Being a Likable Braggart: How Consumers Use Brand Mentions for Self-presentation on Social Media
Bibliographic record
Abstract
Consumer self-presentation is considered a major driver of word-of-mouth (WOM) communication. In particular, the manner in which consumers self-present using brand mentions is likely to impact impressions of the WOM senders as well as the mentioned brands. In some cases, however, mentioning reputable brands in a WOM message can be considered bragging, which can lead to negative impressions of the communicator. In this chapter, we use Twitter data to develop a typology of different strategies consumers use to mention brands while crafting positive self-presentations on social media. Our findings suggest that consumers try to avoid negative evaluations while bragging via brand mentions by (1) mentioning brands in the context of sharing on social media what one is doing, feeling or thinking at the moment, (2) shifting the focus of communication away from the self, and/or (3) downplaying one’s own or the brand’s positive characteristics. These brand mentioning strategies map onto some common tactics used by marketers to encourage consumers to talk about brands on social media. Our typology of brand mentioning strategies is a first step towards examining the downstream consequences of these strategies for the communicator and the mentioned brand. Moreover, our typology can help in developing a theory to guide practitioners in selecting tactics to encourage brand mentions that will benefit the brand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".