Fake News Detection is defined as the prediction of the likelihood that a particular news article is intentionally misleading, something that, until now, has been done by journalists and news editors. With the Sherpa.ai Fake News Detection API, you can determine the validity of a news items with the help of vaious methods in our multi-step system, including Source Analysis and Network Analysis.
Traces the source of the message in order to determine its reputation and reliability.
- Media Sources Reputation Indicator: Assesses the reputation and reliability of the digital media sources that have published news related to the input information, and classifies them into categories such as Bias, Hate, Conspiracy, Clickbait, Reliable, Satire, Parody, Fake, etc.
- Twitter Account Reputation Indicator: Analyzes the reputation and reliability of the Twitter accounts that have published or interacted with tweets related to the input information, and also determines whether accounts are handled by humans, bots, or cyborgs.
Analyzes the spread of the message over time, in order to determine the dissemination patterns and the characterization of the people/bots involved.
- Twitter Virality Indicator: Analyzes the propagation of information in tweets over time, and also provides information about progation patterns related to retweets, replies, and the rate of acceleration.
- Fake News Twitter Classifier Indicator: Establishes whether or not a piece of information is fake, by training a Machine Learning binary classifier using several features extracted from the collection of tweets related to the input information, including quotes, followers, capital letters, and more.
- Enter the URL of your News Article or Tweet in the box
- Click “Analyze”
- Evalue the scores
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