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You can’t really ‘untag’: The Material Addressability of X and the Undermining of Journalistic Authority
Get PDFSocial media’s attention-based economy and design allegedly spur mistrust and attacks against media workers. X (formerly “Twitter”) facilitates a discursive climate that is divisive and polarized. We present research on platform-afforded tagging practices as infrastructures that enact ‘subtle’ undermining of journalistic authority through piggybacking on press-critique. X does not allow effective “untagging” from other people’s tweets. This creates material addressability, amplifying harassment. The case zooms in on repetitive questioning of whether BBC journalist Laura Kuenssberg was ‘at the party’, alluding to Partygate, a scandal about meetings at 10 Downing Street during Covid-19 lockdowns. Repurposing linkages between hashtagged and @-tagged tweets allowed us to map a network of critique, assembling a community of ‘supercharged critical thinkers.’ While the ‘repetitive drum of suspicion’ might seem benign, tagged tweets are embedded within a networked ecology of vitriol and misogyny through hashtags connecting creators to a wider network of distrust. Tagging controls are too rudimentary for journalists
Time to Close the Gender Gap? Field Experimental Evidence on Generative AI and Gender Gaps in IT Job Applications
Get PDFThe rapid emergence of generative artificial intelligence (GenAI) technologies has sparked debates about their potential to democratize technical work and reduce barriers to entry in IT careers. This study investigates whether possessing GenAI skills influences the gender gap in IT job applications through a field experiment on Upwork. Contrary to expectations that GenAI might level the playing field, we find preliminary evidence that GenAI actually widens the gender gap. While both men and women with GenAI skills show increased rates of applying for IT jobs, the effect is stronger for men, particularly among those with high GenAI proficiency. Our findings challenge optimistic narratives about GenAI’s democratizing potential and suggest that technological advances alone cannot address gender inequalities. These results alert policymakers about GenAI’s unintended consequence of widening gender gaps and inform the development of targeted interventions to mitigate inequalities
Introduction to the Minitrack on Data Analytics, Data Visualization, and Business Process Optimization
No full textPredicting Flight Delays Using Machine Learning
Get PDFFlight delays remain a persistent challenge for the aviation industry, generating high costs and disrupting operations across interconnected networks. Existing monitoring and scheduling tools provide valuable oversight but often lack predictive accuracy and cross-stakeholder coordination, limiting their effectiveness in disruption management. This study develops and evaluates an AI-enabled machine learning framework that integrates operational and meteorological data to forecast delays more reliably. Using U.S. domestic flight records and NOAA weather data for 2024, including a case study at Louisville Muhammad Ali International Airport, we apply classification and regression models to predict on-time performance and delay minutes. Ensemble methods, particularly Random Forest with SMOTE balancing, achieve superior results, detecting delayed flights with 94.7% accuracy and reducing mean absolute error in regression tasks to 4.79 minutes. Beyond technical gains, the framework demonstrates how AI-driven prediction can enhance collaborative decision-making by enabling shared situational awareness across airlines, airports, and air traffic control, strengthening resilience and efficiency in aviation operations
The Role of Sentiment Shift: Measuring and Explaining Performance of Fake News Detection After LLM Laundering
Get PDFWith their advanced capabilities, Large Language Models (LLMs) can generate highly convincing and contextually relevant fake news, which can contribute to disseminating misinformation. Though there is much research on fake news detection for human-written text, the field of detecting LLM-generated fake news is still under-explored. This paper augments existing datasets to measure the efficacy of detectors in identifying LLM paraphrased fake news. By investigating which models excel at which tasks (detection, paraphrasing to evade detection, and paraphrasing for semantic similarity), we found detectors struggled to detect LLM-paraphrased fake news more than human-written text. Further, upon inspecting LIME explanations, we observed a possible sentiment shift and digging deeper revealed a worrisome trend for paraphrase quality measurement: many samples exhibit sentiment shift despite a high BERTSCORE
Introduction to the Minitrack on Soft Computing: Theory Innovations and Problem-Solving Benefits
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Introduction to the Minitrack on Dark Sides and Criminal Uses of Digital and Intelligent Technologies
No full textInnovating with Publicly Available LLMs at Work: A Lifespan Perspective
Get PDFThis study examines how various facets of user’s age, i.e. chronological age, professional age and organizational tenure, shape the use of publicly available large language models chatbots (LLMs) at work. Adopting a lifespan perspective, we analyze how combinations of the different facets of age, alongside AI literacy and job autonomy explain extent and innovative LLM use. Using fsQCA on data from the U.S.-based professionals, we identify multiple equifinal pathways to LLMs use. The findings reveal that while chronological age plays a role, it does not operate in isolation. The extent of LLM use is more age-sensitive, with different pathways for younger and older employees, whereas innovative use is driven more by contextual and experiential conditions. Overall, younger workers require high AI literacy, autonomy and longer tenure, while older employees are driven by high AI literacy and shorter tenure. Our results contribute to the research on LMM use in the workplace by providing a nuanced understanding of the role of age