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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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.

Original language	English
Title of host publication	Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023)
Publisher	CEUR-WS
Pages	494–498
Number of pages	5
Volume	3596
ISBN (Print)	9791255000846
Publication status	Published - 30 Nov 2023
Event	9th Italian Conference on Computational Linguistics 2023 - Venice, Italy Duration: 30 Nov 2023 → 2 Dec 2023 https://clic2023.ilc.cnr.it/

Publication series

Name	CEUR Workshop Proceedings
Publisher	CEUR-WS
ISSN (Print)	1613-0073

Conference

Conference	9th Italian Conference on Computational Linguistics 2023
Abbreviated title	CLiC-it 2023
Country/Territory	Italy
City	Venice
Period	30/11/23 → 2/12/23
Internet address	https://clic2023.ilc.cnr.it/

Keywords

Debunker-Assistant
Linguistic Features
Misinformation
Web Domain Network

ASJC Scopus subject areas

General Computer Science

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Capozzi Lupi, A. T. E., Cignarella, A. T., Frenda, S., Lai, M., Stranisci, M. A., & Urbinati, A. (2023). Debunker Assistant: A Support for Detecting Online Misinformation. In Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023) (Vol. 3596, pp. 494–498). (CEUR Workshop Proceedings). CEUR-WS. https://aclanthology.org/2023.clicit-1.62/

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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.",

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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 2023, CLiC-it 2023 ; Conference date: 30-11-2023 Through 02-12-2023",

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Capozzi Lupi, ATE, Cignarella, AT, Frenda, S, Lai, M, Stranisci, MA & Urbinati, A 2023, Debunker Assistant: A Support for Detecting Online Misinformation. in Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023). vol. 3596, CEUR Workshop Proceedings, CEUR-WS, pp. 494–498, 9th Italian Conference on Computational Linguistics 2023, Venice, Italy, 30/11/23. <https://aclanthology.org/2023.clicit-1.62/>

Debunker Assistant: A Support for Detecting Online Misinformation. / Capozzi Lupi, Arthur Thomas Edward; Cignarella, Alessandra Teresa; Frenda, Simona et al.
Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023). Vol. 3596 CEUR-WS, 2023. p. 494–498 (CEUR Workshop Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Debunker Assistant: A Support for Detecting Online Misinformation

AU - Capozzi Lupi, Arthur Thomas Edward

AU - Cignarella, Alessandra Teresa

AU - Frenda, Simona

AU - Lai, Mirko

AU - Stranisci, Marco Antonio

AU - Urbinati, Alessandra

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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.

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KW - Misinformation

KW - Web Domain Network

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BT - Proceedings of the Ninth Italian Conference on Computational Linguistics (CLiC-it 2023)

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ER -

Debunker Assistant: A Support for Detecting Online Misinformation

Abstract

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Keywords

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