Visible to the public Extracting Textual Features of Financial Social Media to Detect Cognitive Hacking

TitleExtracting Textual Features of Financial Social Media to Detect Cognitive Hacking
Publication TypeConference Paper
Year of Publication2018
AuthorsChung, Wingyan, Liu, Jinwei, Tang, Xinlin, Lai, Vincent S. K.
Conference Name2018 IEEE International Conference on Intelligence and Security Informatics (ISI)
Date Publishednov
Keywordsabnormal behavior, Cognition, Cognitive Hacking, Companies, Computer crime, cyber threats, cybersecurity, data mining, Economics, feature extraction, financial market, financial social media messages, Human Behavior, Human Behavior and Cybersecurity, Information Gain, potential cognitive hacking attacks, price movements, Pricing, pubcrawl, social media, Social network services, social networking (online), stock markets, stock movements, text mining, textual features extraction
AbstractSocial 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.
Citation Keychung_extracting_2018