How Athletes Use Twitter

Data Organization in NVivo 10

• Unstructured Data Management: Users can import diverse data formats (documents, PDFs, spreadsheets) to categorize and analyze content (QSR International, 2012).

• Sources & Coding: Keywords/descriptors function as sub-folders in NVivo 10, organizing raw content (e.g., tweets).

• Cluster Analysis: Conducted using Pearson correlation for evaluating word similarity among raw tweet content across keywords and descriptors.

Results

Research Question 1: Keywords & Descriptors Used by Athletes

• Descriptive Coding Method: Analyzed tweets to reduce content into representative keywords and descriptors (Saldana, 2009).

• Identification Process: Raw tweet content organized into a Word document with three columns leading to 50 identified keywords and descriptors summarized.

• Nature of Keywords: Keywords encapsulate thematic areas reflective of repetitive or similar content within athlete tweets.

• Final Keyword Reduction: Utilized Creswell's (2003) constant comparison method to consolidate related keywords.

Table 1: Keywords and Descriptors in Athlete Tweets

• Categories: Grouped based on thematic relevance:

  • Weather, Chapel, Traveling, Prayers, Plans, Winning, Food, Competition, General comments/statements, Games, Family, Friends, Statistics, Running, Fun, Weights, Fashion, Humor, Pop culture, Celebration, Fan/cheering, Behavior, Health, Philosophy, Observations, Holiday well wishes, Greetings, Congratulations, Charity (children), Thanks, God, Mass, Service, Gym, Hard work, Effort, Appearances, Charity events, Personal charities, Giveaways, Nike, adidas, Shoes, Workout/performance apparel, Free products, ESPN, Interview, Social media, Facebook, Twitter.

Research Question 2: Frames Used by Athletes

Thematic Analysis and Focused Coding

• Frame Development: Axial coding revealed themes categorized into four main frames, enhancing understanding of athletes' tweets.

• Identification of Themes: Eleven overarching themes emerged through focused coding:

  • Daily Life, Personal Life, Outlook, Well Wishes, Gratitude, Religion/Faith, Performance, Training, Promotional Efforts/Events, Products, and Media.

Table 2: Focused Coding Developed Themes in Athlete Tweets

• Detailed Breakdown of Themes:

  • Daily Life: Weather, Traveling, Plans, Food, General comments/statements.

  • Personal Life: Family, Friends, Fun, Fashion, Humor, Pop culture, Celebration, Fan/cheering, Behavior, Health.

  • Outlook: Philosophy, Observations.

  • Well Wishes: Holiday well wishes, Greetings, Congratulations.

  • Gratitude: Charity (children), Thanks.

  • Religion/Faith: God, Mass, Service, Chapel, Prayers.

  • Performance: Winning, Competition, Games, Statistics.

  • Training: Running, Weights, Gym, Hard Work, Effort.

  • Promotional Efforts/Events: Appearances, Charity Events, Personal Charities, Giveaways.

  • Products: Nike, adidas, Shoes, Workout/Performance Apparel, Free products.

  • Media: ESPN, Interviews, Social Media, Facebook, Twitter.

Frame Analysis of Athlete Tweets

Emerging Frames

• First Frame: Athlete as Everyday Individual

  • Themes: Daily Life, Personal Life, Outlook - reflects normalcy and relatability.

  • Keywords: Encapsulates insights like weather, food, travel, family discussions, and casual life events.

• Second Frame: Athlete as Thankful and Grateful Individual

  • Themes: Well Wishes, Gratitude, Religion/Faith - demonstrates humility and recognition of support.

  • Keywords: Include messages of well wishes, religious affirmations, and thanks to followers.

• Third Frame: Athlete as Competitor

  • Themes: Performance, Training - signifies dedication and a competitive nature.

  • Keywords: Focused on competition, hard work, and achievements in sports.

• Fourth Frame: Athlete as Promotional Figure

  • Themes: Promotional Efforts/Events, Products, Media - highlights marketing aspects of athlete presence.

  • Keywords: Involves endorsements, promotional events, and media engagements.

Conclusion from the Research

• Comparison of Frames: Each frame underscores different aspects of athlete personas, simultaneously reinforcing and countering traditional media portrayals.

• Theoretical Implications: The findings suggest potential frameworks for understanding athlete portrayal across different media, emphasizing the role of social media in shaping perceptions.

• Future Research Directions: Investigations into gender differences in keyword usage and schema impact on athlete presentation on Twitter along with further studies on frame utilization and self-presentation strategies in athletes.

Data Organization in NVivo 10

NVivo 10 is a powerful tool for unstructured data management, allowing users to import a variety of data formats, such as documents, PDFs, and spreadsheets. This feature enables the categorization and analysis of diverse content, enhancing the research process. Users can effectively organize their raw content, for instance, tweets, by employing keywords or descriptors that act as sub-folders within the software. This structured approach facilitates a more systematic examination of data, making it easier for researchers to identify patterns and insights.

One of the notable methodologies in analyzing the data collected via NVivo is cluster analysis. This is done using Pearson correlation, which evaluates word similarity among the raw tweet content across the designated keywords and descriptors. Such analytical methods aid researchers in understanding the nuances of the data and allow for a deeper investigation into the trends that emerge from athlete communications on social platforms.

Research Findings

The first research question focused on identifying the keywords and descriptors used by athletes in their tweets. Implementing a descriptive coding method, tweets were analyzed and distilled into representative keywords and descriptors. This process, guided by Saldana's coding principles, involved organizing raw tweet content into a Word document with three columns highlighting the keywords identified. The outcome of this identification process was a comprehensive set of 50 keywords and descriptors that encapsulate thematic areas indicative of repetitive or similar content present within the athletes' tweets. A significant aspect of this research involved the final keyword reduction, utilizing Creswell's constant comparison method to consolidate related keywords into organized categories. These categories were broad, covering a range from Weather, Traveling, and Plans to more specific terms like Charity Events and Nike, providing a holistic view of athlete communications.

The second research question sought to explore the types of frames used by athletes in their interactions. Through thematic analysis and focused coding, axial coding revealed that the tweets could be categorized into four primary themes which elucidate the understanding of the athletes’ communications. Eleven overarching themes emerged from this focused coding, including Daily Life, Personal Life, Outlook, Well Wishes, and other significant aspects related to athlete experiences. Each category encapsulated distinct yet interrelated aspects of athlete identity and outreach, from personal reflections to performance-related content.

In analyzing the frames identified in athlete tweets, four main frames emerged. The first frame portrays the athlete as an everyday individual, encompassing themes such as Daily Life and Personal Life, demonstrating a relatability through personal narratives. The second frame highlights the athlete's roles as thankful and grateful individuals, which reflects humility through expressions of gratitude. The third frame focuses on the athlete as a competitor, emphasizing themes that revolve around performance and training, showcasing dedication to their craft. Lastly, the fourth frame presents athletes as promotional figures, underlining the marketing elements associated with their public personas through promotional efforts and media engagements.

Ultimately, the research concludes with a comparison of the different frames and their implications for understanding athlete personas. The findings suggest that these various frames reinforce and sometimes counter the traditional portrayals of athletes in media. This has significant theoretical implications, indicating a need for frameworks that consider how athletes are represented across different platforms, particularly social media, which plays a pivotal role in shaping public perceptions of these individuals. Future research directions include examining gender differences in keyword usage, analyzing how schema impacts athlete presentation on Twitter, and exploring self-presentation strategies employed by athletes in their digital communications.