Receiving a thumbs-down on social media does not push people away from the conversation, but instead tends to encourage them to post more while softening their tone. A new study published in the Journal of Marketing Research provides evidence that negative peer feedback prompts users to remain engaged rather than retreating into isolated communities. These findings suggest that allowing downvotes on social platforms might help moderate extreme discussions without silencing individual voices.
Social media platforms continuously experiment with ways for users to interact and evaluate the posts of their peers. While almost all platforms feature a button to express approval, few allow people to explicitly express negative feedback. Recently, major networks like YouTube and X have explored adding dislike or downvote features to help regulate content.
“We started thinking about this after hearing about YouTube hiding the dislike count, and Twitter (now X) and TikTok testing downvote-style features,” said Jessica Fong, an assistant professor of marketing at the University of Michigan Ross School of Business who is transitioning to the University of Maryland College Park. “We looked into the literature and it became clear there wasn’t much empirical evidence around the effects of downvotes on UGC. At the same time, there’s a lot of discourse about polarization and social media and dislikes have some pretty interesting implications, like whether allowing dislikes pushes users into echo chambers.”
UGC refers to user-generated content, which includes any comments or posts created by the platform’s community. Managers often worry that negative feedback might push these users into isolated groups of like-minded individuals. These isolated groups are often called echo chambers, which occur when people only interact with others who share their exact views. When individuals exist entirely within an echo chamber, it tends to increase political and social division.
To understand how negative feedback actually changes behavior in a real online environment, the scientists looked at Reddit. Reddit is a massive online message board organized into thousands of specific communities. These communities, known as subreddits, are places where users discuss everything from breaking news to niche personal hobbies. On Reddit, people can either upvote a post if they like it or downvote it if they dislike it.
The platform calculates a visible score for every comment by subtracting the downvotes from the upvotes. Additionally, each user has a public reputation score called karma. A person’s karma goes up when they receive upvotes across the site and goes down when they receive downvotes. The authors chose this environment because discussions are highly opinion-based, and the feedback system is publicly visible.
By analyzing how people react when their scores drop, the researchers aimed to see if negative feedback alters how often people post, where they post, and the emotional intensity of their writing. To explore these questions, the researchers tracked a sample of 17,525 Reddit users over a period of 61 days. They monitored these individuals on a daily basis to capture their ongoing habits.
The team collected data on nearly two million comments across more than 32,000 different subreddits. For every comment a user made, the scientists recorded the text, the community it belonged to, and how its upvote and downvote score changed over the following two weeks. Studying the direct impact of downvotes is complicated because people who post highly controversial opinions might naturally attract more negative feedback and also just post more often in general.
To isolate the specific effect of receiving negative feedback, the scientists used a psychological concept known as left-digit bias. Left-digit bias is the human tendency to pay the most attention to the first number in a sequence. For example, a price dropping from ten dollars to nine dollars feels much more significant than a drop from eleven dollars to ten dollars.
The authors applied this concept to Reddit karma. They assumed that a user would notice a drop in their karma much more if the first digit changed, such as falling from 101 to 99, compared to a drop from 102 to 100. Even though both examples represent a loss of two points, the change in the first digit makes the penalty feel much larger. By comparing users who experienced this highly noticeable drop in karma to those who experienced a less obvious drop of the exact same size, the researchers could measure how noticeable negative feedback changes behavior.
The scientists found that experiencing a noticeable drop in karma actually increases a user’s likelihood of posting again. “Downvotes don’t silence users. On Reddit, users who get downvoted actually post more afterward, not less,” Fong said. Rather than quitting the platform out of frustration, the users increased their overall content creation.
“We were initially surprised to find that getting more downvotes makes users post more on average. This pushed us to look more into the mechanism,” Fong said. “We find that people tend to post more because they’re trying to recover their reputation, which on Reddit is in the form of karma. They tend to post more after getting downvotes until their karma recovers to the level of before they got those downvotes.”
Next, the research team looked at where these individuals chose to post after being downvoted. A common worry is that people will leave the community that rejected their opinion and seek out an echo chamber where everyone agrees with them. The data provides evidence that this does not happen. Users continued to comment in the exact same communities where they received the negative feedback.
“Downvotes don’t appear to create or encourage echo chambers,” Fong said. “We don’t find evidence that users abandon the communities where they were downvoted. They keep engaging there while also branching into new spaces.” This detail is highly relevant for social media companies, as it implies that downvotes do not automatically cause people to segregate themselves into divided groups.
Finally, the authors analyzed how negative feedback changed the actual words people used. They wanted to know if getting downvoted made people double down on extreme opinions or if it encouraged them to soften their language. To measure this, the scientists used a machine-learning language tool to scan the text of the comments. This tool identifies the main topics of a sentence and assigns an intensity score based on how emotional or extreme the wording is.
For this part of the study, the researchers looked at what happened when a specific comment’s score dropped from a positive number into the negatives. They found that when an intensely worded comment was downvoted below zero, the user tended to moderate their tone the next time they mentioned that same topic. “Downvotes tend to moderate the tone of what users say next, especially when their original post was emotionally charged,” Fong said.
While these findings present an interesting perspective on platform design, there are some limitations to consider. “Our study is a case study on Reddit, so we must be careful in interpreting these effects on other social media platforms,” Fong said. “We expect these effects to generalize to settings where there is some kind of reputation, like on Reddit, but looking at whether these effects replicate in other settings would be an interesting avenue for future work.”
Looking ahead, the researchers are already expanding this line of inquiry. “My coauthors, Varad Deolankar and S. Sriram, are working on another project related to user-driven content and polarization, except this time we were interested in looking at content consumption rather than production,” Fong said. “We ask, how does the content platforms serve users, and how users interpret that content, drive polarization of beliefs?”
The research team is currently exploring how individual biases interact with platform algorithms. “This is ongoing work, but so far we find that people tend to put less weight on information that conflicts with their prior beliefs,” Fong said. “And this bias contributes to polarization nearly as much as algorithms do (algorithms have been commonly blamed for contributing to polarization).”
The authors are also looking into how different site metrics influence these outcomes. “We also ask whether the engagement metric a platform chooses to optimize (‘likes’ versus dwell time) matters. We find that it does,” Fong said. “Dwell-time-maximizing algorithms produce less polarization than like-maximizing ones. Part of the reason is that users dislike content that opposes their views but spend just as much time reading it as content they agree with, so an algorithm that maximizes dwell time is going to serve more opposing articles than articles that maximize likes.”
The study, “The Effect of Downvotes on Content Creation: Evidence from Social Media,” was authored by Varad Deolankar, Jessica Fong, and S. Sriram.
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