This isn't a bias chart. It measures how dangerous an information environment actually is and where it's heading. Validated against documented pre-conflict cases including Rwanda, Myanmar, and the 2022 Russian invasion of Ukraine.
Model an intervention: what happens to the threat level if you add a new outlet to the ecosystem?
Define reference entities (think tanks, parties, factions, institutions) with known ideological positions. Position: -1 (left pole) to +1 (right pole).
Define media outlets to analyze. Audience is relative weight (e.g., millions of viewers/readers).
Enter citation counts: how many times each outlet cited each anchor entity during the analysis period.
Add anchors and outlets first to generate the citation matrix.
Paste CSV data below. Format: Outlet, Audience, Anchor1, Anchor2, ...
First row must be headers. Subsequent rows contain outlet data with citation counts per anchor.
Ad Fontes and AllSides answer: "How biased is this outlet?" This tool answers: "How dangerous is this information environment, and where is it heading?" The unit of analysis is the ecosystem, not the outlet. The output is predictive, not descriptive. And the framework is grounded in conflict studies, not media literacy.
Adapted from Groseclose & Milyo (2005). Each outlet is scored [-1, +1] based on its citation patterns of anchor entities with known positions, using IDF weighting to discount universally-cited sources.
Audience-weighted standard deviation of SQ scores, normalized to [0-100]. Measures whether the ecosystem is clustered (healthy) or bimodal (polarized).
Derived from Mueller & Rauh (2018, APSR) who found that the disappearance of shared topics (diplomacy, trade, judiciary) is more predictive of conflict onset than the presence of conflict topics. NCI measures pairwise cosine similarity of outlet citation profiles.
Shannon entropy of ecosystem-wide citation distribution. Based on the Yugoslavia pattern: Milosevic controlled 95% of Serbian media by 1989 (Fogg, W&M; MOM Serbia). Low entropy = state capture or monopoly narrative.
Derived from the Myanmar finding: 98% of social media users were on Facebook, and algorithmic reinforcement created sealed information bubbles (Yale Macmillan Center, 2024). XD measures the audience-weighted extremity of media consumption.
Jamison et al. (2023, SSRN) found that media sentiment spikes, not levels, are the strongest short-term conflict predictors (AUC 86-94%). This metric captures the rate and acceleration of polarization change.
Five-factor model with weights calibrated from case literature. The public showcase demonstrates the framework; full calibration parameters, pattern matching trajectories, and proprietary weighting schemas are available under commercial license.
Threat levels are aligned with Stanton's Ten Stages of Genocide and Benesch's Dangerous Speech Framework:
| Range | Level | Interpretation | Historical Pattern |
|---|---|---|---|
| 0-20 | Normal | Healthy media competition | Canada/Germany baseline |
| 20-40 | Watch | Polarization trends detected | Early divergence signals |
| 40-60 | Warning | Narrative fragmentation accelerating | Ethiopia 2019, Myanmar 2020 |
| 60-80 | Alert | Pre-conflict media patterns detected | Yugoslavia 1991, Myanmar 2021-Q1 |
| 80-100 | Critical | Media environment resembles onset conditions | Rwanda 1994, Yugoslavia 1992 |
The tool compares the current ecosystem's [Polarization, NCI, Entropy, Echo, Velocity] vector against reconstructed trajectories from five documented cases: Rwanda (Yanagizawa-Drott 2014, Roozen & Shulman 2014), Myanmar (Yale Macmillan 2024), Yugoslavia (Fogg, PBS Frontline), Ethiopia (Nigussie et al. 2024), and a stable democracy baseline. Pattern matching uses normalized Euclidean distance.
Foundational
Groseclose, T. & Milyo, J. (2005). A Measure of Media Bias. QJE, 120(4).
Mueller, H. & Rauh, C. (2018). Reading Between the Lines. APSR, 112(2).
Yanagizawa-Drott, D. (2014). Propaganda and Conflict. QJE, 129(4).
Benesch, S. (2014). Countering Dangerous Speech. USHMM Working Paper.
Stanton, G. (2016). Ten Stages of Genocide. Genocide Watch.
Hegre, H. et al. (2019). ViEWS: A Political Violence Early-Warning System. JPR, 56(2).
Case Studies
Roozen, K. & Shulman, S. (2014). Tuning in to the RTLM. Conflict & Communication.
Nigussie, D. et al. (2024). Echoes of Violence: Hate Speech in Ethiopian Media. SAGE.
Jamison, A. et al. (2023). Media Sentiment and Future Conflict Events. SSRN.
2025-2026 (Cutting Edge)
Petrova, M. & Tapsoba, A. (2025). Information and Conflict. Economic Policy, Oxford.
Piccardi, T. et al. (2025). Reranking Partisan Animosity Alters Polarization. Science.
EPJ Data Science (2025). Predicting Collective Violence Through Hostile Social Media Discourse.
STFT-VNNGP (2025). Forecasting Geopolitical Events with Sparse Temporal Fusion Transformer. arXiv.
VIEWS/PRIO (2025-2026). Violence & Impacts Early-Warning System monthly forecasts.
ACLED (2026). Conflict Index & 2026 Watchlist.
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