Tracking Fluctuations in Psychological States using Social Media Language: A Case Study of Weekly Emotion

2020 ◽  
Vol 34 (5) ◽  
pp. 845-858
Author(s):  
Johannes C. Eichstaedt ◽  
Aaron C. Weidman
Keyword(s):  
Big Data ◽  
Big Five ◽  
Emotional Valence ◽  
Data Set ◽  
Personality Psychology ◽  
Aggregated Data ◽  
Psychological States ◽  
Final Data ◽  
Valence And Arousal

Personality psychologists are increasingly documenting dynamic, within–person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality–relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text–based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human–annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self–reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model–predicted emotion and self–reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology's evolution into a dynamic science. © 2020 European Association of Personality Psychology

scholarly journals Combining event- and variable-centred approaches to institution-facing learning analytics at the unit of study level

2017 ◽  
Vol 34 (1) ◽  
pp. 63-78 ◽  
Author(s):  
Nick Kelly ◽  
Maximiliano Montenegro ◽  
Carlos Gonzalez ◽  
Paula Clasing ◽  
Augusto Sandoval ◽  
...  
Keyword(s):  
Big Data ◽  
Learning Analytics ◽  
Process Mining ◽  
Approaches To Learning ◽  
Time Frame ◽  
Data Set ◽  
Content Type ◽  
Modelling Techniques ◽  
Level Of Understanding

Purpose The purpose of this paper is to demonstrate the utility of combining event-centred and variable-centred approaches when analysing big data for higher education institutions. It uses a large, university-wide data set to demonstrate the methodology for this analysis by using the case study method. It presents empirical findings about relationships between student behaviours in a learning management system (LMS) and the learning outcomes of students, and further explores these findings using process modelling techniques. Design/methodology/approach The paper describes a two-year study in a Chilean university, using big data from a LMS and from the central university database of student results and demographics. Descriptive statistics of LMS use in different years presents an overall picture of student use of the system. Process mining is described as an event-centred approach to give a deeper level of understanding of these findings. Findings The study found evidence to support the idea that instructors do not strongly influence student use of an LMS. It replicates existing studies to show that higher-performing students use an LMS differently from the lower-performing students. It shows the value of combining variable- and event-centred approaches to learning analytics. Research limitations/implications The study is limited by its institutional context, its two-year time frame and by its exploratory mode of investigation to create a case study. Practical implications The paper is useful for institutions in developing a methodology for using big data from a LMS to make use of event-centred approaches. Originality/value The paper is valuable in replicating and extending recent studies using event-centred approaches to analysis of learning data. The study here is on a larger scale than the existing studies (using a university-wide data set), in a novel context (Latin America), that provides a clear description for how and why the methodology should inform institutional approaches.

scholarly journals Visualization of big data security: a case study on the KDD99 cup data set

2017 ◽  
Vol 3 (4) ◽  
pp. 250-259 ◽  
Author(s):  
Zichan Ruan ◽  
Yuantian Miao ◽  
Lei Pan ◽  
Nicholas Patterson ◽  
Jun Zhang
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Set

How Big Does Big Data Need to Be?

2016 ◽  
pp. 1-12
Author(s):  
Martin Stange ◽  
Burkhardt Funk
Keyword(s):  
Big Data ◽  
Sample Size ◽  
Predictive Analytics ◽  
Optimal Sampling ◽  
Data Set ◽  
Benefit Cost

Collecting and storing of as many data as possible is common practice in many companies these days. To reduce costs of collecting and storing data that is not relevant, it is important to define which analytical questions are to be answered and how much data is needed to answer these questions. In this chapter, a process to define an optimal sampling size is proposed. Based on benefit/cost considerations, the authors show how to find the sample size that maximizes the utility of predictive analytics. By applying the proposed process to a case study is shown that only a very small fraction of the available data set is needed to make accurate predictions.

scholarly journals How Are Personality States Associated with Smartphone Data?

2020 ◽  
Vol 34 (5) ◽  
pp. 687-713
Author(s):  
Dominik Rüegger ◽  
Mirjam Stieger ◽  
Marcia Nißen ◽  
Mathias Allemand ◽  
Elgar Fleisch ◽  
...  
Keyword(s):  
Big Five ◽  
Ambient Noise ◽  
Psychological Research ◽  
Big Five Personality ◽  
Data Set ◽  
Personality Psychology ◽  
Smartphone Sensors ◽  
Self Assessments ◽  
Day Of The Week ◽  
State Responses

Smartphones promise great potential for personality science to study people's everyday life behaviours. Even though personality psychologists have become increasingly interested in the study of personality states, associations between smartphone data and personality states have not yet been investigated. This study provides a first step towards understanding how smartphones may be used for behavioural assessment of personality states. We explored the relationships between Big Five personality states and data from smartphone sensors and usage logs. On the basis of the existing literature, we first compiled a set of behavioural and situational indicators, which are potentially related to personality states. We then applied them on an experience sampling data set containing 5748 personality state responses that are self–assessments of 30 minutes timeframes and corresponding smartphone data. We used machine learning analyses to investigate the predictability of personality states from the set of indicators. The results showed that only for extraversion, smartphone data (specifically, ambient noise level) were informative beyond what could be predicted based on time and day of the week alone. The results point to continuing challenges in realizing the potential of smartphone data for psychological research. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology

scholarly journals Big Data as a Tool to Monitor and Deter Environmental Offenders in the Global South: A Multiple Case Study

2020 ◽  
Vol 12 (24) ◽  
pp. 10436
Author(s):  
Nir Kshetri ◽  
Diana Carolina Rojas Torres ◽  
Hany Besada ◽  
Maria Andreina Moros Ochoa
Keyword(s):  
Big Data ◽  
Satellite Imagery ◽  
Global South ◽  
Convincing Evidence ◽  
Policy Makers ◽  
Aggregated Data ◽  
Environmental Destruction ◽  
Multiple Case ◽  
Multiple Case Studies

While prior research has looked at big data’s role in strengthening the environmental justice movement, scholars rarely examine the contexts, mechanisms and processes associated with the use of big data in monitoring and deterring environmental offenders, especially in the Global South. As such, this research aims to substitute for this academic gap through the use of multiple case studies of environmental offenders’ engagement in illegal deforestation, as well as legal deforestation followed by fire. Specifically, we have chosen four cases from three economies in the Global South: Indonesia, Peru and Brazil. We demonstrate how the data utilized by environmental activists in these four cases qualify as true forms of big data, as they have searched and aggregated data from various sources and employed them to achieve their goals. The article shows how big data from various sources, mainly from satellite imagery, can help discern the true extent of environmental destruction caused by various offenders and present convincing evidence. The article also discusses how a rich satellite imagery archive is suitable for analyzing chronological events in order to establish a cause-effect chain. In all of the cases studied, such evidentiary provisions have been used by environmental activists to oblige policy makers to take necessary actions to counter environmental offenses.

Parent and Grandparent Relationships in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Emerging Adulthood ◽  
Attachment Theory ◽  
Emerging Adults ◽  
Family Relationships ◽  
Longitudinal Research ◽  
Study Data ◽  
Family Development ◽  
Data Set ◽  
Theory Framework

Chapter 7 begins with an overview of Erikson’s ideas about intimacy and its place in the life cycle, followed by a summary of Bowlby and Ainsworth’s attachment theory framework and its relation to family development. The authors review existing longitudinal research on the development of family relationships in adolescence and emerging adulthood, focusing on evidence with regard to links to McAdams and Pals’ personality model. They discuss the evidence, both questionnaire and narrative, from the Futures Study data set on family relationships, including emerging adults’ relations with parents and, separately, with grandparents, as well as their anticipations of their own parenthood. As a way of illustrating the key personality concepts from this family chapter, the authors end with a case study of Jane Fonda in youth and her father, Henry Fonda, to illustrate these issues through the lives of a 20th-century Hollywood dynasty of actors.

Vocational Development in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Turning Point ◽  
Life Stories ◽  
Life Story ◽  
Emerging Adult ◽  
Vocational Development ◽  
Data Set ◽  
The World ◽  
Key Concepts

Chapter 6 reviews research on the topic of vocational/occupational development in relation to the McAdams and Pals tripartite personality framework of traits, goals, and life stories. Distinctions between types of motivations for the work role (as a job, career, or calling) are particularly highlighted. The authors then turn to research from the Futures Study on work motivations and their links to personality traits, identity, generativity, and the life story, drawing on analyses and quotes from the data set. To illustrate the key concepts from this vocation chapter, the authors end with a case study on Charles Darwin’s pivotal turning point, his round-the-world voyage as naturalist for the HMS Beagle. Darwin was an emerging adult in his 20s at the time, and we highlight the role of this journey as a turning point in his adult vocational development.

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