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Titlebook: Disinformation, Misinformation, and Fake News in Social Media; Emerging Research Ch Kai Shu,Suhang Wang,Huan Liu Book 2020 Springer Nature

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樓主: Lipase
41#
發(fā)表于 2025-3-28 15:01:35 | 只看該作者
Systems Collaboration and Integration does the audience engage with mis- and dis-information?, and (3) What feedback do users provide? These patterns and insights can be leveraged to develop better strategies to improve media literacy and informed engagement with crowd-sourced information like social news.
42#
發(fā)表于 2025-3-28 22:07:30 | 只看該作者
43#
發(fā)表于 2025-3-28 23:36:19 | 只看該作者
44#
發(fā)表于 2025-3-29 03:47:12 | 只看該作者
Barrett S. Caldwell,P. U. Grouperopic group analysis and Twitter-Youtube networks, we also show that all of the campaigns originated in similar communities. This informs future work focused on the cross-platform and cross-network nature of these conversations with an eye toward how that may improve our ability to classify the intent and effect of various campaigns.
45#
發(fā)表于 2025-3-29 09:28:40 | 只看該作者
46#
發(fā)表于 2025-3-29 15:04:16 | 只看該作者
47#
發(fā)表于 2025-3-29 19:17:54 | 只看該作者
https://doi.org/10.1007/978-3-030-33312-6 training the model, we construct a million scale dataset of news articles, which we also release for broader research use. Based on the results of a focus group interview, we discuss the importance of developing an interpretable AI agent for the design of a better interface for mitigating the effects of online misinformation.
48#
發(fā)表于 2025-3-29 22:36:52 | 只看該作者
Pretending Positive, Pushing False: Comparing Captain Marvel Misinformation Campaignsopic group analysis and Twitter-Youtube networks, we also show that all of the campaigns originated in similar communities. This informs future work focused on the cross-platform and cross-network nature of these conversations with an eye toward how that may improve our ability to classify the intent and effect of various campaigns.
49#
發(fā)表于 2025-3-30 02:16:49 | 只看該作者
50#
發(fā)表于 2025-3-30 07:31:31 | 只看該作者
Developing a Model to Measure Fake News Detection Literacy of Social Media Usersis empirically tested by applying correlation analyses based on a sample of .?=?96. The updated construct provides a way to measure fake news detection literacy and offers various avenues for further research that are discussed at the end of the chapter.
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