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Behavioural Analytics: Mathematics of the Mind

arXiv 2502.00013 ↗First posted 7 Jan 2025cs.CY cs.LGcited by 0

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Abstract (arXiv)

Behavioural analytics provides insights into individual and crowd behaviour, enabling analysis of what previously happened and predictions for how people may be likely to act in the future. In defence and security, this analysis allows organisations to achieve tactical and strategic advantage through influence campaigns, a key counterpart to physical activities. Before action can be taken, online and real-world behaviour must be analysed to determine the level of threat. Huge data volumes mean that automated processes are required to attain an accurate understanding of risk. We describe the mathematical basis of technologies to analyse quotes in multiple languages. These include a Bayesian network to understand behavioural factors, state estimation algorithms for time series analysis, and machine learning algorithms for classification. We present results from studies of quotes in English, French, and Arabic, from anti-violence campaigners, politicians, extremists, and terrorists. The algorithms correctly identify extreme statements; and analysis at individual, group, and population levels detects both trends over time and sharp changes attributed to major geopolitical events. Group analysis shows that additional population characteristics can be determined, such as polarisation over particular issues and large-scale shifts in attitude. Finally, MP voting behaviour and statements from publicly-available records are analysed to determine the level of correlation between what people say and what they do.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 35 references that are in the map. First posted on arXiv 7 Jan 2025.

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9 of 35 references.

References · 9

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  5. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  6. 2017Word Translation Without Parallel Datacited by 487
  7. 2019CamemBERT: a Tasty French Language Modelcited by 368
  8. 2019Multilingual Universal Sentence Encoder for Semantic Retrievalcited by 193
  9. 2016Empath: Understanding Topic Signals in Large-Scale Textcited by 51

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