Can Crowdsourcing Fix Dating Apps?

A new approach to dating platforms replaces algorithms with crowdsourced votes from human matchmakers who “vibe-check” to set up potential pairs

Existing dating apps have a people problem, but a new dating platform is designed to fix swipe fatigue by using crowdsourcing to vet potential matches. It sounds like fun to get to weigh on the how well a couple “matches.” As a psychologist, however, I had a moment of pause, thinking about how powerful social feedback is to self-esteem. I question whether using the wisdom of the crowds is, in fact, very wise. The judgment of strangers may introduce as many problems as it solves. What do you think?

Key Points

1. A new dating app replaces algorithms with matched pairs based on community votes.
2. Does gamifying matchmaking turn users’ romantic lives as “Love Island Meets TikTok”?
3. We are all sensitive to the opinions of others. Crowdsourced matches may feel like social judgments of a user’s attractiveness and status.

Years of swiping, matching, ghosting, and starting over have turned the search for a partner into something that can feel more like online shopping than the spark of a meet-cute. Dating apps dramatically expanded the pool of people we can meet, but the sheer number of choices can make using them exhausting. The endless pool of potential partners raises expectations and fuels FOMO by creating the illusion that the “perfect” partner is right around the corner. This constant pursuit of an idealized match transforms a simple search into a compulsive behavior pattern that takes a toll on well-being. Excessive swiping has been linked to upward social comparison, fear of being single, partner-choice overload, and lower state self-esteem (Thomas et al., 2022; Thomas et al., 2023). So, it’s no surprise that more than 75% of users report burnout, tired of repetitive small talk, widespread ghosting, breadcrumbing, love bombing, misrepresentation, or scams (Pershikov, 2025).

One solution is proposed by a new dating platform called Rivet that wants to put people back into the process. In trying to fix the algorithmic swipe-and-ghost model, however, this approach may have traded one problematic mechanism for another. Letting others decide how “well you match” strikes me as something closer to the social dynamics of a high school cafeteria where people you don’t know evaluate your romantic appeal.

From Algorithms to Human Matchers

A dating network where “real people, not algorithms” set people up sounds like an improvement. Interestingly, you can join as a matcher, not a dater, to vote on how well potential pairs match. Pairs with enough community support get introduced, though the individuals ultimately decide whether to pursue the match.

Traditional matchmaking isn’t crowdsourced. A friend who sets us up knows something about us, including our personality, values, relationship history, and sense of humor.

On Rivet, anonymous Matchers judge from profiles, including a “Reflection” feature that generates full-body images of two users placed together in a single frame. Matchers use these images to decide whether they “feel a match” visually.

I can see why this is an appealing idea. I mean, who doesn’t like to meddle in other people’s affairs? But this approach taps into the very thing we want to avoid—projecting our preconceived ideas about people based on looks. But dating apps, by definition, have limited information. When information and context are scarce, our brains rely more heavily on heuristics and stereotypes. We are hardwired to process visual data immediately, meaning appearance is the primary shorthand for evaluating strangers. Even without “reflecting” on images, we rely overwhelmingly on what we can see (Alhabash et al., 2014; Chappetta & Barth, 2022). How long have we railed against media that triggers judgments about people based on racial and gender stereotypes or physical attractiveness?

Human intuition isn’t bias-free. Wisdom of the crowds doesn’t eliminate the algorithmic problem. It just shifts it to a human algorithm based on aggregated cultural norms, stereotypes, and biases.

“They look cute together.”

“They seem like the same type.”

Those judgments tell us more about the person doing the matching than about the compatibility of the two people being matched.

When Matchmaking Becomes a Game

Gamification can make things more fun, but it also shifts incentives. The users who vote on matches are rewarded for participating through points and other tracking measures such as matcher quotient, couples paired, contrarian wins, and top matcher rankings. Swiping has already become a form of entertainment, with friends clustered around a phone debating who should get a right or left swipe. My concern is that crowdsourcing formalizes that social judgment, elevating the spectators’ role and turning other people’s romantic lives into the game.

Matching strangers is fun. You don’t have to be single or get rewards to find matchmaking entertaining. Our brains use stories to make sense of the world and establish the causality we seek to feel comfortable. Stories have patterns and we crave resolution to restore order. Fans routinely “ship” fictional characters, celebrities, and even actors who play romantic partners, imagining who belongs together and looking for evidence of chemistry. People also tell stories about strangers based on a few curated photos on dating apps and social media. We even assign names and personalities to AI chatbots (Degen, 2025). Dating apps already turn our tendency to form parasocial attachments into a platform feature. But now, instead of debating whether two characters or celebrities should be together, eager users will get to weigh in on the romantic narratives of real strangers, combining an activity that may already be intrinsically rewarding with tangible rewards.

From Private Rejection to Social Judgment

Relying on the crowd magnifies the perceived audience. A swipe is one person’s decision. A crowdsourced match reflects the judgments of many, which turns a recommendation into a social evaluation. We are already conditioned to interpret social media signals (likes, comments, emojis) as validation of worth. No matter who we are, our self-esteem is sensitive to cues about how much others value and accept us (Leary et al., 1995). Our beliefs about how others see us can shape how we see ourselves (Xu et al., 2015).

An undesirable match can invite social comparison, particularly if users interpret the pairing as information about their own attractiveness or desirability: “Is this how other people see me?” The sensitivity around acceptance and rejection is a normal human response, part of our innate survival mechanisms. Existing dating apps already trigger these feelings but this might be worse.

Bad matches can have an outsized impact. Thanks to our innate survival instincts, negative information makes a bigger impression and carries greater weight than good. An insulting or inexplicable match could produce embarrassment, resentment, shame, or self-doubt instead of being dismissed as a bad recommendation.

The size and consensus of a group can affect our judgments, especially when we’re uncertain or working with limited information. If several people endorse a potential match, users may take that consensus as evidence that the person is compatible or desirable, discounting their own instincts and reservations.

Putting the Humanity Back in Dating

Online dating can feel lonely, transactional, and exhausting, stripped of so much social context. But will the opinions of many actually improve the experience or unleash a new can of worms?

Rivet includes several features intended to keep matchers thoughtful and accountable and make it harder for bad actors, bots, and anonymous trolls to manipulate recommendations. But, it appears that the accountability system relies on the same gamification model of scores, status, rewards, and rankings. The more status and rewards depend on matcher performance, the greater the temptation to optimize the score rather than consider the human beings involved.

I applaud the creative attempt to overcome problems with current dating apps. A potential upside of a community system may be that it functions as “weak ties,” providing a bridge to possibilities and networks users might otherwise overlook. But strangers “feeling a match” because two people look right together risks trading one arbitrary matching system for another.

Community matching may feel like a welcome change after years of swipe fatigue. The novelty, social participation, and illusion of intimacy give people plenty of reasons to try Rivet. However, an appealing experience and an effective matching system are different things. That people will enjoy matching others is an easy bet. Whether strangers are any good at matching people without creating a different set of psychological stressors is another question.

References

Alhabash, S., Hales, K., Baek, J.-h., & Oh, H. J. (2014). Effects of race, visual anonymity, and social category salience on online dating outcomes. Computers in Human Behavior, 35, 22–32. https://doi.org/10.1016/j.chb.2014.02.016

Chappetta, K. C., & Barth, J. M. (2022). How gender role stereotypes affect attraction in an online dating scenario: A replication and extension. Journal Of Social And Personal Relationships, 39(9). https://doi.org/10.1177/02654075221090494

Degen, J. L. (2025). Introducing the spectrum of parasociality. In The shaping of the parasocial self: On the psychology of relationships and intimacy in the digital era (pp. 1–6). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-07438-6_1

Leary, M. R., Tambor, E. S., Terdal, S. K., & Downs, D. L. (1995). Self-esteem as an interpersonal monitor: The sociometer hypothesis. Journal of Personality and Social Psychology, 68(3), 518–530. https://doi.org/10.1037/0022-3514.68.3.518

Pershikov, A. (2025). Swipe fatigue: Trends reshaping the dating app industry. Forbes.

Thomas, M. F., Binder, A., & Matthes, J. (2022). The agony of partner choice: The effect of excessive partner availability on fear of being single, self-esteem, and partner choice overload. Computers in Human Behavior, 126, 106977. https://doi.org/10.1016/j.chb.2021.106977

Thomas, M. F., Binder, A., Stevic, A., & Matthes, J. (2023). 99+ matches but a spark ain’t one: Adverse psychological effects of excessive swiping on dating apps. Telematics and Informatics, 78, 101949. https://doi.org/10.1016/j.tele.2023.101949

Xu, X., Chen, Y., Zhao, Y., & Yang, J. (2015). The neural evidence of the reflected appraisal process as a main path for learning about one’s self-knowledge of personality. Biological Psychology, 110, 85–90. https://doi.org/10.1016/j.biopsycho.2015.06.017