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Up to 20% of US students reported being victimized by their classmates in 2017 (Wang et al., 2020). Consequences of peer victimization include self-harm and suicide attempts (Peng et al., 2019; Sigurdson et al., 2018), depression (Chou et al., 2020), anxiety (Mulder et al., 2017), low self-esteem (Cénat et al., 2015), substance use (Glassner & Cho, 2018), and bringing weapons to school (Smalley et al., 2017). Consequences appear to be worse among youth victimized due to actual or perceived social identities (e.g., targeted because of race or sexual orientation; Bucchianeri, 2016). Peer aggression has been declared a public health issue (Feder, 2007) and researchers continue to seek interventions to decrease its frequency (Olweus & Limber, 2010; Salmivalli et al., 2011; Newman-Carlson & Horne, 2004). This research found youth reporting social identity-based victimization were more likely to experience negative consequences than others, and feelings of social alienation partially explained these outcomes.

This dissertation incorporates three essays related to youth’s health and human capital outcomes. The first two essays investigate the impacts of important public policies on adolescents’ mental health and risky behavioral outcomes. Essay three examines the effects of mothers’ non-cognitive skills on children’s home environment qualities and their cognitive and behavioral outcomes. Domestic violence is a large public issue in the United States. Chapter 1 investigates the effectiveness of warrantless arrest laws enacted by states for domestic violence incidents on multiple youth mental and behavioral outcomes. Under these laws, police officers can arrest a suspect without a warrant even if they did not witness the crime. Although young women remain at the highest risk of victimization of domestic violence, children ages 3 to 17 years are also at elevated risk for domestic violence. Further, over 15 million children witness domestic violence in their homes every year in the United States. Exposure to domestic violence is associated with various social, emotional, behavioral, and health-related problems among youth. Using variation in timing of implementation of the arrest laws across states, I utilize differences-in-differences analyses in multiple, large-scale data sets of nationally representative samples of youth population to study the impact of the laws on a number of youth mental and behavioral outcomes. Results indicate the presence of heterogeneity with respect to the impact of states’ arrest laws on the outcomes studied. The study is useful for policymakers as it provides important evidence on the effectiveness of state measures designed to reduce domestic violence. The estimates obtained in the analyses are robust to multiple sensitivity checks to address key threats to identification. Chapter 2 empirically examines the effects of state cyberbullying laws on youth outcomes with respect to measures of school violence, mental health, and substance use behavior. Electronic form of harassment or cyberbullying is a large social, health, and education issue in the United States. In response to cyberbullying, most state governments have enacted electronic harassment or cyberbullying law as a part of their bullying prevention law. The analysis uses variation in the timing of implementation of cyberbullying laws across states as an exogenous source of variation. Using nationally representative samples of high-school teenagers from national and state Youth Risk Behavior Surveys, the study finds evidence of a positive relationship between adoption of cyberbullying laws and students’ reporting of certain experiences of school violence, mental health problems, and substance use activities. Regression analyses also study the effects of some important components of state cyberbullying laws. Finally, this study examines the sex-specific impacts of cyberbullying laws and its components on youth. The causal estimates are robust to the inclusion of multiple sensitivity checks. This study provides evidence on the efficacy of public measures designed to address cyberbullying among school-age children. Chapter 3 utilizes matched data from National Longitudinal Surveys of Youth (NLSY79) and Children and Young Adults (NLSY79 CYA), to estimate the impact of mothers’ self-esteem on young children’s home environment qualities that enhance early childhood cognitive functioning and extend better emotional support. The estimates suggest that mothers with higher self-esteem provide better home environment to their children during early stages of childhood. The results are robust across different estimation methods, empirical specifications, and demographic groups. This study also finds that mothers with higher self-esteem are more likely to engage in parental practices that support young children’s cognitive and emotional development. Further analysis shows that mothers' self-esteem has a causal relationship with cognitive and behavioral outcomes of school-age children. The results obtained in this study indicate that early childhood development policies directed towards enhancement of non-cognitive skills in mothers can improve children’s human capital outcomes. / Economics

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The advent of social media has revolutionized human communication, significantly improving individuals' lives. It makes people closer to each other, provides access to enormous real-time information, and eases marketing and business. Despite its uncountable benefits, however, we must consider some of its negative implications such as online harassment and cyberbullying. Cyberbullying is becoming a serious, large-scale problem damaging people's online lives. This phenomenon is creating a need for automated, data-driven techniques for analyzing and detecting such behaviors. In this research, we aim to address the computational challenges associated with harassment-based cyberbullying detection in social media by developing machine-learning framework that only requires weak supervision. We propose a general framework that trains an ensemble of two learners in which each learner looks at the problem from a different perspective. One learner identifies bullying incidents by examining the language content in the message; another learner considers the social structure to discover bullying. Each learner is using different body of information, and the individual learner co-train one another to come to an agreement about the bullying concept. The models estimate whether each social interaction is bullying by optimizing an objective function that maximizes the consistency between these detectors. We first developed a model we referred to as participant-vocabulary consistency, which is an ensemble of two linear language-based and user-based models. The model is trained by providing a set of seed key-phrases that are indicative of bullying language. The results were promising, demonstrating its effectiveness and usefulness in recovering known bullying words, recognizing new bullying words, and discovering users involved in cyberbullying. We have extended this co-trained ensemble approach with two complementary goals: (1) using nonlinear embeddings as model families, (2) building a fair language-based detector. For the first goal, we incorporated the efficacy of distributed representations of words and nodes such as deep, nonlinear models. We represent words and users as low-dimensional vectors of real numbers as the input to language-based and user-based classifiers, respectively. The models are trained by optimizing an objective function that balances a co-training loss with a weak-supervision loss. Our experiments on Twitter, Ask.fm, and Instagram data show that deep ensembles outperform non-deep methods for weakly supervised harassment detection. For the second goal, we geared this research toward a very important topic in any online automated harassment detection: fairness against particular targeted groups including race, gender, religion, and sexual orientations. Our goal is to decrease the sensitivity of models to language describing particular social groups. We encourage the learning algorithm to avoid discrimination in the predictions by adding an unfairness penalty term to the objective function. We quantitatively and qualitatively evaluate the effectiveness of our proposed general framework on synthetic data and data from Twitter using post-hoc, crowdsourced annotation. In summary, this dissertation introduces a weakly supervised machine learning framework for harassment-based cyberbullying detection using both messages and user roles in social media. / Doctor of Philosophy / Social media has become an inevitable part of individuals social and business lives. Its benefits, however, come with various negative consequences such as online harassment, cyberbullying, hate speech, and online trolling especially among the younger population. According to the American Academy of Child and Adolescent Psychiatry,1 victims of bullying can suffer interference to social and emotional development and even be drawn to extreme behavior such as attempted suicide. Any widespread bullying enabled by technology represents a serious social health threat. In this research, we develop automated, data-driven methods for harassment-based cyberbullying detection. The availability of tools such as these can enable technologies that reduce the harm and toxicity created by these detrimental behaviors. Our general framework is based on consistency of two detectors that co-train one another. One learner identifies bullying incidents by examining the language content in the message; another learner considers social structure to discover bullying. When designing the general framework, we address three tasks: First, we use machine learning with weak supervision, which significantly alleviates the need for human experts to perform tedious data annotation. Second, we incorporate the efficacy of distributed representations of words and nodes such as deep, nonlinear models in the framework to improve the predictive power of models. Finally, we decrease the sensitivity of the framework to language describing particular social groups including race, gender, religion, and sexual orientation. This research represents important steps toward improving technological capability for automatic cyberbullying detection.

Yes / Smart cities are shifting the presence of people from physical world to cyber world (cyberspace). Along with the facilities for societies, the troubles of physical world, such as bullying, aggression and hate speech, are also taking their presence emphatically in cyberspace. This paper aims to dig the posts of social media to identify the bullying comments containing text as well as image. In this paper, we have proposed a unified representation of text and image together to eliminate the need for separate learning modules for image and text. A single-layer Convolutional Neural Network model is used with a unified representation. The major findings of this research are that the text represented as image is a better model to encode the information. We also found that single-layer Convolutional Neural Network is giving better results with two-dimensional representation. In the current scenario, we have used three layers of text and three layers of a colour image to represent the input that gives a recall of 74% of the bullying class with one layer of Convolutional Neural Network. / Ministry of Electronics and Information Technology (MeitY), Government of India