Detecting Bots in Social-Networks Using Node and Structural Embeddings
Bibliographic record
Abstract
Abstract Users on social networks such as Twitter interact with and are influenced by each other without much knowledge of the identity behind each user. This anonymity has created a perfect environment for bot and hostile accounts to influence the network by mimicking real-user behaviour. To combat this, research into designing algorithms and datasets for identifying bot users has gained significant attention. In this work, we highlight various techniques for classifying bots, focusing on the use of node and structural embedding algorithms. We show that embeddings can be used as unsupervised techniques for building features with predictive power for identifying bots. By comparing features extracted from embeddings to other techniques such as NLP, user profile and node-features, we demonstrate that embeddings can be used as unique source of predictive information. Finally, we study the stability of features extracted using embeddings for tasks such as bot classification by artificially introducing noise in the network. Degradation of classification accuracy is comparable to models trained on carefully designed node features, hinting at the stability of embeddings.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.003 |
| 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".