We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

Social media has become a prominent part of people’s life worldwide. It allows for easy communication and connection between family, friends, and even complete strangers. It has provided for an increased global interconnectedness and allows people to form new relationships. Despite these positives, there are also negative effects of social media, including its danger to mental health. With increased social media use, it is possible to develop an addiction, similar to any substance addiction. People may also experience various mental health disorders, like depression and anxiety disorders. The purpose of this review was to identify a clear relationship between social media use and the development of anxiety disorders. Anxiety disorders is a general term, encompassing the different types, but the main types focused on in this review are generalized anxiety disorder and social anxiety. This review also incorporates information on age in order to clarify if certain age groups are more affected by social media use than others.