Search Results for author: Tiziano Fagni

Found 7 papers, 2 papers with code

The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media

no code implementations16 Dec 2023 Amaury Trujillo, Tiziano Fagni, Stefano Cresci

They offer guidance for future regulations that cater to the reporting needs of online platforms in general, but also highlight opportunities to improve and refine the database itself.

Philosophy

Personalized Interventions for Online Moderation

no code implementations19 May 2022 Stefano Cresci, Amaury Trujillo, Tiziano Fagni

Current online moderation follows a one-size-fits-all approach, where each intervention is applied in the same way to all users.

Sociology

Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets

no code implementations22 Apr 2022 Margherita Gambini, Tiziano Fagni, Caterina Senette, Maurizio Tesconi

Existing approaches, mainly targeting Twitter users, rely on content-based analysis or are based on a mixture of content, network and communication analysis.

Stance Detection Zero-Shot Learning

Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning

no code implementations23 Feb 2022 Tiziano Fagni, Stefano Cresci

Here, we propose a novel unsupervised technique for learning fine-grained political leaning from the textual content of social media posts.

Representation Learning

JaTeCS an open-source JAva TExt Categorization System

no code implementations21 Jun 2017 Andrea Esuli, Tiziano Fagni, Alejandro Moreo Fernandez

JaTeCS is an open source Java library that supports research on automatic text categorization and other related problems, such as ordinal regression and quantification, which are of special interest in opinion mining applications.

feature selection Opinion Mining +1

Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions

2 code implementations23 Jun 2016 Fabio Carrara, Andrea Esuli, Tiziano Fagni, Fabrizio Falchi, Alejandro Moreo Fernández

We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation.

Cross-Modal Retrieval Descriptive +2

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