Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
TLDR
Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.Abstract:
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.read more
Citations
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Book ChapterDOI
Detection of Fake News Problems and Their Evaluation Through Artificial Intelligence
TL;DR: In this paper, the use of Python for detecting fake news in community datasets is proposed, and Python language was applied for experiments. But, the detection of fake news has huge suggestion, while fake news is very, it gets basic to application computational system to find out; this is the reason the use for python like “Count Vectorizer”, “Tfidf Vectorizer, Model for the recognition of fake information in community dataset is planned.
Journal ArticleDOI
Construct validation of the COVID-19 Cavalier Scale: Analysis of indirect effects with optimism on likelihood to travel
TL;DR: The 9-item COVID-19 cavalier scale (CCS) provided a tool for researchers to study these individuals as discussed by the authors , which demonstrated discriminant validity with practical public health traveling implications, identifying and understanding caviler individuals will help control the spread of diseases and reopen society for tourism.
Proceedings ArticleDOI
Prediction and Analysis of Rumour's Impact on Social Media
TL;DR: A rumour influence prediction model RISM (Rumour Impact on Social Media) based on a popular rumour intensity formula to predict the impact of a newborn rumour is devised, which demonstrates the effectiveness of the proposed RISM model.
Proceedings ArticleDOI
A Survey on Video-Based Fake News Detection Techniques
Ronak Agrawal,Dilip Kumar Sharma +1 more
TL;DR: In this article, the authors discussed the existing issues and challenges which make the forgery detection task cumbersome and discussed the use of deep neural network, convolutional neural networks, biological signal and spatio-temporal neural network for fake video identification.
Can Fake News Detection Models Maintain the Performance through Time? A Longitudinal Evaluation of Twitter Publications
TL;DR: In this article, the authors focus on a longitudinal evaluation using social network publications spanning an 18-month period and evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time.
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