Tracing Fake-News Footprints

Wu, Liang; Liu, Huan

doi:10.1145/3159652.3159677

Cited by 227 publications

(34 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These kinds of studies focus on finding and tracking the spread of posts that include or link to misinformation, or model how misinformation examples spread and cluster across online communities without purposively sampling communities. Some make use of network structure information in social media platforms to observe how misinformation examples spread (Shao et al., 2018a, 2018b; Tambuscio et al., 2015; Vosoughi et al., 2018; Wang et al., 2019), as well as how promotion of misinformation examples can become concentrated within certain communities (Surian et al., 2016; Schmidt et al., 2017; Wu and Liu, 2018). Studies that construct models of population-level outcomes using measures of information exposure or engagement are extremely rare – examples include models of cardiovascular mortality and vaccine coverage (Dunn et al., 2017; Eichstaedt et al., 2015).…”

Section: The Problem: Misinformation Studies Are Disconnected From Actionsmentioning

confidence: 99%

Knowing when to act: A call for an open misinformation library to guide actionable surveillance

et al. 2021

View full text Add to dashboard Cite

The design and reporting of data-driven studies seeking to measure misinformation are patchy and inconsistent, and these studies rarely measure associations with, or effects on, behaviour. The consequence is that data-driven misinformation studies are not yet useful as an empirical basis for guiding when to act on emerging misinformation threats, or for deciding when it is more appropriate to do nothing to avoid inadvertently amplifying misinformation. In a narrative review focused on examples of health-related misinformation, we take a critical perspective of data-driven misinformation studies. To address this problem, we propose a curated and open library of misinformation examples and describe its structure and how it might be used to support actionable surveillance. We draw on experiences with other curated repositories to speculate on the likely challenges related to achieving critical mass and maintaining data consistency. We conclude that an open library of misinformation could help improve the consistency of data-driven misinformation study design and reporting, as well as provide an empirical basis from which to make decisions about how to act on new and emerging misinformation threats.

show abstract

Section: The Problem: Misinformation Studies Are Disconnected From Actionsmentioning

confidence: 99%

Knowing when to act: A call for an open misinformation library to guide actionable surveillance

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Liang Wu and Huan Liu [7] States that the classification of messages was wont to find the messages spreading in social media which are not appropriate, trustworthy, etc. And proposed Trace Miner to classify messages in social media, with this an end-to-end LSTM-RNN's Classification model was also included.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review: Machine Learning Approach and Deep Learning Approach for Fake News Detection

2021

IJETER

View full text Add to dashboard Cite

creasing number of social media platforms, emerging new technologies, and population growth which results in the rate of using social media has increased rapidly. With an increasing number of users on online platforms comes to a variety of problems like fake news. The extensive growth of fake news on social media can have a serious impact on the real world and became a cause of concern for net users and governments all over the world. Distinguishing between real news and fake news becoming more challenging. The amount of fake news has become a disguise. In this paper, we have done a survey on detection techniques for fake news using Algorithms and Deep learning techniques. We have compared machine learning algorithms like Naïve-Bayes, Decision tree, SVM, Adaboost, etc. Comparing the accuracy

show abstract

“…Ma et al 16 proposed a kernel‐based method that compared the spreading of rumors to a tree structure to distinguish between different types of higher order rumors. Wu et al 17 utilized a long short‐term memory (LSTM) RNN that could classify and represent the propagation routes of a message. Ma et al 18 proposed a CNN with user‐attention‐based methods.…”

Section: Related Workmentioning

confidence: 99%

Social rumor detection based on multilayer transformer encoding blocks

Lin

Chen

2020

Concurrency and Computation

View full text Add to dashboard Cite

The propagation of rumors on social media has been identified as a critical problem in recent years that causes social panic or economic turmoil (to some extent), thereby giving rise to the need for faster identification. With the advancements in deep learning, researches based on neural networks become popular. Most of the existing methods extensively adopt recurrent neural networks (RNNs), such as gated recurrent unit and long short-term memory. This results in a significant degradation in the concurrency performance of the models, implying an increased consumption of time and resources. This study proposes a model with multilayer transformer encoding blocks for detecting rumors. The self-attention mechanism in the transformer encoding blocks provides better concurrency to the proposed model and improves its performance. The proposed model performs faster executions using less or no recurrences in comparison to other models based on RNNs. Experiments on two real social datasets verified that our model could achieve significantly better results than baseline methods. The accuracy rate increased by 1.1% and 2.2% on the Weibo and PHEME datasets, respectively. In comparison to the methods that use RNNs as a feature extractor, the training duration in the proposed model was reduced by 16% and 70% in Weibo and PHEME, respectively.

show abstract

Tracing Fake-News Footprints

Cited by 227 publications

References 37 publications

Knowing when to act: A call for an open misinformation library to guide actionable surveillance

Knowing when to act: A call for an open misinformation library to guide actionable surveillance

A Review: Machine Learning Approach and Deep Learning Approach for Fake News Detection

Social rumor detection based on multilayer transformer encoding blocks

Contact Info

Product

Resources

About