One obstacle which children with autism spectrum disorders (ASDs) face when learning in a public-school environment is the lack of feeling included when learning. In this study, the term inclusion refers to time that children with ASDs spend in general education settings, interacting and/or engaging with neurotypical students and teachers. Inclusion can help students with ASDs improve their social skills, as well as academic achievement, mental health, and future success (Camargo et al., 2014). Since children with ASDs often have difficulties with social interaction skills, this can prevent their successful inclusion in general education placements. Music is a type of behaviorally-based intervention, which has proven to be effective in helping students develop the skills necessary to be successfully included, and because it is a type of activity which can serve as a bit of a distraction from the social aspect of the interaction, it can help children practice social skills and interact in a comfortable way. This study examines how music is used in public school settings to help foster the skills necessary for autistic children to be involved in standard school curriculums in order to allow them to receive the full benefits from learning in a general education setting. This study was conducted by reviewing past literature on the benefits of inclusion in special education, the benefits of music for children with ASDs, and the difference in efficacy of music interventions when conducted in an inclusive setting. Interviews with special education teachers, music educators, and music therapists were also conducted to address examples of the impact of music in this research area. The study found that music is beneficial in allowing more students to be included in standard school curriculums, and data showed the trend that inclusion positively affected their social and academic development.

In this paper, I introduce the fake news problem and detail how it has been exacerbated<br/>through social media. I explore current practices for fake news detection using natural language<br/>processing and current benchmarks in ranking the efficacy of various language models. Using a<br/>Twitter-specific benchmark, I attempt to reproduce the scores of six language models<br/>demonstrating their effectiveness in seven tweet classification tasks. I explain the successes and<br/>challenges in reproducing these results and provide analysis for the future implications of fake<br/>news research.

Social injustice issues are a familiar, yet very arduous topic to define. This is because they are difficult to predict and tough to understand. Injustice issues negatively affect communities because they directly violate human rights and they span a wide range of areas. For instance, injustice issues can relate to unfair labor practices, racism, gender bias, politics etc. This leaves numerous individuals wondering how they can make sense of social injustice issues and perhaps take efforts to stop them from occurring in the future. In an attempt to understand the rather complicated nature of social injustice, this thesis takes a data driven approach to define a social injustice index for a specific country, India. The thesis is an attempt to quantify and track social injustice through social media to see the current social climate. This was accomplished by developing a web scraper to collect hate speech data from Twitter. The tweets collected were then classified by their level of hate and presented on a choropleth map of India. Ultimately, a user viewing the ‘India Social Injustice Index’ map should be able to simply view an index score for a desired state in India through a single click. This thesis hopes to make it simple for any user viewing the social injustice map to make better sense of injustice issues.