Combating online misinformation by detecting organized groups on social media
Bibliographic record
Abstract
Coordinated misinformation campaign -the malicious and coordinated use of online social media for manipulation has become a pressing global problem.It aims to distort information space to confuse and distract the public, disseminate propaganda and disinformation to foster divisions, and paralyze the decision making abilities of individuals.The ultimate goals or motives of such coordinated misinformation campaigns might be hard to interpret, but their negative influence on public opinion, democracy and elections is significant.We propose algorithmic solutions that aim to detect coordinated misinformation campaign on social media, and conduct case studies on real-world Twitter data.Specifically, we propose the Embed-Cluster-Rank framework, a three-stage algorithmic pipeline that learns low-dimensional representations for each user on a social network, clusters these representations into user clusters, and finally ranks these clusters in terms of the suspiciousness of engaging in coordinated information campaigns.We then propose three instantiations of the Embed-Cluster-Rank framework based on different embedding components -joint autoencoder, linear projection and aggregation, and tensor decomposition.We report experimental results on synthetic data, real-world Twitter data related to the 2019 Canadian Federal Election, and case studies that reveal interesting and important findings on the information landscape as well as suspicious user groups impacting the political dialog.i
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".