Extracting Textual Features Of Financial Social Media To Detect Cognitive Hacking

Keywords

Abnormal behavior; Cognitive hacking; Cyber threats; Cybersecurity; Feature extraction; Financial market; Information gain; Social media; Text mining

Abstract

Social media are increasingly reflecting and influencing the behavior of human and financial market. Cognitive hacking leverages the influence of social media to spread deceptive information with an intent to gain abnormal profits illegally or to cause losses. Measuring the information content in financial social media can be useful for identifying these attacks. In this paper, we developed an approach to identifying social media features that correlate with abnormal returns of the stocks of companies vulnerable to be targets of cognitive hacking. To test the approach, we collected price data and 865,289 social media messages on four technology companies from July 2017 to June 2018, and extracted features that contributed to abnormal stock movements. Preliminary results show that terms that are simple, motivate actions, incite emotion, and uses exaggeration are ranked high in the features of messages associated with abnormal price movements. We also provide selected messages to illustrate the use of these features in potential cognitive hacking attacks.

Publication Date

12-24-2018

Publication Title

2018 IEEE International Conference on Intelligence and Security Informatics, ISI 2018

Number of Pages

244-246

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ISI.2018.8587364

Socpus ID

85061061688 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85061061688

This document is currently not available here.

Share

COinS