Detecting frames in news headlines and its application to analyzing news framing trends surrounding U.S. gun violence

Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as “frames”, which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news headlines related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. Our proposed approach sets a new state-of-the-art performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.Published versio

Mediated Communication and Customer Service Experiences: Psychological and Demographic Predictors of User Evaluations in the United States

People around the world who seek to interact with large organizations increasingly find they must do so via mediated and automated communication. Organizations often deploy both mediated and automated platforms, such as instant messaging and interactive voice response systems (IVRs), for efficiency and cost-savings. Customer and client responses to these systems range from delight to frustration. To better understand the factors affecting people’s satisfaction with these systems, we conducted a generally representative U.S. national survey (N = 1321). Here, we found that people still overwhelmingly like and trust in-person customer service over mediated and automated modalities. As to demographic attitude predictors, age was important (older respondents liked mediated systems less), but income and education were not strong attitude predictors. For personality variables, innovativeness was positively associated with mediated system satisfaction. But communication apprehensiveness, which we expected to be related to satisfaction, was not. We conclude by discussing implications for the burgeoning field of human-machine communication, as well as social policy, equity, and the pullulating digital services divide

Humanizing robots? The influence of appearance and status on social perceptions of robots

Social robots are a lesser known technology with uncertain but seemingly very powerful potential, which for decades has been portrayed in cultural artifacts as threats to human primacy. Research on people’s relationships to non-robotic technology, however, indicates that people will treat robots socially and assimilate them into their lives in ways that may disrupt existing norms but still fulfill a fundamental human need. Through the theoretical lenses of media equation and apparatgiest, this dissertation examines facets of robot humanization, defined as how people think of robots as social and human-like entities through perceptions of liking, human-likeness, and rights’ entitlement. In a 2 (gender) x 2 (physical humanness) x 3 (status) between-subjects online experiment, this dissertation explores the influence of fixed technological traits (the robot’s gender, physical humanness, and described status) and participants’ individual differences on humanization perceptions. Findings show that the robots’ features mattered less than participants’ individual traits, which explained the most variance in humanizing perceptions of social robots. Of those, participants’ prior robot exposure (both in real life and mediated) and efficacy traits were the strongest predictors of robot liking, perceived human-likeness, and perceptions of rights entitlement. Specifically, those with more real-life exposure and who perceived themselves as more technologically competent were more likely to humanize robots, while those with higher internal loci of control and negative mediated views of robots were less inclined to humanize robots. Theoretically, this study’s findings suggest that technological affordances matter less than the ontological understanding that social robots as a category may have in people’s humanizing perceptions. Looking forward, these findings indicate that there is an opportunity in the design of social robots to set precedents now that are prosocial and reflective of the world people strive for and want to inhabit in the future

Mediated Communication and Customer Service Experiences: Psychological and Demographic Predictors of User Evaluations in the United States

People around the world who seek to interact with large organisations increasingly find they must do so via mediated and automated communication. Organisations often deploy both mediated and automated platforms, such as instant messaging and interactive voice response systems (IVRs), for efficiency and cost-savings. Customer and client responses to these systems range from delight to frustration. To better understand the factors affecting people's satisfaction with these systems, we conducted a representative U.S. national survey (N = 1321). We found that people overwhelmingly like and trust in-person customer service compared to mediated and automated modalities. As to demographic attitude predictors, age was important (older respondents liked mediated systems less), but income and education were not strong attitude predictors. For personality variables, innovativeness was positively associated with mediated system satisfaction. However, communication apprehensiveness, which we expected to be related to satisfaction, was not. We conclude by discussing implications for the burgeoning field of human-machine communication, as well as social policy, equity, and the pullulating digital services divide

BUOCA: Budget-Optimized Crowd Worker Allocation

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing.First author draf

BUOCA: Budget-Optimized Crowd Worker Allocation

Due to concerns about human error in crowdsourcing, it is standard practice to collect labels for the same data point from multiple internet workers. We here show that the resulting budget can be used more effectively with a flexible worker assignment strategy that asks fewer workers to analyze easy-to-label data and more workers to analyze data that requires extra scrutiny. Our main contribution is to show how the allocations of the number of workers to a task can be computed optimally based on task features alone, without using worker profiles. Our target tasks are delineating cells in microscopy images and analyzing the sentiment toward the 2016 U.S. presidential candidates in tweets. We first propose an algorithm that computes budget-optimized crowd worker allocation (BUOCA). We next train a machine learning system (BUOCA-ML) that predicts an optimal number of crowd workers needed to maximize the accuracy of the labeling. We show that the computed allocation can yield large savings in the crowdsourcing budget (up to 49 percent points) while maintaining labeling accuracy. Finally, we envisage a human-machine system for performing budget-optimized data analysis at a scale beyond the feasibility of crowdsourcing

Qualitative Content and Discourse Analysis Comparing the Current Consent Systems for Deceased Organ Donation in Spain and England

England switched to an opt-out system of consent in 2020 aiming to increase the number of organs available. Spain also operates an opt-out system yet has almost twice the organ donations per million population compared with England. We aimed to identify both differences and similarities in the consent policies, documents and procedures in deceased donation between the two countries using comparative qualitative content and discourse analysis. Spain had simpler, locally tailored documents, the time taken for families to review and process information may be shorter, there were more pathways leading to organ donation in Spain, and more robust legal protections for the decisions individuals made in life. The language in the Spanish documents was one of support and reassurance. Documents in England by comparison appeared confusing, since additions were designed to protect the NHS against risk and made to previous document versions to reflect the law change rather than being entirely recast. If England's ambition is to achieve consent rates similar to Spain this analysis has highlighted opportunities that could strengthen the English system-by giving individuals' decisions recorded on the organ donor register legal weight, alongside unifying and simplifying consent policies and procedures to support families and healthcare professionals.</p

Mediated communication and customer service experiences

People around the world who seek to interact with large organisations increasingly find they must do so via mediated and automated communication. Organisations often deploy both mediated and automated platforms, such as instant messaging and interactive voice response systems (IVRs), for efficiency and cost-savings. Customer and client responses to these systems range from delight to frustration. To better understand the factors affecting people's satisfaction with these systems, we conducted a representative U.S. national survey (N = 1321). We found that people overwhelmingly like and trust in-person customer service compared to mediated and automated modalities. As to demographic attitude predictors, age was important (older respondents liked mediated systems less), but income and education were not strong attitude predictors. For personality variables, innovativeness was positively associated with mediated system satisfaction. However, communication apprehensiveness, which we expected to be related to satisfaction, was not. We conclude by discussing implications for the burgeoning field of human-machine communication, as well as social policy, equity, and the pullulating digital services divide.Published versio

Opening education through emerging technology: what are the prospects? Public perceptions of artificial intelligence and virtual reality in the classroom

Education technology (Edtech) is a booming industry based on its potential to transform education and learning outcomes. With concern over remote learning, there is renewed excitement about the visual component of Edtech, namely VR, along with artificial intelligence (AI), resulting in more significant investments and innovations. Despite industrial-scale investment in Edtech's diffusion, less is known about the public's view. The public's reception of these technologies, though, maybe necessary in determining the contours of their eventual utilization. Therefore, we conducted a mixed-methods analysis based on a survey of a representative sample of the US population (N=2,254) that explores perceptions of Edtech in two instantiations: AI and VR in education. Respondents were more accepting of VR as a teaching tool than AI taking on educational roles. Assistive AI was born over AI with decision-making responsibilities. Personality and experiential traits had an influence on respondents' openness to education technologies. The results suggest support for a blended model of AI and VR use in the classroom.http://opuseteducatio.hu/index.php/opusHU/article/view/41