Making fans go "GAGA": intimacy, identity and brand building through Twitter
Bibliographic record
Abstract
While the use of digital platforms has harmed some aspects of the music industry, it has also created new opportunities for artists to prosper in terms of marketing, branding, communication and to ultimately become cultural entrepreneurs. Twitter in particular is a social networking service (SNS) that is ideal for studying fan celebrity interactions, as it is a safe space for celebrities to market, brand and communicate with fans through strategic discourse in an informal environment. With SNS such as Twitter, fans now have the opportunity to build seemingly intimate relationships with celebrities, becoming cocreators of content. Widely acknowledged for her unparalleled relationships and interactions with fans on Twitter, singer Lady Gaga, has achieved success and long term viability amidst digitalization through her strategic use of online platforms to not only promote and strengthen her brand, but to build intimate relationships with her fans. Lady Gaga exemplifies how the digitalization of the music industry and growth of online platforms drive artists to take on new roles as entrepreneurs marketers and as conversationalists with fans. As such, this paper presents a review of the extant literature of studies that have explored the connection between celebrity brand building and representation on social media and how fan identification, interaction and intimacy are forged via social media discourse. This is followed by a qualitative discourse analysis of 904 tweets and retweets collected from Lady Gaga’s Twitter account from May 31st, 2015 until April 31st, 2016. This analysis serves to develop an understanding of how language is being used actively, persuasively and to isolate patterns in Lady Gaga’s Twitter discourse. This includes key terms, phrases and overall frequency of these. In analyzing how Lady Gaga uses strategic discourse on Twitter, we can build a greater understanding of how this social medium is used to build intimacy with fans. From this knowledge, both artists and marketers can emulate this model for effective brand management on social media, particularly on Twitter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".