Author: Denis Avetisyan
A new approach leverages natural language processing to anticipate social unrest and planned gatherings by analyzing news coverage.
This research details a system for forecasting planned events by extracting key information – dates, locations, and involved entities – from news articles using topic modeling, named entity recognition, and relation extraction techniques.
Predicting and proactively responding to civil unrest remains a significant challenge for administrative officials, particularly in open societies. This paper, ‘Planned Event Forecasting using Future Mentions and Related Entity Extraction in News Articles’, introduces a novel system for forecasting planned social unrest by leveraging information embedded within news media. The approach combines topic modeling, named entity recognition, and a new ‘Related Entity Extraction’ method to identify key features – dates, locations, people, and organizations – signaling potential events. Could this technique offer a generalized, geographically independent solution for early warning systems and improved public safety preparedness?
Echoes of Discontent: Identifying Signals in the Noise
The increasing volume of online news presents a significant challenge for identifying early indicators of potential social unrest. Distinguishing meaningful signals requires advanced analytical techniques capable of processing large datasets and discerning subtle patterns. Traditional keyword-based approaches are inadequate, failing to capture nuanced expressions or contextual relevance. Effective event detection necessitates a multi-stage process that moves beyond simple lexical analysis, coupling article filtering with semantic understanding through natural language processing and machine learning. Every missed signal is a reminder that even robust systems are subject to change.
Mapping the Network: Uncovering Key Players and Relationships
Identifying related entities – people, organizations, and locations – is critical to understanding the scope of a potential event. Accurate detection relies on pinpointing these key players within large text datasets, establishing a foundation for subsequent analysis. This requires sophisticated Named Entity Recognition (NER) techniques utilizing linguistic rules, statistical models, and machine learning algorithms. Beyond recognition, Relation Extraction is needed to map connections between entities, revealing the event’s network structure. The system achieves 64.3% precision and 63% recall in extracting these connections, enabling the construction of a relational graph that provides a comprehensive understanding of the event’s dynamics.
Refining Perception: Advanced Techniques for Semantic Understanding
To improve entity and relation extraction, a combined approach incorporating advanced methods is utilized, including a Window Based Model and the Stanford Relation Extractor. A Lexicon guides the relation extraction process, ensuring identified relationships are meaningful. Phrase Learning, powered by a Word2Vec Model, expands the ability to identify relevant keywords. These techniques are integrated with Topic Modelling, employing both Vector Space Model (VSM) and LDA Model to refine relevance, yielding 85% precision, 69.5% recall, and a 76.6% F-measure for identifying relevant documents.
Forecasting the Inevitable: From Detection to Prediction
Recent advancements in event forecasting have moved beyond simple detection to proactive prediction, integrating semantic understanding with detailed temporal analysis. A critical component is the inclusion of spatio-temporal data, providing contextual information regarding location and timing. This holistic approach results in a more reliable assessment of risk and allows for targeted interventions. The research demonstrates an overall accuracy of 87% in related entity extraction, validating the system’s potential for reliable forecasting. Like all structures, even the most robust predictions are subject to the relentless march of time, offering not permanence, but a fleeting moment of clarity.
The pursuit of forecasting, as demonstrated in this system for predicting planned events, inherently acknowledges the relentless march of time and the inevitable decay of information. Like all systems, predictive models aren’t static; they require constant refinement as the landscape of news and social dynamics shifts. Andrey Kolmogorov observed, “The most important thing in science is not to be certain, but to be willing to change one’s mind.” This resonates deeply with the methodology presented—a continuous process of information extraction, relation analysis, and model adaptation. The system’s reliance on extracting entities and relations from news articles is a versioning process itself—capturing a snapshot of intent at a specific moment, knowing that future mentions will necessitate updates and revisions to maintain accuracy. The arrow of time always points toward refactoring, even in the realm of predictive analytics.
What Lies Ahead?
The pursuit of predictive capacity, as demonstrated by this work, inevitably encounters the paradox of anticipation. Systems built on identifying precursors to social unrest highlight not a mastery of causality, but a refined sensitivity to patterns—patterns which, like all phenomena, are subject to decay and re-emergence. The current architecture, reliant on news media as a primary data source, must confront the accelerating fragmentation of information ecosystems. Every delay in adapting to these shifts is the price of understanding—a more nuanced signal, or simply, a fading one.
Future work should consider the inherent ephemerality of ‘events’ themselves. The boundaries are rarely neat; the categorization, inevitably subjective. A focus on relational durability—the underlying networks of actors and organizations—may prove more resilient than attempts to pinpoint isolated incidents. Architecture without historical context is fragile and ephemeral; tracking the evolution of these relationships, rather than merely the events they produce, could yield a more graceful aging of these predictive systems.
Ultimately, the value lies not in achieving perfect foresight—an impossible goal—but in developing a deeper comprehension of the complex interplay between information, agency, and the unfolding of time. The system’s true test will not be its accuracy on a given forecast, but its capacity to adapt, learn, and reveal the limitations of its own predictions.
Original article: https://arxiv.org/pdf/2511.07879.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-11-12 17:38