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Characterization of disinformation networks using graph embeddings and opinion mining

Published in:
2019 European Intelligence and Security Informatics Conference, EISIC, 26-27 November 2019.

Summary

Global social media networks' omnipresent access, real time responsiveness and ability to connect with and influence people have been responsible for these networks' sweeping growth. However, as an unintended consequence, these defining characteristics helped create a powerful new technology for spread of propaganda and false information. We present a novel approach for characterizing disinformation networks on social media and distinguishing between different network roles using graph embeddings and hierarchical clustering. In addition, using topic filtering, we correlate the node characterization results with proxy opinion estimates.We plan to study opinion dynamics using signal processing on graphs approaches using longer-timescale social media datasets with the goal to model and infer influence among users in social media networks.
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Summary

Global social media networks' omnipresent access, real time responsiveness and ability to connect with and influence people have been responsible for these networks' sweeping growth. However, as an unintended consequence, these defining characteristics helped create a powerful new technology for spread of propaganda and false information. We present a novel...

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A reverse approach to named entity extraction and linking in microposts

Published in:
Proc. of the 6th Workshop on "Making Sense of Microposts" (part of: 25th Int. World Wide Web Conf., 11 April 2016), #Microposts2016, pp. 67-69.

Summary

In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base.
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Summary

In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions...

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Named entity recognition in 140 characters or less

Published in:
Proc. of the 6th Workshop on "Making Sense of Microposts" (part of: 25th Int. World Wide Web Conf., 11 April 2016), #Microposts2016, pp. 78-79.

Summary

In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. In spite of the challenges associated with information extraction from microposts, it remains an increasingly important genre. This paper presents the MIT Information Extraction Toolkit (MITIE) and explores its adaptability to the micropost genre.
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Summary

In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. In spite of the challenges associated with information extraction from microposts, it remains an increasingly...

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Finding malicious cyber discussions in social media

Summary

Today's analysts manually examine social media networks to find discussions concerning planned cyber attacks, attacker techniques and tools, and potential victims. Applying modern machine learning approaches, Lincoln Laboratory has demonstrated the ability to automatically discover such discussions from Stack Exchange, Reddit, and Twitter posts written in English.
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Summary

Today's analysts manually examine social media networks to find discussions concerning planned cyber attacks, attacker techniques and tools, and potential victims. Applying modern machine learning approaches, Lincoln Laboratory has demonstrated the ability to automatically discover such discussions from Stack Exchange, Reddit, and Twitter posts written in English.

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