Social Media Extremism Detection

This repository contains a reproducible NLP research project for binary classification of social-media text as extremist or non-extremist. It includes a curated dataset, fixed train/validation/test splits, classical machine-learning baselines, embedding-based experiments, a transformer fine-tuning experiment, standardized result summaries, and interpretability-oriented analysis artifacts.

The repository is public so that the research process can be reviewed, replicated, and evaluated. It is not intended to be a production moderation system, a general-purpose software package, or a community-maintained open-source project.

Responsible-use note: The models in this repository should not be used to make automated decisions about people, accounts, posts, or communities. Extremism detection is a high-stakes task with serious false-positive and false-negative risks. Any applied use would require domain-expert review, bias evaluation, privacy review, and human oversight.

Project status

This is an active research repository maintained by the project authors. The current version contains the dataset foundation, fixed split assignments, a verification toolkit, and eight model families whose results are registered in the controlled comparison:

• Logistic Regression with word-level TF-IDF features.
• Calibrated Linear SVM with word-level TF-IDF features.
• Single-Layer Perceptron with word-level TF-IDF features.
• Calibrated Linear SVM with character-level TF-IDF features.
• Calibrated Linear SVM with combined word + character TF-IDF features.
• Logistic Regression with FastText document embeddings.
• Twitter-RoBERTa transformer fine-tuning.
• Heterogeneous multi-checkpoint logit-pooled transformer ensemble.

Three further transformer-ensemble notebooks (08, 09, 10) exist but are not part of the controlled comparison. Two ran and reported test numbers without leaving a derivable result folder; one was never run. The section Transformer ensemble work in progress records where each one stands and why its numbers appear in no results table.

The main research finding so far is that the classical TF-IDF and static-embedding approaches cluster around a similar performance range, while contextual transformer models provide the strongest registered held-out test results — the single fine-tuned Twitter-RoBERTa run, and above it a logit-pooled ensemble of heterogeneous checkpoints. This supports the hypothesis that extremist-text classification benefits from context-aware representations that preserve word order, stance, negation, and social-media phrasing.

A second finding is methodological, and it constrains how the first one may be stated: the test split is 450 rows, so detecting a difference at McNemar exact significance requires roughly +3 accuracy points, about 14 rows. The transformer family's margin over the classical baselines clears that floor comfortably. The gaps among the classical baselines do not come close, and the gaps among the transformer variants tried so far sit at or below it — and no amount of decimal places changes that. INCONCLUSIVE is therefore a first-class, publishable outcome here, and a higher accuracy number is not by itself evidence of a better model.

Research motivation

Most online safety NLP work focuses on toxicity, hate speech, or general harmful-content classification. Violent extremist text can overlap with those categories, but it is not identical to them. It may involve ideological framing, support for violence, coded language, or contextual references that are not captured well by surface-level offensiveness alone.

This project treats extremist-content detection as a distinct text-classification problem. The research goals are to:

1. Build a transparent baseline suite for extremist versus non-extremist classification.
2. Use fixed splits and standardized metrics so model comparisons are meaningful.
3. Compare classical machine-learning, neural, embedding-based, and transformer-informed approaches under the same evaluation protocol.
4. Use interpretable feature-attribution methods to inspect what models learn and where they fail.
5. Document the dataset, methodology, and limitations clearly enough for replication.

Project links

• GitHub repository: https://github.com/73anthonyL/extremism_sentiment_analysis
• Kaggle dataset: https://www.kaggle.com/datasets/adityasureshgithub/digital-extremism-detection-curated-dataset
• Kaggle competition: https://www.kaggle.com/competitions/social-media-extremism-detection-challenge

Kaggle competition

The Social Media Extremism Detection Challenge on Kaggle is treated as an external research reference for the same binary classification task. Competition results should not be mixed directly with the controlled results in this repository unless they are rerun under the same fixed split and metric protocol.

Repository structure

extremism_sentiment_analysis/
├── data/
│   ├── dataset.csv
│   └── extremism_lexicon.txt
├── notebooks/
│   ├── 00_create_dataset_and_splits.ipynb
│   ├── 01_LOG-REG_TF-IDF.ipynb
│   ├── 02_LIN-SVM_TF-IDF.ipynb
│   ├── 03_SLP_TF-IDF.ipynb
│   ├── 04_CHAR-TF-IDF_LIN-SVM.ipynb
│   ├── 05_WORD-CHAR-TF-IDF_LIN-SVM.ipynb
│   ├── 06_FASTTEXT-EMB_LOG-REG.ipynb
│   ├── 07_TWITTER-ROBERTA_FINE-TUNE.ipynb
│   ├── 08_BEST-ROBERTA_SEED-ENSEMBLE.ipynb          # ran; unregistered
│   ├── 09_MULTI-CHECKPOINT_LOGIT-STACK.ipynb        # built; superseded by 11
│   ├── 10_TWITTER-ROBERTA_LOGIT-POOL-STABLE.ipynb   # ran; unregistered
│   └── 11_MULTI-CHECKPOINT_LOGIT-POOL.ipynb         # ran; result registered
├── results_summary/
│   ├── foundation/
│   ├── 01_LOG-REG_TF-IDF/
│   ├── 02_LIN-SVM_TF-IDF/
│   ├── 03_SLP_TF-IDF/
│   ├── 04_CHAR-TF-IDF_LIN-SVM/
│   ├── 05_WORD-CHAR-TF-IDF_LIN-SVM/
│   ├── 06_FASTTEXT-EMB_LOG-REG/
│   ├── 07_TWITTER-ROBERTA_FINE-TUNE/
│   └── 11_MULTI-CHECKPOINT_LOGIT-POOL/
├── research_loop/
│   ├── STATE.md                  # current cycle, stage, and blockers
│   ├── registry.json             # current champion
│   ├── prereg/                   # per-cycle preregistrations
│   ├── probs/                    # sanitized probability artifacts
│   ├── decisions/                # adjudicated cycle verdicts
│   ├── cycles/                   # per-cycle working notes
│   ├── val_log.jsonl             # declared validation looks
│   └── test_ledger.jsonl         # hash-chained test-unlock record
├── tools/
│   ├── eval_from_probs.py        # derives a result folder from probabilities
│   ├── compare_techniques.py     # the only thing that issues a verdict
│   ├── render_tables.py          # regenerates every doc results table
│   ├── protocol_check.py         # invariant checks
│   ├── validate_results_folder.py
│   ├── scan_text_leakage.py
│   ├── ledger.py
│   ├── repair_split_mirror.py
│   └── tests/                    # the toolkit's own pytest suite
├── splits/
│   ├── split_assignments.csv
│   └── split_assignments.PRE-REPAIR.csv
├── docs/
│   ├── README.md
│   ├── DATA_CARD.md
│   ├── EXPERIMENTS.md
│   ├── MODEL_CARD.md
│   ├── RESEARCH_LOOP.md
│   ├── RESPONSIBLE_USE.md
│   ├── RESULTS_SCHEMA.md
│   ├── REPLICATION_GUIDE.md
│   ├── COMPETITION.md
│   └── RELEASE_CHECKLIST.md
├── CHANGELOG.md
├── CITATION.cff
├── LICENSE
├── README.md
├── requirements.txt
├── requirements-lock.txt
└── requirements-dev.txt

The docs/ files support research transparency and replication:

File	Purpose
docs/DATA_CARD.md	Dataset construction, labels, intended use, and caveats.
docs/EXPERIMENTS.md	Standard experiment protocol and comparison rules.
docs/MODEL_CARD.md	Model families, metrics, risks, and evaluation notes.
docs/RESEARCH_LOOP.md	How a candidate technique moves from hypothesis to registered result, and what research_loop/ stores.
docs/RESPONSIBLE_USE.md	Safety, misuse, and deployment limitations.
docs/RESULTS_SCHEMA.md	Expected result files and metric fields for each experiment folder.
docs/REPLICATION_GUIDE.md	Step-by-step workflow for reproducing the experiments.
docs/COMPETITION.md	Kaggle competition context and how competition results relate to this repository.
docs/RELEASE_CHECKLIST.md	Pre-release checklist before public result updates or manuscript-aligned releases.

The verification layer

Results in this repository are derived and checked by tooling rather than transcribed by hand. Two rules follow from that, and they are enforced:

• Results are derived, not transcribed. tools/eval_from_probs.py builds a whole results_summary/<TECHNIQUE>/ folder from a committed probability artifact. No number should be hand-copied out of a notebook into a JSON file.
• Doc tables are rendered, not edited. tools/render_tables.py --write regenerates every results table in this README and in docs/ from results_summary/. The tables live inside paired HTML-comment markers and are rewritten wholesale; hand edits inside a region are clobbered. The marker syntax is documented in the tool's own docstring — it deliberately is not reproduced in the documents it scans.

python3 tools/protocol_check.py --all           # protocol invariants
python3 tools/validate_results_folder.py --all  # schema + internal consistency
python3 tools/render_tables.py --check          # documentation drift
python3 tools/scan_text_leakage.py              # dataset text in committed files
python3 tools/ledger.py verify                  # test-evaluation chain
python3 -m pytest tools/tests/ -q               # the toolkit's own tests

protocol_check.py and scan_text_leakage.py currently report pre-existing failures on notebooks 00–08 and 10: those notebooks carry saved cell outputs containing dataset text, and most declare an abbreviated split_version string. This is a known cleanup backlog, tracked in research_loop/STATE.md, not a defect in the checks.

Dataset and fixed splits

The processed dataset is created from data/dataset.csv using notebooks/00_create_dataset_and_splits.ipynb.

Item	Value
Raw rows	3000
Processed rows	2999
Rows removed	1
Duplicate text hashes	0
Non-extremist rows	1870
Extremist rows	1129
Non-extremist proportion	62.35%
Extremist proportion	37.65%
Random seed	30
Split version	split_v1_stratified_70_15_15_seed30

The dataset uses fixed stratified train/validation/test splits:

Split	Rows	Non-extremist	Extremist
Train	2099	1309	790
Validation	450	281	169
Test	450	280	170

All model notebooks should reuse splits/split_assignments.csv. Do not regenerate splits unless the dataset is intentionally revised and a new split version is created.

Current controlled results

The table below reports held-out test metrics from results_summary/. The positive class is EXTREMIST.

It is rendered from the committed result artifacts by tools/render_tables.py. A technique appears here only once it has a complete, schema-valid result folder derived from a committed probability artifact — which is why notebooks 08, 09, and 10 are absent.

Technique	Validation accuracy	Test accuracy	Test balanced accuracy	Test macro F1	Test ROC-AUC
01_LOG-REG_TF-IDF	0.8244	0.8533	0.8405	0.8429	0.9111
02_LIN-SVM_TF-IDF	0.8267	0.8556	0.8342	0.8422	0.9037
03_SLP_TF-IDF	0.8089	0.8378	0.8200	0.8247	0.9022
04_CHAR-TF-IDF_LIN-SVM	0.8022	0.8333	0.8303	0.8252	0.9028
05_WORD-CHAR-TF-IDF_LIN-SVM	0.8156	0.8356	0.8309	0.8270	0.9032
06_FASTTEXT-EMB_LOG-REG	0.7889	0.8178	0.8178	0.8102	0.9033
07_TWITTER-ROBERTA_FINE-TUNE	0.8578	0.8889	0.8853	0.8826	0.9496
11_MULTI-CHECKPOINT_LOGIT-POOL	0.8733	0.9089	0.8956	0.9015	0.9682

Rendered by tools/render_tables.py from results_summary/ — do not edit by hand.

Confusion-matrix summary:

Technique	TN	FP	FN	TP	FPR	FNR
01_LOG-REG_TF-IDF	250	30	36	134	0.1071	0.2118
02_LIN-SVM_TF-IDF	258	22	43	127	0.0786	0.2529
03_SLP_TF-IDF	250	30	43	127	0.1071	0.2529
04_CHAR-TF-IDF_LIN-SVM	236	44	31	139	0.1571	0.1824
05_WORD-CHAR-TF-IDF_LIN-SVM	238	42	32	138	0.1500	0.1882
06_FASTTEXT-EMB_LOG-REG	229	51	31	139	0.1821	0.1824
07_TWITTER-ROBERTA_FINE-TUNE	252	28	22	148	0.1000	0.1294
11_MULTI-CHECKPOINT_LOGIT-POOL	266	14	27	143	0.0500	0.1588

Rendered by tools/render_tables.py from results_summary/ — do not edit by hand.

Current ranking by held-out test PR-AUC:

1. 11_MULTI-CHECKPOINT_LOGIT-POOL — PR-AUC 0.9553, ROC-AUC 0.9682, positive F1 0.8746.
2. 07_TWITTER-ROBERTA_FINE-TUNE — PR-AUC 0.9233, ROC-AUC 0.9496, positive F1 0.8555.
3. 01_LOG-REG_TF-IDF — PR-AUC 0.8881, ROC-AUC 0.9111, positive F1 0.8024.
4. 02_LIN-SVM_TF-IDF — PR-AUC 0.8825, ROC-AUC 0.9037, positive F1 0.7962.
5. 05_WORD-CHAR-TF-IDF_LIN-SVM — PR-AUC 0.8818, ROC-AUC 0.9032, positive F1 0.7886.
6. 03_SLP_TF-IDF — PR-AUC 0.8777, ROC-AUC 0.9022, positive F1 0.7768.
7. 06_FASTTEXT-EMB_LOG-REG — PR-AUC 0.8763, ROC-AUC 0.9033, positive F1 0.7722.
8. 04_CHAR-TF-IDF_LIN-SVM — PR-AUC 0.8745, ROC-AUC 0.9028, positive F1 0.7875.

A ranking is not a significance test. 11_MULTI-CHECKPOINT_LOGIT-POOL leads on every column, but no verdict has been issued for it — see below.

These are baseline research metrics. They should not be interpreted as deployment readiness.

Transformer ensemble work in progress

Notebooks 08–11 explore whether ensembling contextual transformers improves on the single fine-tuned Twitter-RoBERTa run. Only 11 has a registered result; the other three do not appear in the tables above.

Notebook	State	Status
08_BEST-ROBERTA_SEED-ENSEMBLE	ran on Kaggle	Not registered. No result folder and no probability export; its test metrics exist only in saved cell outputs. Its own reported test accuracy (0.8778) is below the champion, so the BEST in its filename is a claim its numbers contradict — the file should be renamed 08_TWITTER-ROBERTA_SEED-ENSEMBLE.ipynb.
09_MULTI-CHECKPOINT_LOGIT-STACK	built, never run	Superseded by notebook 11, which keeps the multi-checkpoint idea but replaces the learned logit stacker with notebook 10's equal-weight mean-log-odds pool.
10_TWITTER-ROBERTA_LOGIT-POOL-STABLE	ran on Kaggle	Not registered. Evaluated the test split — so it spent a test unlock — but has no preregistration, no ledger entry, and no probability-export cell, so its folder cannot be derived without a rerun.
11_MULTI-CHECKPOINT_LOGIT-POOL	ran on Kaggle	Registered. Its probability artifacts were derived through tools/eval_from_probs.py into results_summary/11_MULTI-CHECKPOINT_LOGIT-POOL/, so it appears in the tables above. Not yet adjudicated — see below.

About the highest accuracy figures

Notebook 11 reports the strongest held-out test result in the repository: 409 of 450 rows, accuracy 0.9089, ROC-AUC 0.9682, PR-AUC 0.9553. It pools mean log-odds across four admitted checkpoints — a fifth, microsoft/deberta-v3-base, was excluded by the prespecified 0.84 validation admission floor — at a fixed 0.50 cutoff.

The way it got there is worth as much as the number. Its validation gate passed at 393/450 against a prespecified bar of 392, and the prespecified primary decision rule was retained, because the strongest of four challengers gained only 2 correct validation examples against a required 3. The result was not tuned into existence after the fact.

Technique	Correct / 450	Accuracy	Status
11_MULTI-CHECKPOINT_LOGIT-POOL	409	0.9089	registered, not adjudicated
10_TWITTER-ROBERTA_LOGIT-POOL-STABLE	403	0.8956	unregistered, no probability export
07_TWITTER-ROBERTA_FINE-TUNE	400	0.8889	current champion

Two caveats still apply, and they are the difference between a strong measurement and an established improvement:

1. The margin is below the detection floor. Notebook 11 leads the champion by 9 test rows. Roughly 14 are needed for a McNemar-detectable difference on a 450-row split, before Holm correction over a family that has now consumed ten test unlocks. It may well come back INCONCLUSIVE.
2. No verdict has been issued. Only tools/compare_techniques.py may adjudicate a candidate against the champion, and it has not been run. 07_TWITTER-ROBERTA_FINE-TUNE therefore remains the champion in research_loop/registry.json, and notebook 11 has no preregistration or ledger entry yet.

So notebook 11 is a real, derived, schema-valid result that leads on every metric — and it is not yet a promotion. research_loop/STATE.md lists the remaining steps.

Reproducibility workflow

Run notebooks in numeric order:

Notebook	Purpose
00_create_dataset_and_splits.ipynb	Creates the processed dataset, validates labels, assigns canonical row IDs, creates fixed stratified splits, and writes the dataset manifest.
01_LOG-REG_TF-IDF.ipynb	Trains and evaluates Logistic Regression with word-level TF-IDF features.
02_LIN-SVM_TF-IDF.ipynb	Trains and evaluates calibrated Linear SVM with word-level TF-IDF features.
03_SLP_TF-IDF.ipynb	Trains and evaluates a single-layer perceptron over TF-IDF features.
04_CHAR-TF-IDF_LIN-SVM.ipynb	Tests whether character n-grams improve robustness to misspellings, obfuscation, hashtags, and noisy social-media phrasing.
05_WORD-CHAR-TF-IDF_LIN-SVM.ipynb	Combines word-level semantic lexical cues with character-level robustness.
06_FASTTEXT-EMB_LOG-REG.ipynb	Tests dense FastText document embeddings with logistic regression.
07_TWITTER-ROBERTA_FINE-TUNE.ipynb	Fine-tunes a contextual Twitter-RoBERTa transformer and saves token-attribution interpretability outputs.

The remaining notebooks are candidate experiments rather than part of the controlled comparison:

Notebook	Purpose
08_BEST-ROBERTA_SEED-ENSEMBLE.ipynb	Averages seed restarts of the fine-tuned transformer. Ran; regressed against 07.
09_MULTI-CHECKPOINT_LOGIT-STACK.ipynb	Learned stacker over several transformer checkpoints. Built but never run; superseded by 11.
10_TWITTER-ROBERTA_LOGIT-POOL-STABLE.ipynb	Replaces probability averaging with mean log-odds pooling across seeds, and threshold grids with exact probability-change intervals. Ran; unregistered.
11_MULTI-CHECKPOINT_LOGIT-POOL.ipynb	Pools mean log-odds across five checkpoints spanning fine-tuning lineage, pretraining corpus, architecture/tokenizer, and scale, anchored on 10's recipe. Awaiting GPU run.

Expected compact output structure for most classical and embedding model families:

results_summary/<TECHNIQUE>/
├── ablation_results.csv
├── best_config.json
├── classification_report_test.json
├── confusion_matrix_test.csv
├── metrics_validation.json
└── metrics_test.json

The RoBERTa folder may instead save a compact image confusion matrix and threshold sweep:

results_summary/07_TWITTER-ROBERTA_FINE-TUNE/
├── ablation_results.csv
├── best_config.json
├── confusion_matrix_test.png
├── metrics_validation.json
├── metrics_test.json
└── threshold_sweep_validation.csv

Installation and environment

Clone the repository:

git clone https://github.com/73anthonyL/extremism_sentiment_analysis.git
cd extremism_sentiment_analysis

Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # macOS/Linux
# .venv\Scripts\activate   # Windows

Install dependencies:

python -m pip install --upgrade pip
pip install -r requirements.txt

For stricter replication of the original working environment, use the lock file instead:

python -m pip install --upgrade pip
pip install -r requirements-lock.txt

Optional Jupyter kernel setup:

python -m ipykernel install --user --name extremism-research --display-name "Python (extremism research)"

Running the experiments

Option A: Kaggle

1. Attach the Kaggle dataset to the notebook environment.
2. Run 00_create_dataset_and_splits.ipynb first.
3. Save the generated processed dataset, split assignments, and dataset manifest.
4. Run each model notebook using the fixed split assignments.
5. Commit only compact, reviewable result summaries. Avoid committing raw prediction files or large model artifacts.

Option B: Local environment

1. Place dataset.csv under data/.
2. Run 00_create_dataset_and_splits.ipynb.
3. Confirm that the generated split files match the expected split version.
4. Run model notebooks in numeric order.
5. Compare results using the JSON and CSV artifacts in results_summary/.

Experiment protocol

To keep the model comparison research-grade:

• Use the same processed dataset for every technique.
• Use the same split_assignments.csv for every technique.
• Tune hyperparameters on the training and validation splits only.
• Select thresholds using validation data only.
• Evaluate on the test split only after model configuration and threshold selection are complete.
• Report accuracy, positive-class precision, positive-class recall, positive-class F1, ROC-AUC, PR-AUC, and confusion-matrix counts.
• Save the best configuration, validation metrics, test metrics, and confusion matrix for each technique.
• Treat preprocessing changes, external pretraining, and task-specific transfer learning as part of the experimental condition.
• Unlock the test split once per technique, ever, and record it in research_loop/test_ledger.jsonl. Each unlock raises the Holm correction every later candidate must clear.
• Let tools/compare_techniques.py issue the verdict. Do not describe a technique as better than another without one.

Interpretability

The project emphasizes explainable AI and model auditing. Interpretability methods differ by model family:

• Linear TF-IDF models: coefficient- or margin-based feature analysis.
• SLP over TF-IDF: linear logit contribution analysis.
• FastText embeddings: embedding-direction and token-contribution approximations.
• Twitter-RoBERTa: token-level gradient attribution and local error review.

Interpretability artifacts should be used to inspect model behavior and guide error analysis. They should not be treated as proof that a model understands ideology, intent, or real-world risk.

Limitations

• The task is high-stakes and cannot be reduced safely to a single automated label.
• The dataset is small enough that external validation is necessary before broad claims.
• The binary label simplifies a complex social and political phenomenon.
• Models may learn lexical, ideological, demographic, or topical shortcuts.
• The transformer result demonstrates performance gains under this split, not deployment readiness.

Repository maintenance

When adding or rerunning a model notebook:

1. Keep the fixed split protocol unchanged unless a new split version is explicitly introduced. Never regenerate the split assignment.
2. Export a sanitized probability artifact (row_id, split, y_true, y_prob) and derive the result folder with tools/eval_from_probs.py. Do not hand-copy metrics into JSON.
3. Save only compact result artifacts to results_summary/.
4. Avoid committing large model artifacts, raw-text predictions, local explanation files containing raw text, or notebooks with saved cell outputs.
5. Regenerate the documentation tables with tools/render_tables.py --write rather than editing them, then confirm --check exits 0.
6. Update the prose in README.md, docs/EXPERIMENTS.md, docs/MODEL_CARD.md, docs/RESULTS_SCHEMA.md, docs/REPLICATION_GUIDE.md, notebooks/README.md, and results_summary/README.md when a new technique becomes part of the controlled comparison.
7. Record the outcome in research_loop/ — see docs/RESEARCH_LOOP.md. The test split is unlocked once per technique, ever, and every unlock raises the Holm correction that future candidates must clear.

Citation

See CITATION.cff for citation metadata.

Authors

Maintained by the project authors as a research and replication artifact.