Extracting, Mining and Predicting Users’ Interests from Social Media
Bibliographic record
Abstract
Mining user interests from user behavioral data is critical for many applications. Based on user interests, service providers like advertisers can significantly reduce service delivery costs by offering the most relevant products to their customers. The challenge of accurately and efficiently identifying user interests has been the subject of increasing attention for several years. With the emergence and growing popularity of social media, many users are extensively engaged in social media applications to express their feelings and views about a wide variety of social events/topics as they happen in real time. The abundance of user generated content on social media provides the opportunity to build models that are able to accurately and effectively extract, mine, and predict users’ interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services, and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users’ interests and preferences. In this monograph, the authors cover five important subjects related to the mining of user interests from social media: (1) the foundations of social user interest modeling, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions, and opportunities for further work. The monograph is a valuable resource for those who have familiarity with social media mining and the basics of information retrieval (IR) techniques.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".