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The rise of fake news in the past decade has brought with it a host of consequences, from swaying opinions on elections to generating uncertainty during a pandemic. A majority of methods developed to combat disinformation either focus on fake news content or malicious actors who generate it. However, the virality of fake news is largely dependent upon the users who propagate it. A deeper understanding of these users can contribute to the development of a framework for identifying users who are likely to spread fake news. In this work, we study the characteristics and motivational factors of fake news spreaders on social media with input from psychological theories and behavioral studies. We then perform a series of experiments to determine if fake news spreaders can be found to exhibit different characteristics than other users. Further, we investigate our findings by testing whether the characteristics we observe amongst fake news spreaders in our experiments can be applied to the detection of fake news spreaders in a real social media environment.
Recent years have witnessed remarkable progress towards computational fake news detection. To mitigate its negative impact, we argue that it is critical to understand what user attributes potentially cause users to share fake news. The key to this ca
The history of journalism and news diffusion is tightly coupled with the effort to dispel hoaxes, misinformation, propaganda, unverified rumours, poor reporting, and messages containing hate and divisions. With the explosive growth of online social m
The popularity of social media platforms such as Twitter has led to the proliferation of automated bots, creating both opportunities and challenges in information dissemination, user engagements, and quality of services. Past works on profiling bots
The wide spread of fake news in social networks is posing threats to social stability, economic development and political democracy etc. Numerous studies have explored the effective detection approaches of online fake news, while few works study the
Todays social media platforms enable to spread both authentic and fake news very quickly. Some approaches have been proposed to automatically detect such fake news based on their content, but it is difficult to agree on universal criteria of authenti