Debunker Assistant: a support for detecting online misinformation

Arthur Thomas Edward Capozzi Lupi; Alessandra Teresa Cignarella; Simona Frenda; Mirko Lai; Marco Antonio Stranisci; Alessandra Urbinati

Debunker Assistant: a support for detecting online misinformation

Arthur Thomas Edward Capozzi Lupi
, Alessandra Teresa Cignarella
, Simona Frenda
, Mirko Lai
, Marco Antonio Stranisci
, Alessandra Urbinati

Risultato della ricerca: Contributo su rivista › Articolo da conferenza › peer review

Abstract

This paper describes the framework developed for the Debunker-Assistant, an application that allows users and newspapers to assess the trustworthiness of a news item starting from its headline, body of text and URL. The Debunker-Assistant adapts ideas from Natural Language Processing and Network Science to counter the spread of online misinformation. Its centerpiece is a set of four News Misinformation Indicators based on linguistically engineered features, models, network analysis metrics (Echo Effect, Alarm Bell, Sensationalism, and Reliability). In this short contribution, we describe the back-end structure on which the indicators are implemented.

Lingua originale	Inglese
Rivista	CEUR Workshop Proceedings
Volume	3596
Stato di pubblicazione	Pubblicato - 2023
Pubblicato esternamente	Sì
Evento	9th Italian Conference on Computational Linguistics, CLiC-it 2023 - Venice, Italy Durata: 30 nov 2023 → 2 dic 2023

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title = "Debunker Assistant: a support for detecting online misinformation",

abstract = "This paper describes the framework developed for the Debunker-Assistant, an application that allows users and newspapers to assess the trustworthiness of a news item starting from its headline, body of text and URL. The Debunker-Assistant adapts ideas from Natural Language Processing and Network Science to counter the spread of online misinformation. Its centerpiece is a set of four News Misinformation Indicators based on linguistically engineered features, models, network analysis metrics (Echo Effect, Alarm Bell, Sensationalism, and Reliability). In this short contribution, we describe the back-end structure on which the indicators are implemented.",

keywords = "Debunker-Assistant, Linguistic Features, Misinformation, Web Domain Network",

author = "\{Capozzi Lupi\}, \{Arthur Thomas Edward\} and Cignarella, \{Alessandra Teresa\} and Simona Frenda and Mirko Lai and Stranisci, \{Marco Antonio\} and Alessandra Urbinati",

note = "Publisher Copyright: {\textcopyright} 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).; 9th Italian Conference on Computational Linguistics, CLiC-it 2023 ; Conference date: 30-11-2023 Through 02-12-2023",

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journal = "CEUR Workshop Proceedings",

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TY - JOUR

T1 - Debunker Assistant

T2 - 9th Italian Conference on Computational Linguistics, CLiC-it 2023

AU - Capozzi Lupi, Arthur Thomas Edward

AU - Cignarella, Alessandra Teresa

AU - Frenda, Simona

AU - Lai, Mirko

AU - Stranisci, Marco Antonio

AU - Urbinati, Alessandra

PY - 2023

Y1 - 2023

N2 - This paper describes the framework developed for the Debunker-Assistant, an application that allows users and newspapers to assess the trustworthiness of a news item starting from its headline, body of text and URL. The Debunker-Assistant adapts ideas from Natural Language Processing and Network Science to counter the spread of online misinformation. Its centerpiece is a set of four News Misinformation Indicators based on linguistically engineered features, models, network analysis metrics (Echo Effect, Alarm Bell, Sensationalism, and Reliability). In this short contribution, we describe the back-end structure on which the indicators are implemented.

AB - This paper describes the framework developed for the Debunker-Assistant, an application that allows users and newspapers to assess the trustworthiness of a news item starting from its headline, body of text and URL. The Debunker-Assistant adapts ideas from Natural Language Processing and Network Science to counter the spread of online misinformation. Its centerpiece is a set of four News Misinformation Indicators based on linguistically engineered features, models, network analysis metrics (Echo Effect, Alarm Bell, Sensationalism, and Reliability). In this short contribution, we describe the back-end structure on which the indicators are implemented.

KW - Debunker-Assistant

KW - Linguistic Features

KW - Misinformation

KW - Web Domain Network

UR - https://www.scopus.com/pages/publications/85181167200

M3 - Conference article

AN - SCOPUS:85181167200

SN - 1613-0073

VL - 3596

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 30 November 2023 through 2 December 2023

ER -

Debunker Assistant: a support for detecting online misinformation

Abstract

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