Exploring newsjacking as social media–based ambush marketing
Bibliographic record
Abstract
Purpose This study explores the use of event-related promotional hashtags by non-sponsors as a form of social ambushing, akin to newsjacking, as potential means of ambushing major events and the potential challenges facing commercial rights holders. Design/methodology/approach Framed within the context of the 2018 PyeongChang Winter Olympic Games, the present research takes a descriptive analytical approach to social media analysis. Social media data were accessed from Twitter's API across a six-week Games period and subsequently coded and categorized based upon strategic intent, content and key structural characteristics. A quantitative analysis of Tweet distribution, frequency and buzz was then conducted, providing insight into the impacts and effects of social ambushing via newsjacking. Findings Importantly, the study's findings suggest that whilst newsjacking by non-sponsors throughout the Games was pervasive, the potential reach and impact of such social ambushing may be limited. Non-sponsoring firms primarily adopted Games hashtags for behavioural or diversionary means, however consumer response to such attempts was minimal. These findings offer renewed perspective for scholars and practitioners on social ambushing and ambush marketing interventionism. Originality/value This research provides an important investigation into the manifestations and potential implications of social ambushing and illustrates the potential for brands to newsjack sporting events through unauthorized hashtag usage, necessary advances in sport marketing research.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".