scholarly journals Computational personality assessment

2021 ◽  
Vol 2 ◽  
Author(s):  
Clemens Stachl ◽  
Ryan L. Boyd ◽  
Kai T. Horstmann ◽  
Poruz Khambatta ◽  
Sandra C. Matz ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Personality Assessment ◽  
Reliability And Validity ◽  
Ground Truth ◽  
Mobile Sensing ◽  
Objective Measures ◽  
Digital Revolution ◽  
Affect And Cognition ◽  
Digital Footprints

Computational methods have increased the objectivity of measures of human behavior and positioned personality science to benefit from the ongoing digital revolution. In this review, we define and discuss computational personality assessment (CPA), a measurement process that uses computational technologies to obtain estimates of personality. We briefly review some of the most promising sources of data currently used for CPA: mobile sensing, digital footprints from social media, images, language, and experience sampling. We present a concise overview of key findings, discuss the promise and opportunities of CPA (e.g., moving towards objective measures of personality, obtaining new insights from big data), and highlight important limitations and challenges in the development and application of CPA (e.g., establishing reliability and validity, selecting appropriate ground truth criterion, assessing affect and cognition, implications for ethics and privacy). We conclude with our perspective on how CPA could change our understanding of individual differences.

scholarly journals Computational Personality Assessment - An Overview and Perspective

2021 ◽  
Author(s):  
Clemens Stachl ◽  
Ryan L. Boyd ◽  
Kai Tobias Horstmann ◽  
Poruz Khambatta ◽  
Sandra Matz ◽  
...  
Keyword(s):  
Social Media ◽  
Human Behavior ◽  
Personality Assessment ◽  
Mobile Sensing ◽  
Digital Data ◽  
Objective Measures ◽  
Digital Revolution ◽  
Personality Research ◽  
Online Social Media ◽  
Practical Implications

Computational methods for the representation and analysis of data have drastically increased the objectivity, reliability, and the practical implications of research conducted throughout most scientific pursuits. Our rapidly-emerging potential to transform digital data into objective measures of human behavior, thoughts, and feelings has perfectly positioned personality science as a critical discipline that will benefit from today’s ongoing digital revolution. Here, we review and discuss some of the most promising approaches to computational personality assessment based on data from experience sampling, natural language, online social media, mobile sensing, and images. We present a concise overview of key findings, discuss the potential and promise ofcomputational personality assessment, and highlight important remaining questions in their development and application. We conclude with an optimistic outlook on how computational assessment could fuel the transition from personality research to personality science.

scholarly journals DIDŽIŲJŲ DUOMENŲ PANAUDOJIMAS SOCIALINĖJE MEDIJOJE / THE BIG DATA USE IN SOCIAL MEDIA

2019 ◽  
Vol 11 (0) ◽  
pp. 1-13
Author(s):  
Roberta Karpovičiūtė ◽  
Jolanta Sabaitytė
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analysis ◽  
Conceptual Model ◽  
Large Data ◽  
Decision Support Tool ◽  
Virtual Space ◽  
Management Tools ◽  
Support Tool ◽  
Digital Revolution

The digital revolution and the communication platforms provided by the web 2.0 virtual space era, such as social media, social networks, other tools and channels, create new opportunities for better marketing decisions based on user-generated data analysis. Every day customers of social media and other virtual tools are creating huge amounts of their actions caused data, and business lack management tools for the support of this process, which could create knowledge in the area of customer profiles and preferences deeper cognition. Growing numbers of social media users indicate the popularity of these communication tools among the information society, but science today lacks a deeper knowledge of social media generated data and other algorithms for this data usage. Therefore, the purpose of the article is defined as the development of the conceptual model of big data generated by social media usage in business. The formation of the conceptual model is based on the analysis of big data assumptions and application possibilities, social media classification peculiarities and different channel specifics, identification of big data analysis methods and analysis of large data applications generated by social media. The conceptual model creates preconditions for deeper knowledge of user-generated big data in nowadays widely used communication platforms, as well as creation of the decision support tool for marketing specialists in order to use big data from social media in deeper customer profile and preferences cognition. Methods employed in this research are: literature and other references analysis, synthesis and logical analysis of information, comparison of information, systemization and visualization.

Psychometric and Validity Issues in Machine Learning Approaches to Personality Assessment: A Focus on Social Media Text Mining

2020 ◽  
Vol 34 (5) ◽  
pp. 826-844 ◽  
Author(s):  
Louis Tay ◽  
Sang Eun Woo ◽  
Louis Hickman ◽  
Rachel M. Saef
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Text Mining ◽  
Personality Assessment ◽  
Ground Truth ◽  
Psychometric Validation ◽  
Learning Approaches ◽  
Text Data ◽  
Personality Psychology ◽  
Social Media Text

In the age of big data, substantial research is now moving toward using digital footprints like social media text data to assess personality. Nevertheless, there are concerns and questions regarding the psychometric and validity evidence of such approaches. We seek to address this issue by focusing on social media text data and (i) conducting a review of psychometric validation efforts in social media text mining (SMTM) for personality assessment and discussing additional work that needs to be done; (ii) considering additional validity issues from the standpoint of reference (i.e. ‘ground truth’) and causality (i.e. how personality determines variations in scores derived from SMTM); and (iii) discussing the unique issues of generalizability when validating SMTM for personality assessment across different social media platforms and populations. In doing so, we explicate the key validity and validation issues that need to be considered as a field to advance SMTM for personality assessment, and, more generally, machine learning personality assessment methods. © 2020 European Association of Personality Psychology

DYNAMIC EXTRACTION AND ANALYTICS OF BIG DATA FROM CLOUD AND SOCIAL MEDIA INTEGRATED PLATFORMS

2020 ◽  
Vol 9 (6) ◽  
pp. 3703-3711
Author(s):  
N. Oberoi ◽  
S. Sachdeva ◽  
P. Garg ◽  
R. Walia
Keyword(s):  
Social Media ◽  
Big Data ◽  
Dynamic Extraction

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

Indigenous language newspapers and the digital media conundrum in Africa

2021 ◽  
pp. 026666692098340
Author(s):  
Kevin Onyenankeya
Keyword(s):  
Social Media ◽  
Digital Media ◽  
Digital Transformation ◽  
Qualitative Approach ◽  
Indigenous Language ◽  
Technological Determinism ◽  
Traditional Media ◽  
Digital Revolution ◽  
The Future ◽  
Major Factors

The future of journalism is being shaped by the convergence of technology and societal shifts. For indigenous language press in Africa battling to stay afloat amidst stiff competition from traditional media, the pervasive and rapidly encroaching digital transformation holds both opportunities and potential threats. Using a qualitative approach, this paper examined the implication of the shift to digital media for the future of the indigenous language newspaper in Africa and identifies opportunities for its sustainability within the framework of the theories of technological determinism and alternative media. The analysis indicates poor funding, shrinking patronage, and competition from traditional and social media as the major factors facing indigenous newspapers. It emerged that for indigenous language newspapers to thrive in the rapidly changing and technology-driven world they need to not only adapt to the digital revolution but also explore a business model that combines a futuristic outlook with a practical approach.

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