To conclude, based on our empirical data, both the race of the user and the app’s race-biased algorithm impact the choices one can make on Tinder, thus playing a big role in online dating patterns. Both profiles attracted and matched more men of their own race and “smile” is a relatively important factor for both profiles. However, the reasons behind which men matched with them vary. While men tended to focus on more superficial things of the white girl, such as her physical appearance, the black girl’s bio which indicates more her personality got more attention. This is interesting considering that both had the same description.

That being said, there are certain limitations to our project. We were indifferently swiping through all of the profiles that were presented to us by algorithm, but after some reflection, we realized that different demographics frequent Tinder during different times of the day. Thus, having a fixed period of time for swiping is what we would do differently. We think that researching the various demographics of the day would help us to craft out a more specific problématique. 

To develop our project, one could compare different cities. Our experiments and of course conclusion were on the local level of Reims, but we could have made it more universal, taking into account cities of different sizes, diverse countries and cultures. It could have been interesting as well to use the same picture for both profiles but with different descriptions, to see to what extent the personality plays a role. However, Tinder users would probably have found it suspicious and the accounts might have been deleted. People of other skin colors could also be a part of the research, and it could be interesting to observe the reception of the Tinder profiles of people of different skin colors in different countries or cultures. Finally, other sexualities, genders and people of the LGBTQ+ community could be part of the research as well, so that the scope of our project encompasses various types of individuals, rendering it more inclusive.

Tinder thus allows many experiments to be undertaken, and is an ideal platform to understand human behavior, as well as the impact of digital culture on our daily lives.

“No-one was harmed in the making of this research!”

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When conducting a study on the general public, we have to be particularly careful with the ethical considerations behind our research. Specific measures need to be taken to ensure that no harm is caused to others. Especially with a research topic as controversial as ours on race and romantic relationships, we had to be extra careful during our field investigation.

In order to ensure that our project is ethical, we took several extra measures during the preparation and the investigation phase.

1. Stock photos to set up the tinder profiles

For the tinder profiles, we used stock photos which can be purchased online for a wide variety of purposes. By using stock photos, we ensured the privacy of everyone involved in the research and we were able to have more similar-looking profiles than if we had used pictures of our own. We also hoped that by using stock photos we would prevent all possible hate speech towards one of the pictured persons: if it had been a personal picture, it could have had long-lasting effects on the person pictured.

2. Debriefing with the candidates

After we finished asking our questions to the Tinder users, we took time to explain to each person that we matched and texted with that this was merely for the purpose of an investigation. Indeed, we didn’t want them to have their hopes up by bonding with a fictional character. We explained the aim of our project and asked them if they were fine with us using their data. 

3. All data published was anonymous.

In our published data, we made sure not to include any full names to protect the participant’s identity. So even if a participant agreed to the use of his data, no names were published and his anonymity remained protected.

We collected data for 9 days in which we swiped 30 people per day totaling 270 people. We were trying to discover whether or not there was a racial bias in tinders algorithm and whether or not the race of the girls changed what race of men matched with them. 

Overall Sara matched with 137 people whilst Anna matched with 151 people.  We found that overall Sara was proposed 34 black men whilst Anna was proposed 21. From these results we can see that tinders algorithm must have influenced the number of black people proposed to the girls as they swiped the same number of men and did not reject any. 

Through the data collected we also created Graphs I and II that show the race of the people that matched with the girls. The races we include: Caucasian, African, Asian and Middle-Eastern. 

Diagram I  

Diagram II 

Diagram I and II show that their race affect the choices people take on tinder. There were 13% more caucasian people that matched with Anna a caucasian female than with Sara. On the other hand 8% more black people matched with Sara than with Anna. This indicates that either black people prefer to swipe black people or that the tinder algorithm impacts the choices one can make. There is probably a bit of both that plays into the results we received. Sara swiped overall  24.8% of black males whilst Anna swiped 13.9%. This demonstrates that the tinder algorithm attributed more black males to Sara than Anna. However interestingly a 7.9% less black men swiped right on Anna than she swiped right on. For Sara about 10% of the black men she swiped right did not swipe back. This was surprising as it showed that actually more black men on average swiped right on Anna than Sara. However, this result is not very clear as it might not have been the race that impacted the mens’ decisions rather than the name of the girls or what clothes they were wearing even though we tried to keep these things similar. Moreover, it could also have something to do with the fact that Sara was simply proposed to more black men so more black men could also swipe left on her. 

The box-whisker graphs demonstrate what age groups were most likely to swipe right on both Anna and Sara. For Anna the median age lay at 23 whilst for Sara it was slightly older, 24. Moreover from the graphs one can also see that the ages of the men that swiped right on Anna were more spread than the men that swiped right on Sara. The lower quartile for anna was around 21 years of age whilst it was about 23 for Sara. Moreover the upper quartile was 26 for Anna and 27 for Sara. This demonstrates that Anna perhaps attracted a wider range of mens age groups whilst Sara attracted a slightly older group of men. However for both Anna and Sara the extremities of the graph show the minimum and maximum ages of men that were interested were quite similar at 18 and 33,34 years.

Diagram III

Diagram IV 

For the textual analysis part we looked at the responses of the tinder matches to the following questions: “Hey, I’m happy that we’ve matched!” and “I was wondering what made you like my profile/swipe right?” 

Diagram V: Sara

Diagram VI: Anna 

When comparing the words used by the men to respond to the first text message we can see that the word most commonly used is a greeting. Then for Sara it is interesting to see that her name comes up relatively frequently whilst for anna it does not. Moreover for Sara the men quickly start describing her features and the words “pretty” and “happy” appear. For Anna they stayed more superficial and did not go into detail yet what they think about her. 

Diagram VII: Sara

Diagram VIII: Anna 

The diagrams above show the words that the men used to answer the question why they chose to swipe right. For Sara, it seems as though the description of her bio seems to be a main factor while for Anna the reasons seem relatively superficial. The men told Sara they chose her because of her like of music and the cinema whilst they told Anna they like her because they think she is cute, smiley, has nice hair, and has pretty dimples. However for both their smile seems to be important as many men commented on this feature. To conclude this analysis has shown that men responded differently to a certain extent to both women even though we used the same texts and the same generic responses. Moreover, the data has shown that Sara attracted slightly older men and had a higher total of black men that matched with her even though Anna was more often chosen by black men that she had swiped right, than Sara.