[HTML][HTML] Emotion detection for misinformation: A review

Z Liu, T Zhang, K Yang, P Thompson, Z Yu… - Information …, 2024 - Elsevier
With the advent of social media, an increasing number of netizens are sharing and reading
posts and news online. However, the huge volumes of misinformation (eg, fake news and …

[PDF][PDF] Human-in-the-loop Artificial Intelligence for Fighting Online Misinformation: Challenges and Opportunities.

G Demartini, S Mizzaro, D Spina - IEEE Data Eng. Bull., 2020 - damianospina.com
The rise of online misinformation is posing a threat to the functioning of democratic
processes. The ability to algorithmically spread false information through online social …

Crowdsourced fact-checking at Twitter: How does the crowd compare with experts?

M Saeed, N Traub, M Nicolas, G Demartini… - Proceedings of the 31st …, 2022 - dl.acm.org
Fact-checking is one of the effective solutions in fighting online misinformation. However,
traditional fact-checking is a process requiring scarce expert human resources, and thus …

The many dimensions of truthfulness: Crowdsourcing misinformation assessments on a multidimensional scale

M Soprano, K Roitero, D La Barbera, D Ceolin… - Information Processing …, 2021 - Elsevier
Recent work has demonstrated the viability of using crowdsourcing as a tool for evaluating
the truthfulness of public statements. Under certain conditions such as:(1) having a balanced …

Overview of the CLEF-2019 CheckThat! Lab: automatic identification and verification of claims

T Elsayed, P Nakov, A Barrón-Cedeno… - Experimental IR Meets …, 2019 - Springer
We present an overview of the second edition of the CheckThat! Lab at CLEF 2019. The lab
featured two tasks in two different languages: English and Arabic. Task 1 (English) …

A benchmark dataset of check-worthy factual claims

F Arslan, N Hassan, C Li, M Tremayne - Proceedings of the International …, 2020 - aaai.org
In this paper we present the ClaimBuster dataset of 23,533 statements extracted from all US
general election presidential debates and annotated by human coders. The ClaimBuster …

[PDF][PDF] Overview of the CLEF-2019 CheckThat! lab: Automatic identification and verification of claims. Task 1: Check-worthiness.

P Atanasova, P Nakov, G Karadzhov… - CLEF (Working …, 2019 - groups.csail.mit.edu
We present an overview of the 2nd edition of the CheckThat! Lab, part of CLEF 2019, with
focus on Task 1: Check-Worthiness in political debates. The task asks to predict which …

Generating label cohesive and well-formed adversarial claims

P Atanasova - Accountable and Explainable Methods for Complex …, 2024 - Springer
Adversarial attacks reveal important vulnerabilities and flaws of trained models. One potent
type of attack are universal adversarial triggers, which are individual n-grams that, when …

How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking

R Linder, S Mohseni, F Yang, SK Pentyala… - Applied AI …, 2021 - Wiley Online Library
Explainable artificial intelligence (XAI) systems aim to provide users with information to help
them better understand computational models and reason about why outputs were …

Overview of the shared task on fake news detection in Urdu at Fire 2021

M Amjad, S Butt, HI Amjad, A Zhila, G Sidorov… - arXiv preprint arXiv …, 2022 - arxiv.org
Automatic detection of fake news is a highly important task in the contemporary world. This
study reports the 2nd shared task called UrduFake@ FIRE2021 on identifying fake news …