"No civil discourse, no cooperation; misinformation, mistruth." These were the words of former Facebook Vice President Chamath Palihapitiya who publicly expressed his regret in a 2017 interview over his role in co-creating Facebook. Palihapitiya shared that social media is ripping apart the social fabric of society and he also sounded the alarm regarding social media’s unavoidable global impact. He is only one of social media’s countless critics. The more disturbing issue resides in the empirical evidence supporting such notions. At least 95% of adolescents own a smartphone and spend an average time of two to four hours a day on social media. Moreover, 91% of 16-24-year-olds use social media, yet youth rate Instagram, Facebook, and Twitter as the worst social media platforms. However, the social, clinical, and neurodevelopment ramifications of using social media regularly are only beginning to emerge in research. Early research findings show that social media platforms trigger anxiety, depression, low self-esteem, and other negative mental health effects. These negative mental health symptoms are commonly reported by individuals from of 18-25-years old, a unique period of human development known as emerging adulthood. Although emerging adulthood is characterized by identity exploration, unbounded optimism, and freedom from most responsibilities, it also serves as a high-risk period for the onset of most psychological disorders. Despite social media’s adverse impacts, it retains its utility as it facilitates identity exploration and virtual socialization for emerging adults. Investigating the “user-centered” design and neuroscience underlying social media platforms can help reveal, and potentially mitigate, the onset of negative mental health consequences among emerging adults. Effectively deconstructing the Facebook, Twitter, and Instagram (i.e., hereafter referred to as “The Big Three”) will require an extensive analysis into common features across platforms. A few examples of these design features include: like and reaction counters, perpetual news feeds, and omnipresent banners and notifications surrounding the user’s viewport. Such social media features are inherently designed to stimulate specific neurotransmitters and hormones such as dopamine, serotonin, and cortisol. Identifying such predacious social media features that unknowingly manipulate and highjack emerging adults’ brain chemistry will serve as a first step in mitigating the negative mental health effects of today’s social media platforms. A second concrete step will involve altering or eliminating said features by creating a social media platform that supports and even enhances mental well-being.

The role of technology in shaping modern society has become increasingly important in the context of current democratic politics, especially when examined through the lens of social media. Twitter is a prominent social media platform used as a political medium, contributing to political movements such as #OccupyWallStreet, #MeToo, and #BlackLivesMatter. Using the #BlackLivesMatter movement as an illustrative case to establish patterns in Twitter usage, this thesis aims to answer the question “to what extent is Twitter an accurate representation of “real life” in terms of performative activism and user engagement?” The discussion of Twitter is contextualized by research on Twitter’s use in politics, both as a mobilizing force and potential to divide and mislead. Using intervals of time between 2014 – 2020, Twitter data containing #BlackLivesMatter is collected and analyzed. The discussion of findings centers around the role of performative activism in social mobilization on twitter. The analysis shows patterns in the data that indicates performative activism can skew the real picture of civic engagement, which can impact the way in which public opinion affects future public policy and mobilization.

Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.