Literature DB >> 34248317

Considerations for the Ethical Implementation of Psychological Assessment Through Social Media via Machine Learning.

Megan N Fleming1.   

Abstract

The ubiquity of social media usage has led to exciting new technologies such as machine learning. Machine learning is poised to change many fields of health, including psychology. The wealth of information provided by each social media user in combination with machine learning technologies may pave the way for automated psychological assessment and diagnosis. Assessment of individuals' social media profiles using machine learning technologies for diagnosis and screening confers many benefits (i.e., time and cost efficiency, reduced recall bias, information about an individual's emotions and functioning spanning months or years, etc.); however the implementation of these technologies will pose unique challenges to the professional ethics of psychology. Namely, psychologists must understand the impact of these assessment technologies on privacy and confidentiality, informed consent, recordkeeping, bases for assessments, and diversity and justice. This paper offers a brief review of the current applications of machine learning technologies in psychology and public health, provides an overview of potential implementations in clinical settings, and introduces ethical considerations for professional psychologists. This paper presents considerations which may aid in the extension of the current Ethical Principles of Psychologists and Code of Conduct to address these important technological advancements in the field of clinical psychology.

Entities:  

Keywords:  Ethical Principles of Psychologists; automated assessment; machine learning in psychology; psychology; social media

Year:  2020        PMID: 34248317      PMCID: PMC8261642          DOI: 10.1080/10508422.2020.1817026

Source DB:  PubMed          Journal:  Ethics Behav        ISSN: 1050-8422


  15 in total

1.  How accurate is recall of key symptoms of depression? A comparison of recall and longitudinal reports.

Authors:  J Elisabeth Wells; L John Horwood
Journal:  Psychol Med       Date:  2004-08       Impact factor: 7.723

2.  Record keeping guidelines.

Authors: 
Journal:  Am Psychol       Date:  2007-12

3.  Tracking suicide risk factors through Twitter in the US.

Authors:  Jared Jashinsky; Scott H Burton; Carl L Hanson; Josh West; Christophe Giraud-Carrier; Michael D Barnes; Trenton Argyle
Journal:  Crisis       Date:  2014

4.  Toward Automating HIV Identification: Machine Learning for Rapid Identification of HIV-Related Social Media Data.

Authors:  Sean D Young; Wenchao Yu; Wei Wang
Journal:  J Acquir Immune Defic Syndr       Date:  2017-02-01       Impact factor: 3.731

Review 5.  Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

Authors:  David C Mohr; Mi Zhang; Stephen M Schueller
Journal:  Annu Rev Clin Psychol       Date:  2017-03-17       Impact factor: 18.561

6.  Clinical assessment of affective instability: comparing EMA indices, questionnaire reports, and retrospective recall.

Authors:  Marika B Solhan; Timothy J Trull; Seungmin Jahng; Phillip K Wood
Journal:  Psychol Assess       Date:  2009-09

7.  The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic.

Authors:  Alessio Signorini; Alberto Maria Segre; Philip M Polgreen
Journal:  PLoS One       Date:  2011-05-04       Impact factor: 3.240

8.  Sharing feelings online: studying emotional well-being via automated text analysis of Facebook posts.

Authors:  Michele Settanni; Davide Marengo
Journal:  Front Psychol       Date:  2015-07-23

9.  Activities on Facebook reveal the depressive state of users.

Authors:  Sungkyu Park; Sang Won Lee; Jinah Kwak; Meeyoung Cha; Bumseok Jeong
Journal:  J Med Internet Res       Date:  2013-10-01       Impact factor: 5.428

10.  Forecasting the onset and course of mental illness with Twitter data.

Authors:  Andrew G Reece; Andrew J Reagan; Katharina L M Lix; Peter Sheridan Dodds; Christopher M Danforth; Ellen J Langer
Journal:  Sci Rep       Date:  2017-10-11       Impact factor: 4.379

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  1 in total

1.  Cross-evaluation of social mining for classification of depressed online personas.

Authors:  Alina Trifan; José Luis Oliveira
Journal:  J Integr Bioinform       Date:  2021-05-20
  1 in total

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