1,721,106 research outputs found
Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study
Full text linkBackground: As societies become more complex, larger populations suffer from insomnia In 2014, the US Centers for Disease Control and Prevention declared that sleep disorders should be dealt with as a public health epidemic. However, it is hard to provide adequate treatment for each insomnia sufferer, since various behavioral characteristics influence symptoms of insomnia collectively. Objective: We aim to develop a neural-net based unsupervised user clustering method towards insomnia sufferers in order to clarify the unique traits for each derived groups. Unlike the current diagnosis of insomnia that requires qualitative analysis from interview results, the classification of individuals with insomnia by using various information modalities from smart bands and neural-nets can provide better insight into insomnia treatments. Methods: This study, as part of the precision psychiatry initiative, is based on a smart band experiment conducted over 6 weeks on individuals with insomnia. During the experiment period, a total of 42 participants (19 male; average age 22.00 [SD 2.79]) from a large university wore smart bands 24/7, and 3 modalities were collected and examined: sleep patterns, daily activities, and personal demographics. We considered the consecutive daily information as a form of images, learned the latent variables of the images via a convolutional autoencoder (CAE), and clustered and labeled the input images based on the derived features. We then converted consecutive daily information into a sequence of the labels for each subject and finally clustered the people with insomnia based on their predominant labels. Results: Our method identified 5 new insomnia-activity clusters of participants that conventional methods have not recognized, and significant differences in sleep and behavioral characteristics were shown among groups (analysis of variance on rank: F-4,F-37 =2.36, P=.07 for the sleep_min feature; F-4,F-37 =9.05, P<.001 for sleep_efficiency; F-4,F-37 =8.16, P<.001 for active_calorie; F-4,F-37 =6.53, P<.001 for walks; and F-4,F-37 =3.51, P=.02 for stairs). Analyzing the consecutive data through a CAE and clustering could reveal intricate connections between insomnia and various everyday activity markers. Conclusions: Our research suggests that unsupervised learning allows health practitioners to devise precise and tailored interventions at the level of data-guided user clusters (ie, precision psychiatry), which could be a novel solution to treating insomnia and other mental disorders. ©Sungkyu Park, Sang Won Lee, Sungwon Han, Meeyoung Cha.11Nsciescopu
Automatically Detecting Image–Text Mismatch on Instagram with Deep Learning
No full text© 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.Visual social media have emerged as an essential brand communication channel for advertisers and brands. The active use of hashtags has enabled advertisers to identify customers interested in their brands and better understand their consumers. However, some users post brand-incongruent content—for example, posts composed of brand-irrelevant images with brand-relevant hashtags. Such visual information mismatch can be problematic because it hinders other consumers’ information search processes and advertisers’ insight generation from consumer-initiated social media data. This study aims to characterize visually mismatched content in brand-related posts on Instagram and builds a visual information mismatch detection model using computer vision. We propose a machine-learning model based on three cues: image, text, and metadata. Our analysis shows the effectiveness of deep learning and the importance of combining text and image features for mismatch detection. We discuss the advantages of machine-learning methods as a novel research tool for advertising research and conclude with implications of our findings.11Nssciscopu
Others Are to Blame: Whom People Consider Responsible for Online Misinformation
No full text© 2022 ACM.Determining who is responsible for online misinformation is an important problem. This research offers a multifaceted view of the public's perception of who is responsible for online misinformation. Via two studies, we surveyed how people attribute responsibility separately for creating, disseminating, and failing to prevent the dissemination of false information online. Study 1 (N=99) employed a mixed-methods approach to identify a series of actors deemed responsible for each aspect of misinformation. Its open-ended methodology suggested that participants tended to externalize responsibility, which we explored further in the subsequent study. Study 2 (N=496) found that the responsible entities differed for the three distinct aspects of misinformation: online users, news media, and interest groups were associated with creating falsehoods, whereas social media platforms were predominantly seen as accountable for failing to prevent them. Our data shows that blame was directed towards those on the opposite side of the political spectrum, indicating substantial polarization. Most critically, people did not seem to associate themselves with online misinformation and externalized responsibility towards "other users."We discuss implications, including the need to promote personal accountability among users and the social demand for accountable social media platforms and news media.11Nscopu
sj-pdf-1-jou-10.1177_14648849211069241 – Supplemental Material for News comment sections and online echo chambers: The ideological alignment between partisan news stories and their user comments
No full textSupplemental Material, sj-pdf-1-jou-10.1177_14648849211069241 for News comment sections and online echo chambers: The ideological alignment between partisan news stories and their user comments by Jiyoung Han, Youngin Lee, Junbum Lee and Meeyoung Cha in Journalism</p
The Conflict Between People’s Urge to Punish AI and Legal Systems
No full textRegulating artificial intelligence (AI) has become necessary in light of its deployment in high-risk scenarios. This paper explores the proposal to extend legal personhood to AI and robots, which had not yet been examined through the lens of the general public. We present two studies (N = 3,559) to obtain people’s views of electronic legal personhood vis-à-vis existing liability models. Our study reveals people’s desire to punish automated agents even though these entities are not recognized any mental state. Furthermore, people did not believe automated agents’ punishment would fulfill deterrence nor retribution and were unwilling to grant them legal punishment preconditions, namely physical independence and assets. Collectively, these findings suggest a conflict between the desire to punish automated agents and its perceived impracticability. We conclude by discussing how future design and legal decisions may influence how the public reacts to automated agents’ wrongdoings.11Nscopu
On the Social-Relational Moral Standing of AI: An Empirical Study Using AI-Generated Art
No full textThe moral standing of robots and artificial intelligence (AI) systems has become a widely debated topic by normative research. This discussion, however, has primarily focused on those systems developed for social functions, e.g., social robots. Given the increasing interdependence of society with nonsocial machines, examining how existing normative claims could be extended to specific disrupted sectors, such as the art industry, has become imperative. Inspired by the proposals to ground machines’ moral status on social relations advanced by Gunkel and Coeckelbergh, this research presents online experiments (∑N = 448) that test whether and how interacting with AI-generated art affects the perceived moral standing of its creator, i.e., the AI-generative system. Our results indicate that assessing an AI system’s lack of mind could influence how people subsequently evaluate AI-generated art. We also find that the overvaluation of AI-generated images could negatively affect their creator’s perceived agency. Our experiments, however, did not suggest that interacting with AI-generated art has any significant effect on the perceived moral standing of the machine. These findings reveal that social-relational approaches to AI rights could be intertwined with property-based theses of moral standing. We shed light on how empirical studies can contribute to the AI and robot rights debate by revealing the public perception of this issue.11Nscopu
QAnon shifts into the mainstream, remains a far-right ally
No full text© 2022The rise of domestic fringe groups within the United States has been well documented, threatening political and social stability. The QAnon conspiracy theory has developed as one such destructive group, though it remains a largely misunderstood movement. Through a mixed-methods analysis of over 3.5 million messages on Telegram from three politically extreme communities - QAnon, far-right, and far-left - we studied how QAnon fits within the larger non-mainstream political ecosystem. Our analysis provides insights into how this new political movement is dissimilar to the far-right or the far-left but shares offline interests with the far-right. The topics discussed within QAnon communities were unique to the movement and the least reactive to news cycles. Links shared by QAnon, particularly from YouTube and Twitter, were often from traditional conservative sources and individuals, whereas the far-left and far-right relied on less mainstream sources. Finally, though QAnon may be distinct from the other communities, it coalesces with the far-right during particular political events where the former United States President Trump is a major player. Our findings highlight how fringe groups react to major political events and navigate conversations online.11Nsciescopu
Understanding and identifying the use of emotes in toxic chat on Twitch
No full textThe latest advances in NLP (natural language processing) have led to the launch of the much needed machine-driven toxic chat detection. Nevertheless, people continuously find new forms of hateful expressions that are easily identified by humans, but not by machines. One such common expression is the mix of text and emotes, a type of visual toxic chat that is increasingly used to evade algorithmic moderation and a trend that is an under-studied aspect of the problem of online toxicity. This research analyzes chat conversations from the popular streaming platform Twitch to understand the varied types of visual toxic chat. Emotes were sometimes used to replace a letter, seek attention, or for emotional expression. We created a labeled dataset that contains 29,721 cases of emotes replacing letters. Based on the dataset, we built a neural network classifier and identified visual toxic chat that would otherwise be undetected through traditional methods and caught an additional 1.3% examples of toxic chat out of 15 million chat utterances.11Nscopu
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