Instagram users’ meaning construction through micro-influencer-generated content
Bibliographic record
Abstract
In 2019, Instagram is the fastest-growing social platform with over a billion monthly users sharing more than 95 million images and videos daily. Social media influencers have become a new, effective way of reaching the right target audience and building relationships between brands and consumers. \n \nThe purpose of this study is to understand how meanings are constructed by the followers of Instagram micro-influencers. Furthermore, this study exposes the factors that explain micro-influencer following. \n \nThe theoretical framework consists of the uses and gratifications theory, meaning construction, semiotics and influence theories. Based on these theories, the theoretical framework focuses on an individual’s meaning construction, which is influenced by memories, values, culture, language, beliefs, motivations, social relations and media usage. The meanings in an image are a negotiation between the producer and the viewer, and reflect individual values, attitudes and political, social and cultural beliefs. Meanings are therefore produced between the micro-influencers’ content and the interpretation of the follower. \n \nThe research questions were answered through insight provided by ten in-depth ZMET interviews that were conducted in Finland and in Canada. Four emerging thematic meaning constructs and factors, four core meanings and multiple subthemes were found that explain Instagram’s micro-influencer following and relationship formation. \n \nIt was found that a sense of similarity, shared meanings and personality presented through visual content by the micro-influencer has a substantial impact on micro-influencer following. In addition, the results confirmed that people seek motivation, inspiration and confirmation of their own beliefs, situations and experiences in life from the micro-influencer and build a sense of belonginess and themselves. Based on the results of this research, a model of the different levels of relationship formation was created. The study gives insight into the various aspects of influencer marketing that need to be taken into consideration by practitioners and gives guidance on how to create compelling influencer marketing strategies and campaigns through meaningful content in order to form valuable relationships with consumers.
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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 | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".