Horizon Academic is thrilled to offer a new addition to our catalogue of Seminar courses: Leveraging Machine Learning and Social Media to Detect Fake News, Understand Mental Health, and Combat Cybercrime. Spearheading the course will be our highly experienced instructor, Dr. Maria Konte. At Georgia Tech’s School of Computer Science, Dr. Maria Konte currently works as a Research Scientist at the Institute for Information Security & Privacy. 

People are increasingly turning to public social media platforms, such as Twitter, Facebook, and Instagram, to read daily news and get a sense of what’s happening in the world as well as share their thoughts, feelings, and worldviews. In turn, social media data feeds can provide invaluable insights and strong signals of emerging problems. As social media seeps into our everyday lives, misinformation, fake news, and hate speech permeates our online existence, resulting in implications for the real world. This course enables students to be one step ahead of the malicious activity on social networking services by leveraging powerful machine learning tools and social media feeds to detect when a social media account is involved with spreading disinformation or engaging in cyberbullying, “trolling,” or the like. Alternatively, students may choose to focus on wielding machine learning to develop predictive models that can identify declines in mental health decline, such as depression. The scope of this course further covers discerning incidents of abuse or misuse of social media accounts as a tool for spamming, participating in cyberattacks, or proliferating malicious software. Of course, students are also welcome and encouraged to propose their own research project and questions. Nonetheless, below, students can find a list of pre-approved topics for the course. For a full-fledged description of the Leveraging Machine Learning and Social Media to Detect Fake News, Understand MentalHealth, and Combat Cybercrime course offering, please visit this page.

Course Description:

In this class, we will work with real world social media datasets (Twitter, Reddit, etc) and applied machine learning techniques to develop models that indicate when a problem is under the way. 

Pre-approved use cases and project areas include:

1. Digital Epidemiology: Mental health and social media 

2. Predicting Depression Using social media data 

3. Detecting fake news and misinformation

4. Modeling the spread of information over social media 

5. Detecting hate speech 

6. Identifying cyberbullying on social media

7. Applying graph analysis techniques on social media 

8. The formation of communities on social media 

9. Non-Coding Track: public policy and regulation of social media