 2:02 PM
Our 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.
Our 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.
December 23, 2024 at 2:02 PM
Our 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.
Our 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)
1️⃣ Supervised IO Detection
2️⃣ Scarcely-Labeled Supervised IO Detection
3️⃣ Cross-IO Detection (with minimal or no labeled data from emerging IOs)
December 23, 2024 at 2:02 PM
Our 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)
1️⃣ Supervised IO Detection
2️⃣ Scarcely-Labeled Supervised IO Detection
3️⃣ Cross-IO Detection (with minimal or no labeled data from emerging IOs)
Maintaining 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
Our multi-modal learning framework IOHunter integrates both content and contextual information to identify actors attempting to manipulate online discussions - i.e., IO Drivers
December 23, 2024 at 2:02 PM
Maintaining 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
Our multi-modal learning framework IOHunter integrates both content and contextual information to identify actors attempting to manipulate online discussions - i.e., IO Drivers