Papers by Marco Antonio Stranisci
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (2026.findings-acl)
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Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
| Challenge: | polarization is a pervasive threat to democratic institutions, civil discourse, and social cohesion worldwide . most existing datasets focus on English or high-resource languages, reflecting a widespread trend across NLP tasks . |
| Approach: | They propose a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. |
| Outcome: | The proposed dataset analyzes polarization detection, type, and manifestation using a variety of annotation platforms adapted to each cultural context. |
WikiBio: a Semantic Resource for the Intersectional Analysis of Biographical Events (2023.acl-long)
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Marco Antonio Stranisci, Rossana Damiano, Enrico Mensa, Viviana Patti, Daniele Radicioni, Tommaso Caselli
| Challenge: | Existing corpora and models for biographical event detection are lacking . Detecting biographical events from unstructured data is a useful task to explore and compare bias in representations of individuals. |
| Approach: | They present a corpus annotated for biographical event detection using 20 Wikipedia biographies and 5 existing corpora to train a model. |
| Outcome: | The proposed model detects all mentions of the target-entity in a biography with an F-score of 0.808 and the entity-related events with an 0.859 score. |
That is Unacceptable: the Moral Foundations of Canceling (2025.acl-long)
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| Challenge: | Annotators' canceling attitudes are influenced by the type of controversial events and involved celebrities. |
| Approach: | They propose to annotate canceling incidents from YouTube and an annotated corpus of videos that are based on their morality to determine their canceling attitudes. |
| Outcome: | The dataset analyzes canceling attitudes of annotators from six videos and comments gathered from YouTube. |
APPReddit: a Corpus of Reddit Posts Annotated for Appraisal (2022.lrec-1)
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Marco Antonio Stranisci, Simona Frenda, Eleonora Ceccaldi, Valerio Basile, Rossana Damiano, Viviana Patti
| Challenge: | Existing resources for emotion recognition are lacking for appraisal models. |
| Approach: | They propose to use APPReddit to annotate non-experimental data according to Appraisal theories . they compare it with enISEAR, a corpus of events created in an experimental setting and annotated according to this theory. |
| Outcome: | The proposed model predicts four appraisal dimensions without significant loss . the proposed model is compared with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. |