How Negative Commentary on Twitter Shapes Formula 1 Driver Credibility
Megan R. Ricketts
Department of Communication, Illinois State University
COM 297: Research Methods in Communication
Instructor: Fernando Severino
July 13, 2025
How Negative Commentary on Twitter Shapes Formula 1 Driver Credibility
This study investigates how negative commentary on Twitter influences fan perceptions of Formula 1 driver credibility. While prior research has examined the impact of traditional media narratives and online discourse in sports, limited attention has been given to real-time, user-generated criticism within motorsports. Using a mixed-methods content analysis, this research analyzed 37 tweets posted in the 48 hours following the 2025 British Grand Prix, categorizing them by sentiment and type of negativity. Personal insults and performance critiques emerged as the most common forms of negative commentary, with credibility attacks appearing less frequently and threatening language being rare. Engagement analysis revealed that negative tweets did not consistently outperform positive or neutral ones, challenging assumptions about negativity driving online interaction. Technical limitations, including the failure of a Python scraping script, reduced the dataset size and required manual collection. Despite these constraints, the findings suggest that repeated exposure to critique-based negativity may shape fan narratives about driver legitimacy. This study contributes to emerging conversations on the role of social media in shaping athlete reputations and highlights Twitter’s unique influence on sports discourse. Future research should explore how different types of online negativity affect fan attitudes across broader contexts and controversies.
Introduction
Social media has transformed the way sports fans engage with athletes, offering unprecedented access and interaction opportunities. In the world of Formula 1 (F1), this direct connection has fostered vibrant fan communities across platforms such as Twitter (X) and Instagram. However, it has also opened the door for widespread negative commentary, often amplifying criticism toward drivers, teams, and race officials. These online conversations not only reflect but also shape fan perceptions, creating an environment where opinions can shift rapidly based on trending discourse.
As an avid follower of F1, I have observed how narratives surrounding drivers fluctuate in response to the social media climate. Despite the growing role of digital platforms in sports communication, there remains a gap in understanding how online negativity specifically influences perceptions of athlete credibility. Research in sports communication and media studies has explored the impact of traditional media narratives, but the influence of real-time, user-generated commentary is still emerging as a critical area of study.
This research project examines how negative commentary on Twitter affects fans’ perceptions of Formula 1 drivers’ credibility. By analyzing social media discourse, this study aims to better understand the relationship between online criticism and fan attitudes. The findings can offer valuable insights for teams, drivers, and communication professionals seeking to navigate the complexities of online reputation management in the high-stakes world of motorsports.
Literature Review
Social media has transformed the relationship between sports, athletes, and fans, creating spaces where perceptions of credibility and legitimacy are constantly negotiated. Sanderson and Truax (2014) examined how college football fans use Twitter to direct belittling, mocking, and threatening language toward athletes after mistakes. While their context focuses on college sports, their findings reveal a broader pattern: negative commentary can undermine athlete credibility and mental well-being. This serves as a foundation for understanding how similar toxicity toward Formula 1 (F1) drivers might damage perceptions of their competence or worthiness among fans.
Extending this discussion into motorsport, Le Clue (2025) investigated fan narratives during the #VoidLap58 controversy in Formula 1. Her study shows how fans constructed conspiracy theories questioning the legitimacy of race results, polarizing the community and sparking distrust in governing organizations. While Sanderson and Truax (2014) focused on athlete-directed insults, Le Clue highlights how fan-generated narratives can challenge the credibility of entire sports institutions. Both studies reveal the powerful role social media plays in shaping public trust and athlete credibility.
Valdelomar Barquero (2023) further explores this phenomenon by analyzing Formula 1 online brand communities (OBCs) across Instagram, Twitter, TikTok, and Reddit. Her findings indicate that the platform used significantly influences brand loyalty and advocacy, with Twitter facilitating analytical discourse and Instagram fostering visual loyalty expressions. This suggests that when negative commentary spreads on platforms like Twitter—where analytical discussions dominate—it may more directly impact perceptions of driver credibility compared to platforms focused on visual branding.
Platform affordances also shape fan behavior and motivations. Billings et al. (2017) demonstrated that ephemeral content platforms, such as Snapchat, encourage casual fandom due to the reduced perceived risk of posting reactions. In contrast, the permanence of Twitter posts encourages critical commentary that remains in public discourse. This distinction suggests that persistent negative tweets about driver performance or controversies may sustain negative perceptions over time.
Athlete engagement on social media also plays a role in shaping fan perceptions. Frederick et al. (2013) examined how athletes use Twitter to foster parasocial (one-way) and social (two-way) relationships with fans. Although their focus was on athlete-generated content rather than fan discourse, their findings imply that athletes who engage directly with fans may mitigate negative credibility impacts by appearing authentic and approachable.
Finally, Xu (2025) conducted a scoping review of athlete social media self-presentation (ASMSP), finding that while athletes use social media to challenge traditional media stereotypes and build personal brands, they still face gender and identity-based biases. In motorsport, where stereotypes about masculinity, aggression, and nationality persist, understanding how F1 drivers present themselves online could offer insight into whether they reinforce or challenge these narratives—and how that, in turn, affects credibility perceptions.
Collectively, these studies reveal that:
Negative commentary can undermine athlete credibility (Sanderson & Truax, 2014; Le Clue, 2025).
Fan narratives during controversies influence public trust in sports organizations (Le Clue, 2025).
Platform affordances impact the spread, framing, and permanence of negativity (Billings et al., 2017; Valdelomar Barquero, 2023).
Athlete self-presentation may counter stereotypes but remains shaped by systemic biases (Xu, 2025).
Social engagement strategies may help protect athlete credibility (Frederick et al., 2013).
These insights underscore that social media does not merely reflect public opinion—it actively constructs the credibility and legitimacy of athletes and sports organizations.
Research Questions
This study addresses the following questions:
How does negative commentary on social media influence Formula 1 driver credibility?
How do fan narratives during controversies affect public trust in motorsport organizations?
How does exposure to toxic fan discourse on social media influence neutral fans’ perceptions of a sport or athlete?
Does athlete social media self-presentation challenge or reinforce traditional media stereotypes in motorsports?
Methodology
This study employs a mixed-methods content analysis to examine how negative commentary on social media influences fans’ perceptions of Formula 1 driver credibility. Content analysis was selected because it systematically categorizes and quantifies textual data, allowing for the identification of patterns in user-generated online discourse. This method is particularly useful for analyzing how language, framing, and frequency of commentary contribute to shaping public narratives around athletes.
Past research supports this approach. Sanderson and Truax (2014) conducted a content analysis of hostile tweets directed at a college football player, demonstrating how social media discourse can quickly frame athletes either positively or negatively based on fan reactions. Their study showed the potential of social media to amplify emotional responses and influence public perception. Similarly, Billings et al. (2017) applied content analysis to investigate sports media framing across cultures, revealing how narratives around athletes—especially those involving gender or disability—are constructed through media discourse. These studies highlight content analysis as a proven method in sports communication research, making it an appropriate choice for this project focused on Formula 1.
A mixed-methods approach enhances the value of this research by combining quantitative analysis with qualitative thematic analysis. The quantitative component involves frequency counts of negative tweets and their subcategories, providing measurable insights into how often certain types of negativity appear. For example, tallying the number of tweets that label a driver “undeserving” or “talentless” helps identify the prevalence of credibility-related attacks in online discourse.
The qualitative component involves analyzing the language and narratives within these tweets, exploring how negativity is framed and what recurring themes emerge. This thematic analysis captures nuances that quantitative data alone cannot reveal, such as patterns in metaphor use or the ways fans construct legitimacy narratives. By blending these methods, the study offers a comprehensive understanding of both the scale and depth of negativity in Formula 1 fan commentary.
Data was collected using a Python-based scraping script designed to retrieve tweets containing relevant keywords, hashtags, and mentions of selected Formula 1 drivers. Automating the collection process through Python provided a consistent and efficient method for gathering publicly available tweets, reducing the potential for human bias during selection. The script targeted tweets containing driver names and common hashtags like #F1, #BritishGP, and other race-specific or driver-specific terms, ensuring relevance to the study’s focus.
Approximately 500 tweets were gathered from the 48-hour period following the 2025 British Grand Prix. This timeframe was chosen to capture peak emotional reactions from fans, a strategy informed by Sanderson and Truax’s (2014) findings that social media commentary is often most raw and authentic immediately after a sporting event. The dataset focused on three high-profile, often polarizing drivers—Lando Norris, Lewis Hamilton, and Max Verstappen—due to their consistent presence in fan discourse and media narratives. If significant controversies emerged during the race, tweets concerning other drivers were also included to capture relevant commentary.
The scraped tweets were compiled into a structured dataset for analysis. I served as the sole coder for this study, responsible for reviewing, categorizing, and analyzing all data. Each tweet was first coded as positive, neutral, or negative. Tweets identified as negative were further classified into five predefined subcategories based on their content:
Personal insults — Comments attacking a driver’s appearance, personality, or character traits (e.g., “He’s such a loser.”)
Performance critiques — Criticisms of a driver’s racing ability, decisions, or results (e.g., “Worst driver on the grid today.”)
Nationality-based slurs — Derogatory remarks based on nationality or ethnicity (e.g., “Stupid [nationality] can’t drive.”)
Threatening language — Direct or implied threats of harm or violence (e.g., “I hope he crashes and dies.”)
Credibility attacks — Statements questioning a driver’s legitimacy, qualifications, or right to compete in Formula 1 (e.g., “Only in F1 because of daddy’s money.”)
The quantitative analysis involved counting the frequency of each negative category and subcategory to determine patterns of negativity distribution across the sample. This frequency count offered insights into which forms of negative commentary were most common in the context of Formula 1 discourse.
The qualitative thematic analysis explored recurring language choices, metaphors, and narratives within each coded category. For instance, within the “credibility attacks” category, I examined how fans invoked accusations of nepotism, favoritism, or lack of talent. This analysis aimed to understand how specific language frames fan perceptions and contributes to broader conversations about athlete legitimacy.
Due to time constraints, I was unable to conduct a formal inter-coder reliability check or a secondary coding review. As the sole coder, I followed a detailed coding guide to promote internal consistency; however, the absence of a formal reliability assessment introduces a degree of subjectivity and potential coder bias.
This study acknowledges several limitations inherent in the methodology.
Sample bias is possible, as Twitter users represent only a segment of the Formula 1 fanbase, potentially skewing findings toward the views of more vocal online participants. Nonetheless, Twitter remains a dominant platform for real-time fan discourse, making it a relevant source for this analysis.
Language barriers limited the study to English-language tweets, excluding perspectives from non-English-speaking fans.
Subjectivity in coding is unavoidable in qualitative research, especially with a single-coder design.
Temporal limitations arise from analyzing only a short, post-event window, which may not capture longer-term discourse trends.
All tweets analyzed were publicly available, and no personally identifiable information was collected or reported. Data has been analyzed in aggregate form to protect individual privacy, in accordance with ethical research standards for studies involving publicly accessible online content.
This mixed-methods content analysis is an appropriate and effective approach for investigating the nature of negative social media discourse surrounding Formula 1 drivers. By combining quantitative measures of negativity with qualitative insights into framing and language use, this methodology offers a comprehensive lens through which to examine how online commentary may influence public perceptions of athlete credibility. The approach builds upon established sports communication research and addresses both the measurable trends and underlying narratives present in fan discourse.
Results
A total of 37 tweets were collected and analyzed for sentiment and categories of negativity. Of the tweets coded, 15 (40.5%) were categorized as positive, 11 (29.7%) as neutral, and 11 (29.7%) as negative. This relatively even distribution allowed for an unbiased look at how different sentiments circulate within Formula 1-related Twitter discourse. Among the tweets coded as negative, the most common forms of negativity were personal insults and performance critiques, each appearing in three tweets. For example, one tweet read, “Lewis got beaten by another German called Nico”—a comment that, while not overtly aggressive, was categorized as a personal insult based on its dismissive tone. Another tweet stated, “Norris didn’t win on merit,” which was categorized under performance critique for directly questioning a driver’s ability and legitimacy.
Credibility attacks appeared in two tweets. One such example stated, “The aura of a [Lando] [N]orris bottle is unmatched” followed by a dismissive comment undermining the credibility of the driver. Threatening language was rare, with only one tweet fitting this category, and nationality-based slurs were absent entirely. This suggests that, within this sample, Formula 1-related negativity often focuses on a driver’s performance or character rather than overt hostility or discrimination. These results align with prior research on online sports discourse, which suggests that athletes are frequently subject to performance-related criticism rather than explicit abuse (Sanderson & Truax, 2014).
Engagement metrics (likes, retweets, and replies) were examined to assess how tweets of different sentiment categories performed in terms of audience interaction. For example, tweets categorized as neutral or positive generally saw high levels of likes and retweets, though some negative tweets also received significant engagement. This pattern suggests that while negativity does not overwhelmingly dominate engagement, it still garners noticeable interaction, supporting the idea that negative commentary may attract attention, even if it is not the dominant narrative.
An unexpected factor that influenced the results was a technical limitation encountered during data collection. The original plan was to scrape tweets using a Python-coded script to ensure a broad, randomized sample of Twitter discourse. However, due to coding errors and scraping limitations, this method was unsuccessful. As a result, all tweets had to be manually collected. This manual collection process reduced the sample size from what was originally intended and limited the randomness of the dataset. However, it ensured that all tweets analyzed were directly relevant to the research questions. While this smaller sample size limits the generalizability of the findings, it provided a more focused look at the specific language and sentiment present in fan commentary during the study period.
These results partially support the research question, which aimed to explore whether negative commentary on social media affects perceptions of Formula 1 driver credibility and whether such commentary drives audience engagement. The data indicates that while negativity is present in online discussions, it often manifests in critiques of performance or personal remarks rather than in severe attacks. Furthermore, negative tweets did not consistently outperform positive or neutral tweets in terms of engagement, suggesting that negativity alone is not a guaranteed driver of interaction. This finding challenges common assumptions in social media studies that negativity always correlates with higher engagement.
Overall, the results suggest that Formula 1 fans engage with a variety of commentary types online, with a noticeable presence of critique-based negativity. These findings imply that fan discourse may influence perceptions of driver credibility primarily through repeated performance critiques and personal commentary, rather than through overt hostility. This nuanced understanding of online sports discourse adds to the existing literature by highlighting the complex ways in which fan narratives are constructed and engaged with on platforms like Twitter.
Discussion, Conclusion, and Limitations
The findings of this study support existing research suggesting that social media discourse significantly influences fan perceptions of athletes. As identified by Sanderson and Truax (2014), negative commentary can undermine athlete credibility, while Le Clue’s (2025) work highlights how fan-driven narratives can impact trust in sporting institutions. The analysis of tweets in this study revealed similar patterns, with personal insults, credibility attacks, and negative performance critiques emerging as the most common forms of commentary directed at high-profile Formula 1 drivers. These results reinforce the idea that Twitter is not only a space for fan engagement but also a powerful tool in shaping public narratives and athlete reputations.
Additionally, the analysis suggests that repeated exposure to certain types of negative commentary—especially personal attacks and credibility critiques—can influence how fans perceive a driver’s legitimacy within the sport. This supports findings by Billings et al. (2017) and Frederick et al. (2013) on the lasting impact of online discourse and the role of social media in constructing parasocial relationships.
This study also underscores the importance of Twitter’s platform dynamics. The permanence and visibility of tweets allow critical narratives to persist and amplify, aligning with Valdelomar Barquero’s (2023) research on platform affordances. This suggests that negative discourse on Twitter may have a lasting impact on fan perceptions.
However, this study faced several limitations. The coding of tweets was conducted solely by the researcher, limiting the ability to confirm consistency through intercoder reliability checks. Additionally, the data sample was constrained by time and accessibility to Twitter content, which may affect the representativeness of the findings within the broader Formula 1 fan community.
Future research could explore how exposure to different types of negative commentary shapes perceptions across various fan groups or examine how these patterns change during major controversies. Understanding the nuances of digital discourse remains critical in analyzing how social media influences athlete credibility in modern motorsports.
References
Billings, A. C., Qiao, F., Conlin, L., & Nie, T. (2017). Permanently desiring the temporary? Snapchat, social media, and the shifting motivations of sports fans. Communication & Sport, 5(1), 10–26. https://doi.org/10.1177/2167479515588760
Frederick, E. L., Lim, C. H., Clavio, G., Pedersen, P. M., & Burch, L. M. (2013). Choosing between the one-way or two-way street: An exploration of relationship promotion by professional athletes on Twitter. Communication & Sport. https://doi.org/10.1177/2167479512466387
Le Clue, N. (2025). Controversy, social media, and Formula One: Examining #VoidLap58. Transformative Works and Cultures, 45. https://doi.org/10.3983/twc.2025.2651
Sanderson, J., & Truax, C. (2014). “I hate you man!”: Exploring maladaptive parasocial interaction expressions to college athletes via Twitter. Journal of Issues in Intercollegiate Athletics, 7, 333–351. https://scholarcommons.sc.edu/jiia/vol7/iss1/1
Valdelomar Barquero, A. C. (2023). Digital grandstands: Understanding the impact of social media platforms in the dynamics of online brand communities of Formula One (Master’s thesis, Erasmus University Rotterdam). Erasmus Thesis Repository.
Xu, Q. (2025). Selfie scores: A scoping review of research on athlete social media self-presentation. Communication & Sport. Advance online publication. https://doi.org/10.1177/21674795251315832
Communication 297, Research Methods in Communication, Fernando Severino,
Illinois State University
Illinois State University
