Social media is a great way to connect with people who are distant. There are many things that can be learned from social media and a variety of ways to experience new things. While it is beneficial in many ways, social media can also be very toxic to users. Many users receive their news information for social media sources rather than traditional sources of information. Many of the sources on social media offer their own set of biases, inaccurate information, and even false information that hasn’t been fact-checked. False and biased information can benefit the sources of information in many ways while harming the information’s readers. Researchers at Indiana University identified three different biases, possessed by society, that make misinformation on social media harmful.

Brain Biases
Humans encounter a variety of things throughout their lives and lifestyles that allow them to create biases towards many things. Known as cognitive biases, these biases are formed in order to prevent information overload in a person’s brain. The brain, only capable of processing a finite number of things, develops systems of remembering information.

Societal Biases
People form biases based on their circle of acquaintances and the people they routinely hang around. The group of people that surround a person plays major influences on the thoughts that that person has about the world. People are more likely to accept information when it is accepted by the people around them. Similarly, people tend to reject the information that the people in their surroundings reject. This effect is known as an “echo chamber” and can easily allow groups of people to be manipulated.

Machine Biases
Machine biases can play a major role in the information that a person is shown on social media. Through a task known as machine learning, machines are fed algorithms to decipher the things that people like t online and to determine what to show those people in the future. Social media platforms, eCommerce sites, and search engines utilize machine learning to provide their users with better services. However, machine learning can easily promote biases towards the information that users are already exposing themselves to. Rather than providing a full picture, machine learning can tailor the wrong type of information for a user.

About The Author
Yuri Vanetik is a Political Coalition Builder, Private Investor, Philanthropist, and Entrepreneur that has become known as an expert in business and policy. In addition to working as the leader of Vanetik International, LLC, a corporate management consulting firm, for nearly 25 years of experience, Vanetik has also worked on several political campaigns. In addition to his political work in California, he was also in the state finance leadership for Late Senator McCain’s Kitchen Presidential Campaign. Yuri Vanetik has been featured in a variety of notable publications, including the Wall Street Journal, California Business Journal, Bloomberg Law, and Forbes.

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