Communication Via Self-disclosure Behavior of Micro-influencers on Social Media in Thailand
Bibliographic record
Abstract
Technology and human way of life have changed through eras and time, just like business operations that require marketing in order to develop to have their spaces in the consumers’ minds. It can be seen that with the time passes, marketing communication tools also change. Currently, it is unavoidable to rely on internet technology to help in the marketing process through the use marketing communication tools called “Marketing Communication”, done through social media. The website has created a model of society, allowing consumers to search for information on their own based on the direct experiences of those who have used that products, influential people in ideas or influencers, which play a huge role in the distribution of news and information. Therefore, various agencies/ organizations are persuading these influencers to participate in activities, organized to help. The influencer will publish information and create word-of-mouth online. As the importance of the behavior of the group of people that are classified as micro-influencers have increased rapidly, information dissemination through micro-influencers has become an important tool in communication that marketers rely on by using consumers to communicate with consumers. Therefore, there are studies of forms of identity disclosure, level of disclosure, social capital, and social support of self-disclosure behavior of individuals, who are micro-influencer. It is beneficial to entrepreneurs, including marketers to study about aforementioned topics in order to plan communication to consumers by choosing to use consumers as messengers in order to make that communication most effective. The research was conducted in-depth interviews with 30 micro-influencers on social media via Facebook, between 24-38 years old, with 500 - 10,000 followers. The research found that Social support is the main reason that micro-influencers have revealed themselves on social media via Facebook. Meanwhile, it is also a way to learn about self-disclosure forms on social networks of influential people at the micro level or micro-influencers on order to be used as a tool for marketing communication in the current marketing world, especially the form of marketing communication in Thailand through social media.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".