Predicting Twitter Engagement With Deep Language Models
Bibliographic record
Abstract
Twitter has become one of the main information sharing platforms for millions of users world-wide. Numerous tweets are created daily, many with highly time sensitive content such as breaking news, new multimedia content or personal updates. Consequently, accurately recommending relevant tweets to users in a timely manner is a highly important and challenging problem. The 2020 ACM RecSys Challenge is aimed at benchmarking leading recommendation models for this task. The challenge is based on a large and recent dataset of over 200M tweet engagements released by Twitter with content in over 50 languages. In this work we present our approach where we leverage recent advances in deep language modeling and attention architectures, to combine information from extracted features, user engagement history and target tweet content. We first fine-tune leading multilingual language models M-BERT and XLM-R for Twitter data. Embeddings from these models are used to extract tweet and user history representations. We then combine all components together and jointly train them to maximize engagement prediction accuracy. Our approach achieves highly competitive performance placing 2’nd on the final private leaderboard. Full code is available here: https://github.com/layer6ai-labs/RecSys2020.
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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 | 0.000 |
| 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".