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Negative Information Measurement at AI Edge: A New Perspective for Mental Health Monitoring

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Published:22 January 2022Publication History
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Abstract

The outbreak of the corona virus disease 2019 (COVID-19) has caused serious harm to people’s physical and mental health. Due to the serious situation of the epidemic, a lot of negative energy information increases people’s psychological burden. However, effective interventions against mental health problems are not in abundance. To address such challenges, in this article, we propose the concept of negative information to describe information that has a negative impact on people’s mental health. To achieve the measurement of negative information, the level of mental health inversely measures the degree of negative information. Specifically, we design a system to measure the negative information used to monitor the mental health state of the user under the impact of negative information. The cognition of mental health is realized based on the intelligent algorithm deployed on the edge cloud, and the needs of users can be responded to in real time in practical applications. Finally, we use real collected dataset to verify the influence of negative information. The experiments show that the system can achieve negative information measurement and provide an effective countermeasure for solving mental health problems during a pandemic situation.

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          • Published in

            cover image ACM Transactions on Internet Technology
            ACM Transactions on Internet Technology  Volume 22, Issue 3
            August 2022
            631 pages
            ISSN:1533-5399
            EISSN:1557-6051
            DOI:10.1145/3498359
            • Editor:
            • Ling Liu
            Issue’s Table of Contents

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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            Publication History

            • Published: 22 January 2022
            • Accepted: 1 June 2021
            • Revised: 1 March 2021
            • Received: 1 October 2020
            Published in toit Volume 22, Issue 3

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