Jie Ren

This study investigates the evolution of public sentiment and discourse surrounding artificial intelligence through a comprehensive multi-method analysis of 28,819 Reddit comments spanning March 2015 to May 2024. Addressing three research questions—(1) what dominant topics characterize AI discourse, (2) how has sentiment changed over time, particularly following ChatGPT 5.2’s release, and (3) what linguistic patterns distinguish positive from negative discourse—we employ 28 distinct analytical techniques to provide validated insights into public AI perception. Methodologically, the study integrates VADER sentiment analysis, Linguistic Inquiry and Word Count (LIWC) analysis with regression validation, dual topic modeling using Latent Dirichlet Allocation and Non-negative Matrix Factorization for cross-validation, four-dimensional tone analysis, named entity recognition, emotion detection, and advanced NLP techniques including sarcasm detection, stance classification, and toxicity analysis. A key methodological contribution is the validation of LIWC categories through linear regression (R² = 0.049, p < 0.001) and logistic regression (61% accuracy), moving beyond the descriptive statistics typical of prior linguistic analyses. Results reveal a pronounced decline in positive sentiment from +0.320 in 2015 to +0.053 in 2024. Contrary to expectations, sentiment decreased following ChatGPT’s November 2022 release, with negative comments increasing from 31.9% to 35.1%—suggesting that direct exposure to powerful AI capabilities intensifies rather than alleviates public concerns. LIWC regression analysis identified negative emotion words (β = −0.083) and positive emotion words (β = +0.063) as the strongest sentiment predictors, confirming that affective rather than technical engagement drives public AI attitudes. Topic modeling revealed nine coherent themes, with facial recognition, algorithmic bias, AI ethics, and social media misinformation emerging as dominant concerns across both LDA and NMF analyses. Network analysis identified regulation as a central hub (degree centrality = 0.929) connecting all major AI concerns, indicating strong public appetite for governance frameworks. These findings contribute to theoretical understandings of technology risk perception, provide practical guidance for AI developers and policymakers, and demonstrate validated computational methods for tracking public opinion toward emerging technologies.

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years.

We investigate the level of empathy mental health posts receive on social media and generative artificial intelligence (GenAI). Specifically, we examine gender effects to determine if posts authored by self-identified men, women, or unknown (no self-identified gender discloser) receive varying levels of empathy across different technical platforms. Using a sample of mental health posts from Reddit, we find that self-identified women receive more empathy relative to men across all platforms. We further find that Inflection Pi, a GenAI tool specifically designed to be empathetic, provides the most empathy, but it still favors self-identified women over men. Self-identified men attempting to receive empathy for their prolonged emotional distress are disadvantaged relative to self-identified women.

Understanding the increase in the local/global mobility of scientists is critical from academic/practical perspectives. This research study focuses on the “push/pull” factors that influence highly educated individuals to migrate to other countries. Using the Open Researcher and Contributor ID database, we examine the association between different aspects of culture and economic conditions as push/pull factors. By exploring over 6,000 doctoral researcher migration records from 194 countries, we investigate how the interaction between a migrant's home country characteristics (push factors) and immigrant country characteristics (pull factors) affects their migration. The findings show that migration behavior is correlated with the culture and income of immigrant countries. Immigrants are more likely to move to countries that are culturally similar to their home country. The high-income/individualistic nature of “sending” and “receiving” nations interacts with one another and motivates individual talents to migrate further. Policy implications include mitigating brain drain and encouraging skilled migration for innovation/economic growth.

This is an introduction to the 2024 HICSS minitrack on the Bright and Dark Side of Social Media in the Marginalized Contexts within the special track Information Technology, Social Justice, and Marginalized Context. In this minitrack, we seek to expose the dark side of social media in marginalized contexts, as well as to advance our understanding of how social media could be used to bring light to society in the marginalized contexts. This introduction provides an overview of this minitrack and the three papers accepted to the minitrack.

In our paper, we investigate the responses that individuals with mental illnesses receive on their posts on Reddit and ChatGPT. Using terror management theory (TMT), we propose that the level of empathy of the comments is a function of the mortality salience of the commenter and the technical platform. To test our proposed effects empirically, we extracted a sample of posts and their comments from the "mental illness" and "mental health" subreddits along with responses generated from those posts on ChatGPT. In our statistical analyses, we found a significant main effect for mortality salience in relation to empathy consistent with the TMT, but this effect was qualified by the technical platform. We found that higher mortality salience favored ChatGPT over Reddit and lower mortality salience had the opposite effect.

In our paper, we investigate the responses that individuals with mental illnesses receive on their posts on Reddit and ChatGPT. Using terror management theory (TMT), we propose that the level of empathy of the comments is a function of the mortality salience of the commenter and the technical platform. To test our proposed effects empirically, we extracted a sample of posts and their comments from the "mental illness" and "mental health" subreddits along with responses generated from those posts on ChatGPT. In our statistical analyses, we found a significant main effect for mortality salience in relation to empathy consistent with the TMT, but this effect was qualified by the technical platform. We found that higher mortality salience favored ChatGPT over Reddit and lower mortality salience had the opposite effect.

This is an introduction to the 2024 HICSS minitrack on the Bright and Dark Side of Social Media in the Marginalized Contexts within the special track Information Technology, Social Justice, and Marginalized Context. In this minitrack, we seek to expose the dark side of social media in marginalized contexts, as well as to advance our understanding of how social media could be used to bring light to society in the marginalized contexts. This introduction provides an overview of this minitrack and the three papers accepted to the minitrack.