Machine Learning and Natural Language Processing (ML/NLP)

This article studied a selection of right-wing extremist (RWE) groups on Twitter. The authors looked at particular language-based networks as case studies, collecting Twitter data for groups across eight countries.

This article presents methods for identifying the recruitment activities of violent groups within extremist social media websites.

This paper explores the use of crawling global social networking platforms to undercover previously unknown radicalized individuals. To prove the utility of this process the authors collect a YouTube dataset from a group that potentially has a radicalizing agenda.

This paper presents a theoretical computerized system to detect social polarization and to estimate the related chances of violent radicalization. Existing technologies are analyzed to determine how they can be integrated into the proposed system to fulfil the authors’ objectives.

This article addresses the degree to which geolocation prediction is vital to geospatial applications like localised search and local event detection.

This resource contains the description and data analysis from a research project conducted by Fifth Tribe into ISIS’s use of Twitter in the aftermath of the 2015 Paris Attacks.

This 2013 article focuses on Natural Language Processing (NLP) and particularly social media monitoring of Twitter for purposes of national security.

The authors of this 2011 article hypothesize that the results of Google Trends, given its daily and weekly reports on queries related to various industries may be correlated to the current level of economic activity in these industries.

This article examines the use of Machine Learning (ML) algorithms for predicting key nodes for targeting within terrorist organizational structures.

This is a foundational report and a seminal work in the study of social media intelligence and open source research. The paper reviews 245 papers in a semi-systematic literature review of how information and insight can be drawn from open social media sources.

In this paper, the authors argue that despite the widespread use of social media in various domains (e.g.

This article is a seminal piece and a foundational resource in the field of social media analytics and open source intelligence by some of the field’s leading authors.

This article focuses on the problem of identifying influential Twitter users using Machine Learning (ML) techniques and Natural Language Processing (NLP).