| Title | Extracting Textual Features of Financial Social Media to Detect Cognitive Hacking |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Chung, Wingyan, Liu, Jinwei, Tang, Xinlin, Lai, Vincent S. K. |
| Conference Name | 2018 IEEE International Conference on Intelligence and Security Informatics (ISI) |
| Date Published | nov |
| Keywords | abnormal 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 |
| 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. |
| DOI | 10.1109/ISI.2018.8587364 |
| Citation Key | chung_extracting_2018 |
Extracting Textual Features of Financial Social Media to Detect Cognitive Hacking