Unsupervised Word Embedding with Ensemble Deep Learning for Twitter Rumor Identification
Bibliographic record
Abstract
In social networks, rumor identification is a major problem. The structural data in a topic is applied to derive useful attributes for rumor identification. Most standard rumor identification methods concentrate on local structural attributes, ignoring the global structural attributes that exist between the source tweet and its responses. To tackle this issue, a Source-Replies relation Graph (SR-graph) has been built to develop an Ensemble Graph Convolutional neural Net (EGCN) with a Nodes Proportion Allocation Mechanism (NPAM) which identifies the rumor. But, the word vectors were trained by the standard word-embedding model which does not increase the accuracy for large Twitter databases. To solve this problem, an unsupervised word-embedding method is needed for large Twitter corpora. As a result, the Twitter word-embedded EGCN (T-EGCN) model is proposed in this article, which uses unsupervised learning-based word embedding to find rumors in huge Twitter databases. Initially, the latent contextual semantic correlation and co-occurrence statistical attributes among words in tweets are extracted. Then, to create a rumor attribute vector of tweets, these word embeddings are concatenated with the GloVe model's word attribute vectors, Twitter-specific attributes, and n-gram attributes. Further, the EGCN is trained by using this attribute vector to identify rumors in a huge Twitter database. Finally, the testing results exhibit that the T-EGCN achieves 87.56% accuracy, whereas the RNN, GCN, PGNN, EGCN, and BiLSTM-CNN attain 65.38%, 68.41%, 75.04%, 81.87%, and 86.12%, respectively for rumor identification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".