Haoning Xue

Haoning Xue, Brian Nishimine, Martin Hilbert et al.

The prevalence of short form video platforms, combined with the ineffectiveness of age verification mechanisms, raises concerns about the potential harms facing children and teenagers in an algorithm-moderated online environment. We conducted multimodal feature analysis and thematic topic modeling of 4,492 short videos recommended to children and teenagers on Instagram Reels, TikTok, and YouTube Shorts, collected as a part of an algorithm auditing experiment. This feature-level and content-level analysis revealed that unsafe (i.e., problematic, mentally distressing) short videos (a) possess darker visual features and (b) contain explicitly harmful content and implicit harm from anxiety-inducing ordinary content. We introduce a useful framework of online harm (i.e., explicit, implicit, unintended), providing a unique lens for understanding the dynamic, multifaceted online risks facing children and teenagers. The findings highlight the importance of protecting younger audiences in critical developmental stages from both explicit and implicit risks on social media, calling for nuanced content moderation, age verification, and platform regulation.

Haoning Xue, Jingwen Zhang, Xiaohui Wang et al.

The contemporary media landscape is characterized by sensational short videos. While prior research examines the effects of individual multimodal features, the collective impact of multimodal features on viewer engagement with short videos remains unknown. Grounded in the theoretical framework of Message Sensation Value (MSV), this study develops and tests a computational model of MSV with multimodal feature analysis and human evaluation of 1,200 short videos. This model that predicts sensory and behavioral engagement was further validated across two unseen datasets from three short video platforms (combined N = 14,492). While MSV is positively associated with sensory engagement, it shows an inverted U-shaped relationship with behavioral engagement: Higher MSV elicits stronger sensory stimulation, but moderate MSV optimizes behavioral engagement. This research advances the theoretical understanding of short video engagement and introduces a robust computational tool for short video research.

Haoning Xue, Yoo Jung Oh, Xinyi Zhou et al.

Conversational AI, such as ChatGPT, is increasingly used for information seeking. However, little is known about how ordinary users actually prompt and how ChatGPT adapts its responses in real-world conversational information seeking (CIS). In this study, a nationally representative sample of 937 U.S. adults engaged in multi-turn CIS with ChatGPT on both controversial and non-controversial topics across science, health, and policy contexts. We analyzed both user prompting strategies and the communication styles of ChatGPT responses. The findings revealed behavioral signals of digital divide: only 19.1% of users employed prompting strategies, and these users were disproportionately more educated and Democrat-leaning. Further, ChatGPT demonstrated contextual adaptation: responses to controversial topics contain more cognitive complexity and more external references than to non-controversial topics. Notably, cognitively complex responses were perceived as less favorable but produced more positive issue-relevant attitudes. This study highlights disparities in user prompting behaviors and shows how user prompts and AI responses together shape information-seeking with conversational AI.

Haoning Xue, Yue Li, Benjamin A. Lyons et al.

Make America Healthy Again (MAHA) is a health-related campaign slogan proposed by Robert F. Kennedy Jr. and later incorporated into the political coalition of President Trump. While #MAHA quickly circulated beyond the campaign itself and became a prominent hashtag for public discussion, it remains unclear whether this public discourse reflected, reshaped, or diverged from the stated agenda of the MAHA campaign. This study presents a large-scale, cross-platform analysis of early #MAHA public discourse between September 2024 and January 2025, using the framework of Agenda-Melding Theory. Drawing on 41,819 #MAHA-related posts, this study combines structural topic modeling, interrupted time-series analysis, and AI-assisted data annotation to examine the thematic structure and temporal dynamics. The most prominent finding is the substantial disconnect between #MAHA public discourse and the stated MAHA agenda: 81.3% of posts did not engage any of the five campaign priorities of the MAHA campaign. There were also pronounced cross-platform differences, with online platforms clustering into three broad discourse environments: (a) grassroots partisan-support spaces, (b) informational sources, and (c) health-focused spaces. #MAHA functioned less as a unified campaign agenda than as a symbolic frame interpreted differently across platforms. More broadly, this study provides useful empirical insight into how campaign slogans are reinterpreted and how public agendas are formed, amplified, and transformed in the fragmented digital environments.