People are naturally attracted to excitement. According to new Virginia Tech research, the same is true on social media, where feeds are overrun with international travel, skydiving and other experiences that may seem out of the ordinary.
When social media users are constantly shown rare and unique events, they overestimate how often they occur and seek to share their own. This unbalanced content stream creates a "rareness bias diffusion." The new concept, developed by Alice Jang, an assistant professor, and Viswanath Venkatesh, the Verizon Chair of Business Information Technology, both in the Pamplin College of Business, describes how this bias can spread rare information widely, leaving everyday social media users so exposed to it that they perceive it as more common than it truly is.
"They think everyone else is traveling to Paris or doing something exciting, while they are just sitting at home doing mundane things," Jang said. "It makes people feel like their lives are falling short, when in reality, everyone is mostly doing mundane things—they only post the exciting parts."
The studies revealed that human interaction with social media content is a compounding factor in how inaccurate information spreads online. According to Jang, the research challenges the current understanding of social media content.
The tendency to overlook the ordinary stems from social media users' perception bias and their motivation to seek variety in the content they see and share, according to the research published in MIS Quarterly.
The result is a skewed picture of how the world actually is, where the unusual looks common and the common disappears. This suggests social media users should be cautious—and not for the usual reasons. The distortion on social media is not entirely driven by the algorithm. Instead, it comes from ordinary human biases that social media compounds into a pattern in which rare information gets shared more often.
In the study, Jang and Venkatesh conducted six experiments to understand how people choose what to share online and how they react to seeing rare content. Jang presented research participants with fictional scenarios of a dystopian city under attack by various monsters—something unique that people would probably share online.
Of the various monsters and attacks, participants were shown some scenarios more than others. In the end, participants were more likely to share the scenario they had seen the least. These results reinforce the idea that people are more likely to share content that is unique, rare or out of the ordinary.
Jang then created a simulated network of thousands of social media users and unique social media content to analyze these human biases on a larger scale.
"There is a bias that we simply cannot fix. It's impossible to fix," Jang said. "A lot of the prior literature just disregards this fact and assumes that people are rational."
Venkatesh added, "Yes, it's a bias, but when people know this, they could perhaps know that what we're seeing on social media is others' highlight reel and not day-to-day life. This may reduce harmful negative effects on oneself."
This innate human bias leads social media users to wonder: Is what I am seeing online representative of what is actually happening? While people may actively edit their timelines and curate unique content to post online, the research reveals that the corresponding problem runs much deeper than just a network's algorithm.
There is a baseline human bias that the platform cannot fully fix. Instead, the responsibility falls back on the user.
Ultimately, the research found, social media users need to understand that their feeds are overrun with exciting and out-of-the-ordinary content because of the content they engage with. Having this knowledge allows users to better protect themselves from this false reality on social media.
More information
Alice Jayoung Jang et al, Information Sharing on Social Media: Introducing the Role of Exposure Frequency and Its Emergent Effects, MIS Quarterly (2026). DOI: 10.25300/misq/2026/17766
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Citation: Research reveals how human bias shapes social media feeds (2026, July 20) retrieved 20 July 2026 from https://phys.org/news/2026-07-reveals-human-bias-social-media.html
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