Twitter

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

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This paper discusses automated methods for detecting online radicalization and radical communities on various platforms, from websites, to forums, to social media platforms such as Facebook, YouTube, Twitter, and others.

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This 2013 article analyzes content generated on Twitter during the attacks at the Boston Marathon in April 2013. The authors perform an in-depth characterization of what factors influenced malicious content and profiles becoming viral.

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This 2014 paper utilizes a form of network representation that incorporates multiple data views to uncover and categorize networks of accounts that fall into four broadly defined groups in the Syrian conflict.

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This netnographic study examines al-Shabaab’s Western media strategy. The authors focus their study on the group’s recruitment of Western Muslims through the analysis of primary sources, interviews in East Africa and a quantitative analysis of the group’s Twitter outputs.

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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.

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This article focuses on the use of linguistic “weak signals”—digital traces of intent—in social media as a tool of counterterrorism aimed at preventing lone-wolf attacks.

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The authors of this article propose a methodology for automatically identifying events and their associated user-contributed social media documents (such as those posted on Flickr, YouTube, and Facebook) to enable event browsing and search in state-of-the-art search engines.

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This paper examines approaches for analyzing Twitter messages to distinguish between those covering real-world events and non-event messages. To validate this work the authors applied their process to 2.6 million Twitter messages.

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This is a slideshow of the June 2011 lecture on the topic of community detection using graphs with a focus on Social Media applications.

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