Identifying High Value Users in Twitter Based on Text Mining Approaches
Bibliographic record
Abstract
Finding out new potential users for specific products are always the needs of the marketing department in industries. While Traditional ways like RFM model perform poorly in exploring new users. While the popularity of social media like Twitter and Facebook provides advertisers a new way to find, understand and target their users. In this paper, we propose a new method to find out and rank high-value target audience for a specific brand by utilizing machine learning and text mining approach. Overall tweets from 10 accounts in Twitter are collected to build the target and non-target dataset. In order to solve the data imbalance problem, five data resampling methods are assessed. Ensemble learning approaches include Bagging and Boosting algorithm are used to build the classifier. The results show that SMOTE outperforms other resampling method and AdaBoosting algorithm outperform other single classifier and Bagging model. We also find out the existence of marking accounts exists so that a threshold is set to filter these accounts which are not real users. We believe that our approach could be used in industry for identifying high-value users for online marketing purpose.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".