Twitter Bot Detection Using Neural Networks and Linguistic Embeddings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Twitter is a web application playing the dual role of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. In this article, we propose a Twitter bot detection model using recurrent neural networks, specifically bidirectional lightweight gated recurrent unit (BiLGRU), and linguistic embeddings. To the best of our knowledge, our Twitter bot detection model is the first that does not require any handcrafted features, or prior knowledge or assumptions about account profiles, friendship networks or historical behavior. The proposed model uses only textual content of tweets and linguistic embeddings to classify bot and human accounts on Twitter. Experimental results show that the proposed model performs better or comparably to state-of-the-art Twitter bot detection models while requiring no feature engineering, making it faster and easier to train and deploy in a real network. We also present experimental results that show the performance and computational costs of different types of linguistic embeddings and recurrence network variants for the task of Twitter bot detection. The results will potentially help researchers design high-performance deep-learning models for similar tasks.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it