This project investigates how conversational power and discursive influence manifest across diverse Reddit communities, focusing on the dynamics among dominant and marginalized users, themes, and rhetorical styles. Analyzing 2,250 posts and 142,514 comments from 50 subreddits ranging from social judgment (r/AmItheAsshole) to political debate (r/politics), the study employs network science, natural language processing, sentiment analysis, and community detection algorithms to reveal patterns of power distribution and emotional polarization. Using the Python Reddit API Wrapper (PRAW) for data collection, the research constructs directed user interaction graphs to compute centrality measures. Findings show varied conversational structures: centralized networks in r/relationships contrast with egalitarian patterns in r/Feminism. Community detection identifies distinct subcommunities, with r/politics demonstrating ideological diversity, while r/MensRights reflects echo-chamber dynamics. Sentiment analysis reveals political subreddits have positive sentiment among central users, while r/MensRights exhibits negative sentiment. Notably, weak correlations between network influence and sentiment suggest that dominance does not reliably predict emotional tone. Topic modeling indicates that contentious issues evoke consistently negative sentiment, and the PrivilegeAnalyzer assesses how structural positions impact comment reception. Comparative analysis shows r/relationships has high structural centralization, whereas r/AmItheAsshole displays distributed influence. The research concludes that Reddit communities range from egalitarian to hierarchical, with influence linked to specific emotional styles, noting that highly emotional posts often come from peripheral users. This study innovates by combining network analysis with NLP to explore online power dynamics, providing insights for platform designers and researchers examining social hierarchies in digital spaces.

This study investigates ethical concerns in automated content moderation, specifically focusing on algorithmic bias in hate speech detection systems. Using the Davidson et al. (2017) Twitter dataset with 24,783 annotated tweets, it examines how class imbalance and culturally specific language affect model performance, particularly resulting in the misclassification of content from African-American users. The dataset reveals significant class imbalance: 19,190 tweets are labeled as "Offensive," 4,163 as "Neither," and only 1,430 as "Hate Speech." Text preprocessing included standard techniques like lowercase conversion, mention removal, and lemmatization. A Bidirectional LSTM-GRU hybrid neural network architecture was employed, combining RandomOverSampler for minority class augmentation and class weight balancing to address imbalances. Exploratory data analysis highlighted unique textual patterns, with offensive language featuring culturally specific slang often misclassified by conventional models. Despite achieving reasonable overall accuracy, the model demonstrated persistent bias toward predicting the dominant "Offensive" class, even for ambiguous content. Testing of phrases showed benign statements were sometimes incorrectly skewed toward offensive categorization. Findings indicate that model bias arises not only from data distribution but also from how abusive language is socially interpreted, particularly affecting African-American Vernacular English (AAVE). The study underscores the need for nuanced content classification approaches that consider linguistic diversity and cultural context, rather than relying solely on methods like oversampling or class weighting, which fail to address inherent annotation biases in training datasets. This research contributes to the discourse on algorithmic fairness, highlighting critical questions about representation and the consequences of automated hate speech detection in online spaces.

Food waste represents a significant environmental, economic, and social challenge. Despite awareness of its negative impacts, a gap persists between individuals' intentions to reduce food waste and their actual behaviors. This study investigated the influence of individual behavioral factors (subjective norms, perceived behavioral control, habit formation, and positive reinforcement) on food waste reduce intention. It further examined the mediating roles of resource scarcity and competing priorities in this relationship. Robust OLS regression analysis on data from self-reported survey from UK with sample size of 742 revealed that all examined behavioral factors exhibited significant positive direct effects on food waste reduction intention. However, the partial mediation analysis highlighted a crucial distinction - competing priorities emerged as a more substantial mediator compared to lack of resource opportunities. Specifically, competing priorities mediated 33.4% of the effect of subjective norms, 11.2% for perceived behavioral control, 27.5% for habit formation, and 20.7% for positive reinforcement on food waste concern. In contrast, lack of resource opportunities only mediated 2.8%, 1.8%, 2.1%, and 1.8% of these effects respectively. This finding suggests individuals under pressure of managing multiple demands may deprioritize food waste reduction despite intentions. Demographic factors like gender, race, and age also significantly influenced food waste reduction intention. The study underscores adopting comprehensive interventions targeting psychological factors and contextual barriers like competing priorities to foster sustainable food waste reduction behaviors. Addressing priority management alongside promoting food waste as a salient concern and mitigating resource limitations could bridge the intention-behavior gap.

This research proposal presents a comprehensive investigation into the differential experiences of immigrant and non-immigrant delivery workers in New York City's platform economy, with particular attention to health and safety risks and perceptions of corporate responsibility. The study addresses a critical gap in understanding how algorithmic management and precarious employment conditions disproportionately impact marginalized populations, particularly immigrant workers who have become increasingly reliant on app-based delivery platforms such as Uber Eats, DoorDash, and Grubhub. Drawing on theoretical frameworks of algorithmic despotism, racialized economic marginalization, and intersectional vulnerability, this proposal argues that the platform labor paradigm exacerbates existing inequalities by curtailing workers' control over fundamental rights while denying them access to basic labor protections and social safety nets. The research employs a mixed-methods design combining quantitative survey analysis with qualitative in-depth interviews to capture both the statistical patterns and lived experiences of platform workers. The quantitative component will utilize structured questionnaires with validated scales measuring perceived health risks, job satisfaction, workplace safety, and perceptions of platform accountability, employing stratified sampling to ensure proportional representation across delivery platforms, neighborhoods, and demographic characteristics. Statistical analyses including t-tests, ANOVA, and regression models will identify significant differences between immigrant and non-immigrant workers. The qualitative component will conduct semi-structured interviews using purposive sampling techniques to explore workers' challenges, coping strategies, and recommendations for systemic change. Literature review reveals that immigrant delivery workers face compounded vulnerabilities due to language barriers, non-recognition of foreign credentials, restricted access to public benefits, immigration-related fears, and dependence on gig work as primary income rather than supplemental earnings. The study's findings will inform targeted policy interventions including enforceable legal frameworks mandating algorithmic transparency, portable benefits uncoupled from employment classification, guaranteed minimum wages for all workers, and accessible skills training programs to enhance digital literacy and transferable vocational capabilities. By amplifying marginalized voices and documenting the intersection of economic precarity, racial inequality, and immigration status within platform labor, this research contributes to ongoing discourse surrounding ethical technology integration in labor practices and the future of work. The anticipated outcomes will provide empirical evidence to support collaborative efforts among immigrant communities, labor organizations, policymakers, and advocacy groups in promoting greater transparency, accountability, and collective bargaining power, ultimately fostering a more equitable and sustainable platform economy that protects worker dignity, health, and economic security.

This study investigates the complex relationship between digital identity and authenticity by examining how platform anonymity influences self-expression across different social media environments. Through quantitative analysis of 65 participants using convenience sampling, the research compares user behaviors and attitudes on anonymous platforms (Reddit, 4-Chan, Whisper) versus non-anonymous platforms (Instagram, TikTok, Facebook). The study tests four hypotheses examining social constraints on authenticity, the impact of anonymity on honest self-expression, gender differences in platform usage, and advice-seeking behaviors across platform types. The findings reveal that a significant majority (69.23%) of participants report constraining their self-expression due to concerns about social judgment, with this effect more pronounced among female respondents (76%) compared to male respondents (62%). Furthermore, 60% of participants indicated they would express themselves more honestly on completely anonymous platforms, compared to only 30.77% on platforms displaying usernames, demonstrating that anonymity substantially facilitates authentic self-disclosure. Gender analysis revealed unexpected patterns, with 56.92% of participants being female, contradicting initial hypotheses about male predominance in anonymous platform usage. Importantly, the research uncovered distinct patterns in advice-seeking behavior: approximately 48% of females and 31% of males prefer anonymous platforms for personal issues, while preferences shift toward non-anonymous platforms for work and social matters, with 29% of females and 14% of males choosing identifiable platforms for these contexts. These findings illuminate the critical role of perceived privacy and social context in determining platform choice and self-presentation strategies. The study demonstrates that social constraints significantly impede authentic self-expression on digital platforms, while anonymity serves as a liberating force that enables individuals to express their genuine thoughts and concerns without fear of judgment or social repercussions. The research underscores the importance of designing digital environments that balance anonymity with user safety, recognize gender-specific needs for privacy in personal matters, and accommodate the varying levels of disclosure comfort across different types of issues. These insights have substantial implications for platform designers, mental health professionals, and researchers seeking to understand the evolving dynamics of digital identity construction and authentic self-expression in contemporary online spaces. The study acknowledges limitations inherent in convenience sampling and calls for further research with more balanced, randomized samples to deepen understanding of how digital platform architecture influences human authenticity and well-being.

This essay explores the complex relationship of authentic identities in contemporary cyberspace, examining how anonymous digital platforms influence self-actualization and identity formation. Drawing upon the theoretical frameworks of Pierre Bourdieu's concept of habitus, Michel Foucault's technologies of the self, and Emile Durkheim's theories of social integration and solidarity, this study investigates the extent to which anonymous virtual identities align with authentic selves and how this alignment influences both perceived and actual identity. The research demonstrates that anonymous online spaces can provide liberation from the constraints of habitus, allowing individuals to explore repressed aspects of their subjectivity that diverge from societal expectations ingrained through their socialization. Through the lens of Foucault's technologies of the self, the paper examines how individuals voluntarily engage in digital identity experimentation as a form of self-fashioning, using anonymous forums to safely explore various facets of identity without real-world consequences. Furthermore, applying Durkheim's framework, the study reveals how online communities foster social solidarity and belonging, particularly for those experiencing fragmented identities or social isolation in physical spaces. The paper acknowledges both the empowering potential of virtual identity exploration such as enhanced self-knowledge, expanded cultural recognition, increased life satisfaction, and the potential risks, including deindividuation, escapism, and distorted self-perception. Ultimately, this research argues that when approached with moderation and balanced alongside real-world responsibilities, anonymous virtual identity play can contribute meaningfully to authentic self-actualization by offering alternative avenues for self-expression, community connection, and personal growth that transcend conventional offline limitations.