REVIEW 7 cited by
False Information on Web and Social Media: A Survey
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection. A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling. Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information. In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research.
Forward citations
Cited by 7 Pith papers
-
A Turing Test for ''Localness'': Conceptualizing, Defining, and Recognizing Localness in People and Machines
An empirical chat-based study with 230 participants shows localness is a multidimensional, socially constructed identity and that people recognize locals better than they identify nonlocals.
-
Identity Deepfake Threats to Biometric Authentication Systems: Public and Expert Perspectives
A UK-based mixed-methods study finds a wide gap between public trust in biometrics and expert concern about deepfake spoofing, and proposes a tri-layer mitigation framework.
-
SliceNDice: Mining Suspicious Multi-attribute Entity Groups with Multi-view Graphs
A likelihood-based suspiciousness metric and greedy mining algorithm detect groups of entities that share unusually many rare attribute values across multiple views, validated on Snapchat advertiser fraud.
-
Misinformation spreading on correlated multiplex networks
A heterogeneous edge-based compartmental theory predicts the outbreak threshold and final size of misinformation spreading on correlated two-layer networks, with good agreement to simulations.
-
Exploiting Multi-domain Visual Information for Fake News Detection
A multi-domain visual network that combines frequency-domain compression artifacts with pixel-domain semantic features improves fake news image detection by over 9% accuracy.
-
A Survey on False Information Detection: From A Perspective of Propagation on Social Networks
A survey that organizes propagation-based false information detection into homogeneous and heterogeneous categories, summarizing datasets, methods, and future directions.
-
The Mass, Fake News, and Cognition Security
The paper defines Cognition Security (CogSec) as a multidisciplinary field studying cognitive impacts of fake news and outlines research challenges, techniques, and future directions.
Discussion (0). Continue with ORCID to comment.