Negative Comments Towards Formula 1 Drivers on Twitter

Abstract

In this chapter, we examine the frequency of positive and negative comments on Twitter towards three Formula 1 drivers. The fact that Twitter promotes formations of discursive communities controlled by the fans of the sport facilitates our data collection. We will analyse whether these negative comments are influenced by personal prejudice or stereotypes. Our results confirmed that racial prejudice was witnessed in the data.

1 Literature Review

Run by the Fédération Internationale de l’Automobile (FIA), Formula 1 is widely recognized as the pinnacle of motorsport with the highest team budgets, the most sophisticated technology and an audience of over 400 million people per race [1]. For instance, while the annual budget of a mid-tier Formula 1 team such as McLaren can easily exceed $200 million, other motorsports, like IndyCar, operate on considerably more modest budgets, with top teams often spending a fraction of that amount. Behind this sport there are great millionaires, so it is understood that the drivers are also children of millionaires or owners of the teams themselves. However, the exclusive profile of lovers of this sport has led to hate speech towards drivers of different origins, as is the case of the driver Lewis Hamilton. In this chapter we aim to investigate whether there are more negative or racist comments towards specific drivers from diverse backgrounds in computer-mediated communication (CMC), with a particular focus on Twitter, as it fosters the formation of discursive communities predominantly controlled by fans of the sport. To identify comments that may reflect personal biases or stereotypes, we selected three drivers who differ in terms of both social and racial factors: Lewis Hamilton, Max Verstappen, and Sergio Perez, commonly known as Checo Perez. For the remainder of this chapter, we will refer to him as Checo Perez for brevity and consistency.

CMC refers to human interactions that occur through the use of devices, such as computers, tablets, and smartphones that include email, text messages, and tweets [2]. This type of communication has facilitated interactions between people, since it occurs instantaneously and does not require the person to be present. The disadvantage, however, is that this means of communication has given freedom to people to use more aggressive language due to the fact that many of their identities are not exposed [3]. Similarly, some users adopt various strategies in order to remain anonymous, such as using multiple accounts and usernames and falsifying their identities to hide themselves [4]. Consequently, the CMC has become a research site for linguists as these platforms are public and free, simplifying the way of extracting data [5].

2 Drivers

Lewis Hamilton was born in Stevenage, Hertfordshire, England, on January 7, 1985, into a mixed-race and low-income family. Differently from most Formula 1 drivers, whose families support their racing careers, Lewis Hamilton had the opportunity to become an F1 driver, thanks to funding from McLaren, who noticed his early talent in the sport. Lewis Hamilton was one of the first drivers of colour in Formula 1 when he started in 2007. Even though diversity in Formula 1 has been slowly increasing, Lewis Hamilton is the only black driver. Max Verstappen was born in Hasselt, Belgium, on September 30, 1997. He hails from a background that is neither high society nor low in resources. He came to Formula 1 at the age of 17, becoming the youngest driver in history. Like many drivers in the sport, Max Verstappen received financial support in his early career, with his father, Jos Verstappen, being a former racing driver who played a pivotal role in his development. Checo Perez was born in Guadalajara, Mexico, on January 26, 1990. Checo Perez is also a current driver of colour in the sport; however, the driver comes from a wealthy family. His father is a politician, with a name of great importance in Mexico [6].

3 Hypotheses and Research Questions

We expect to see Hamilton as the most discriminated person or victim of negative comments and the second to be Checo Perez. We anticipate that Max Verstappen will receive less negative comments out of the three drivers. Also, we anticipate that many of the negative comments against Hamilton will be due to the fact that a person of colour has never before been seen competing in a high-status sport or that is usually reserved for white people from high society. We hypothesize that the type of attack Hamilton faces will be not so much due to his performance as a driver but rather due to his appearance. Therefore, we believe that Hamilton will experience hate speech, which is a long-standing issue that has historically been utilized as an extreme method of displaying rejection and intolerance towards those perceived as different [7].

4 Methodology

Twitter is a blog site where people can freely express their opinions publicly. Conversations or posts are usually informal and very direct. Its simplicity has attracted a large user base [3]. With this in mind, we decided that Twitter would be the ideal social network to collect data and verify which words are usually recurring for the three selected drivers. To collect and formulate the corpus for this research, we used Rstudio, which allowed us to collect tweets through the Rtweet package, available for free on http://rstudio.org/. In order to obtain a robust and representative corpus of the cultural diversity presented by fans of this sport, we decided to collect 5000 tweets from each driver in Spanish, English, and Portuguese from December 7 to December 10, 2022, two weeks after the last race of the season. Next, we tokenized the tweets and checked the frequency of both positive and negative adjectives for each driver. Adjectives unrelated to drivers and adjectives with incidence less than two were excluded from the analysis. As the data retrieved using Rstudio contains unigrams, we analysed all comments that included the selected adjectives for each driver to answer our research questions. The initial idea for this evaluation was to collect tweets for four days; however, we had issues with RStudio and ended up losing relevant data from one of the drivers, which caused a two-day reduction of data collection.

5 Results

These tweets took place after the 2022 season ended, where Max Verstappen second world title with Red Bull Racing. Lewis Hamilton had faced a challenging season with the Mercedes car, contending with performance issues and struggling to maintain competitive consistency throughout the year. Checo Perez finished his first year as a supporting driver for Red Bull Racing, where encountered difficulties adapting to the car, experiencing intermittent challenges consistently. Below, we respectively present the most frequent adjectives in English, Spanish, and Portuguese. These are subdivided by the drivers in alphabetical order. As mentioned, results unrelated to drivers or with incidence less than two were excluded. To facilitate the reading of the data, we present the results in bar graphs, in which the positive adjectives organized according to their frequency are first indicated, followed by negative adjectives also organized according to their frequency.

5.1 Frequency of Tweets in English

5.1.1 Checo Perez

Fig. 2.1

Frequency of both positive and negative adjectives in English tweets for Checo Perez

We can observe that the frequency of positive and negative comments is very similar (Fig. 2.1). The adjective poor is used to show sympathy or pity towards Checo Perez, which could be considered positive or negative. These tweets took place after the end of Checo Perez’s first year as the second driver for Red Bull Racing, the top contender racing team, and was looking forward to solidifying his position as a top-tier driver, so the use of the adjective poor may be related to his rank in 2022 as a supporting driver. Another adjective that caught our attention was racist, but after reading the tweets, we realized that his fans are referring to people who use negative language towards the driver.

5.1.2 Lewis Hamilton

Fig. 2.2

Frequency of both positive and negative adjectives in English tweets for Lewis Hamilton

As expected, the frequency is high (Fig. 2.2). Tweets that include the term “God” are unrelated to the driver, but rather refer to expressions like “Oh, my God!”. Other adjectives focus mostly on his physical appearance. Two adjectives that interested us were racist and uncle. Similarly to Checo Perez’s case, his fans were defending him, calling his haters racist. The adjective uncle is maybe being used with a racist intent, as can be seen in Example 1.

• Example 1: @[username] Gimme my money my great great uncle Lewis Hamilton III was a slave back in the 1860’s

5.1.3 Max Verstappen

Fig. 2.3

Frequency of both positive and negative adjectives in English tweets for Max Verstappen

The most prevalent words here are talented and GOAT, which stands for “the greatest of all time” (Fig. 2.3). It is interesting that the adjectives of high frequency relate to his ability as an athlete, not to his appearance.

5.2 Frequency of Tweets in Spanish

5.2.1 Checo Perez

Fig. 2.4

Frequency of both positive and negative adjectives in Spanish tweets for Checo Perez

The frequencies of positive adjectives are lower (Fig. 2.4). We see a mix of compliments related to his physique and talent as a driver. We did not expect these results because Checo Perez has a larger audience in Spanish-speaking countries. However, the negative adjectives confirmed our initial assumption, as they are not aimed at the driver himself, but at his haters.

5.2.2 Lewis Hamilton

Fig. 2.5

Frequency of both positive and negative adjectives in Spanish tweets for Lewis Hamilton

The result of these frequencies did not surprise us (Fig. 2.5). What we find interesting is that the three adjectives illustrated above are very common and do not seem to reinforce or emphasize Lewis Hamilton’s abilities. One possible reason for these results is his rivalry with Checo Perez and his historic competition with Fernando Alonso, a two-time Spanish Formula 1 champion. On the other hand, there are negative adjectives that, despite their low frequency, appear to carry racist connotations, as can be seen in Example 2:

• Example 2: Hay diferencia entre monos y Lewis Hamilton, y es que el segundo es el macho alfa (There is a difference between monkeys and Lewis Hamilton, and the second is the alpha male)

5.2.3 Max Verstappen

Fig. 2.6

Frequency of both positive and negative adjectives in Spanish tweets for Max Verstappen

The frequencies of both types of adjectives are low in Max Verstappen’s case, because the tweets are in Spanish (Fig. 2.6). Verstappen is not only complimented for his abilities but also for his physical appearance. Negative adjectives are somewhat different from positive ones, as they only focus on criticizing his appearance.

5.3 Frequency of Tweets in Portuguese

5.3.1 Checo Perez

Fig. 2.7

Frequency of both positive and negative adjectives in Portuguese tweets for Checo Perez

In Portuguese, results show low frequencies in both positive and negative words (Fig. 2.7). A problem we noticed when analysing the data was that many tweets had the Portuguese verb checo, the conjugation of this verb in first person singular is a homograph of Checo Perez’s first name, which resulted in many unrelated tweets. Some tweets related to positive adjectives, such as melhor (best) and atraente (attractive), mention Checo Perez ironically. The negative results emphasize Checo Perez’s position as Max Verstappen’s second driver, mirroring the results seen in Spanish.

5.3.2 Lewis Hamilton

Fig. 2.8

Frequency of both positive and negative adjectives in Portuguese tweets for Lewis Hamilton

The frequency of positive tweets in Portuguese is somewhat high, which seems to indicate the popularity of Hamilton in Portuguese-speaking countries, especially in Brazil, where the driver has received the distinction of honorary Brazilian citizen (Fig. 2.8). Part of the adjectives are related to his skills as a driver, including goat. The words lindo and bonito relate to his appearance, as noticed in tweets in English or Spanish. In contrast, most of the negative tweets do not seem to be related to his skills but to other personal or physical characteristics, such as feio (ugly) and maluco (crazy). After analysing the tweets, we noticed that these words are used as intensifiers in Portuguese. Other adjectives, such as manchado (tainted) and racista (racist), were actually used to describe the former driver Nelson Piquet, who made a racist comment in 2021 when referring to Lewis Hamilton.

5.3.3 Max Verstappen

Fig. 2.9

Frequency of both positive and negative adjectives in Portuguese tweets for Max Verstappen

As we have seen in English and Spanish, many of the adjectives reinforce Max Verstappen’s skills as a Formula 1 driver, which corroborates our hypothesis (Fig. 2.9). In the same way, the negative comments also focus on Verstappen’s attributes as an athlete. In conclusion, none of the adjectives make reference to his heritage or skin colour.

6 Discussion

The data tell us that our hypothesis is confirmed, with negative comments accounting for personal biases and discrimination. We find negative adjectives towards the three drivers in all three languages. Even so, most adjectives of racial connotation refer to Hamilton. After analysing his tweets in Spanish, it was possible to conclude that when Hamilton is mentioned, most comments are not only attacking his ability as a driver, but mainly focused on his physical appearance. In fact, racist adjectives prevail, which was not the case for Checo Perez. We also verified that Verstappen received many negative comments, contradicting our hypothesis. However, negative comments make no mention of his heritage or ethnicity, which seems to confirm that the issue of race is not a relevant factor in this case. While one might anticipate a more culturally diverse and respectful fan base in an international motorsport as Formula 1, our conclusion points to a disheartening reality: fan bases within the sport often exhibit biases and discrimination against drivers from diverse backgrounds and racial backgrounds.

For future research, it would be interesting to repeat the same study with a larger dataset encompassing public figures in sports who are frequently subjected to stereotypes in order to determine if any discernible patterns emerge. Anonymity allows people to freely exchange ideas and opinions that, expressed otherwise, could irrevocably damage their reputation or cause them personal harm. This observation has been confirmed through the analysis of the tweets conducted in this chapter. With this in mind, we hope that our study can inspire other researchers on the topic of aggression towards athletes based on their heritage in CMC.