Transformer-based models for combating rumours on microblogging platforms: a review
The remarkable success of Transformer-based embeddings in natural language tasks has
sparked interest among researchers in applying them to classify rumours on social media …
sparked interest among researchers in applying them to classify rumours on social media …
A systematic literature review and meta-analysis of studies on online fake news detection
The ubiquitous access and exponential growth of information available on social media
networks have facilitated the spread of fake news, complicating the task of distinguishing …
networks have facilitated the spread of fake news, complicating the task of distinguishing …
Fake news detection in Dravidian languages using transfer learning with adaptive finetuning
Fake news has become a major challenge for online platforms and society as a whole, with
potentially harmful consequences for individuals and organizations. While there has been a …
potentially harmful consequences for individuals and organizations. While there has been a …
Countering malicious content moderation evasion in online social networks: Simulation and detection of word camouflage
Content moderation is the process of screening and monitoring user-generated content
online. It plays a crucial role in stopping content resulting from unacceptable behaviors such …
online. It plays a crucial role in stopping content resulting from unacceptable behaviors such …
Identifying sarcasm using heterogeneous word embeddings: a hybrid and ensemble perspective
Past research suggests pre-trained word embedding strategies to assess and determine
feelings conveyed in various text documents. However, using a single word embedding …
feelings conveyed in various text documents. However, using a single word embedding …
Is cross-linguistic advert flaw detection in Wikipedia feasible? A multilingual-BERT-based transfer learning approach
M Li, H Zhou, J Hou, P Wang, E Gao - Knowledge-Based Systems, 2022 - Elsevier
Wikipedia is one of the most prominent online platforms from which people acquire
knowledge; thus, its article quality should be of great concern. Currently, many scholars …
knowledge; thus, its article quality should be of great concern. Currently, many scholars …
MMAdapt: A Knowledge-guided Multi-source Multi-class Domain Adaptive Framework for Early Health Misinformation Detection
This paper studies a critical problem of emergent health misinformation detection, aiming to
mitigate the spread of misinformation in emergent health domains to support well-informed …
mitigate the spread of misinformation in emergent health domains to support well-informed …
Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey
In today's global digital landscape, misinformation transcends linguistic boundaries, posing
a significant challenge for moderation systems. While significant advances have been made …
a significant challenge for moderation systems. While significant advances have been made …
Fake news detection: deep semantic representation with enhanced feature engineering
Due to the widespread use of social media, people are exposed to fake news and
misinformation. Spreading fake news has adverse effects on both the general public and …
misinformation. Spreading fake news has adverse effects on both the general public and …
Reevaluating synthesizing sentiment analysis on COVID-19 fake news detection using spark dataframe
SF Pane, R Prastya, AG Putrada… - 2022 International …, 2022 - ieeexplore.ieee.org
Some research uses the random forest model and sentiment analysis to detect COVID-19
fake news. However, there is still a research opportunity to apply the method to Indonesian …
fake news. However, there is still a research opportunity to apply the method to Indonesian …