DOMAIN: Explainable Credibility Assessment Tools for Empowering Online Readers Coping with Misinformation

Despite all the fact-checking initiatives on news and social media aimed at countering misinformation, they remain insufficient to promptly address the wide array of misleading information disseminated by both news and social media outlets. Rather than attempting to identify or filter misleading information, this work advocates new tools for assisting online readers in identifying misinformation among the massive online content pushed every day through multiple platforms. We introduce DOMAIN, an article assessment resource bundle comprising a multidimensional indicator to categorize articles into different types (hard news, soft news, opinion, satire, and conspiracy), a set of explanatory metrics to help users understand the results, a tool for verifying the reliability of the article’s source, and a text summary of the assessment. This work also studies how DOMAIN tools impact online readers, specifically focusing on (i) understanding the extent to which computer-generated assessments influence human perceptions of credibility; (ii) evaluating the effectiveness of automatic article categorization in human assessment of credibility; and (iii) identifying the most relevant explanatory metrics for promoting informed and critical consumption of information.

Authors

Institutions

Publication Details

Journal
ACM Transactions on the Web
Published
2024-10-26
DOI
https://doi.org/10.1145/3696472
Citations
2
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
2.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

DOMAIN: Explainable Credibility Assessment Tools for Empowering Online Readers Coping with Misinformation

Paula Carvalho, Danielle Caled, Mário J. Silva, Francisco Sousa
2 citations
ACM Transactions on the Web
Misinformation and Its Impacts
2.26
article

DOMAIN: Explainable Credibility Assessment Tools for Empowering Online Readers Coping with Misinformation

Paula Carvalho, Danielle Caled, Mário J. Silva, Francisco Sousa
article en
2 citations

Abstract

Despite all the fact-checking initiatives on news and social media aimed at countering misinformation, they remain insufficient to promptly address the wide array of misleading information disseminated by both news and social media outlets. Rather than attempting to identify or filter misleading information, this work advocates new tools for assisting online readers in identifying misinformation among the massive online content pushed every day through multiple platforms. We introduce DOMAIN, an article assessment resource bundle comprising a multidimensional indicator to categorize articles into different types (hard news, soft news, opinion, satire, and conspiracy), a set of explanatory metrics to help users understand the results, a tool for verifying the reliability of the article’s source, and a text summary of the assessment. This work also studies how DOMAIN tools impact online readers, specifically focusing on (i) understanding the extent to which computer-generated assessments influence human perceptions of credibility; (ii) evaluating the effectiveness of automatic article categorization in human assessment of credibility; and (iii) identifying the most relevant explanatory metrics for promoting informed and critical consumption of information.

ACM Transactions on the WebVol. 19(1)
Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT), University of Lisbon (PT), Universidade Politécnica de Lisboa (PT)
Peace, Justice and strong institutions
Openalex Percentile: Top 10%
Misinformation and Its Impacts
2.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.