Analysis of Social Network Users' Online Behavior from the Perspective of Psychology

2014 ◽  
Vol 22 (10) ◽  
pp. 1647 ◽  
Author(s):  
Qi YAO ◽  
Huawei MA ◽  
Huan YAN ◽  
Qi CHEN
Keyword(s):  
Social Network ◽  
Online Behavior

Online Multi-Contextual Analysis

2011 ◽  
pp. 542-554 ◽  
Author(s):  
Alyson Young ◽  
David Gurzick ◽  
Anabel Quan-Haase
Keyword(s):  
Social Network ◽  
Social Consequences ◽  
Contextual Analysis ◽  
Online Behavior ◽  
Network Site ◽  
Alternative Approach ◽  
Multi Method Approach ◽  
Methodological Practice ◽  
Method Approach ◽  
Level Of Analysis

This chapter proposes online multi-contextual analysis (OMCA) as a new multi-method approach for investigating and analyzing the behaviors, perceptions, and opinions of social network site (SNS) users. This approach is designed to extend methods currently available for the investigation of the use and social consequences of these sites with techniques that converge upon and triangulate users’ perceptions of their online behavior. Using quantitative measures of SNS usage, OMCA provides a much neglected level of analysis. We discuss current methodological practice in SNS research and introduce OMCA as an alternative approach. We then describe two studies that have employed OMCA to illustrate the method’s diversity and potential for providing new insights. Finally, we discuss the strengths and weaknesses of OMCA in comparison to single approaches and draw conclusions for theories of SNSs.

scholarly journals Indicators of Pre-Suicidal State of Minors in the Internet Space

2021 ◽  
Vol 11 (3) ◽  
pp. 47-61
Author(s):  
E.S. Ponomareva ◽  
V.V. Delibalt
Keyword(s):  
Social Network ◽  
Empirical Study ◽  
Online Communication ◽  
Well Being ◽  
Sentence Completion ◽  
The Internet ◽  
Cognitive Errors ◽  
Online Behavior ◽  
The Past ◽  
Child Questionnaire

The article aims to describe potential indicators of pre-suicidal state of minors manifesting in online-behavior that were revealed in the empirical study which involved 43 respondents aged 13-15 (M=14.19). The subjects for the study were selected among the teenagers who had responded to our advertisement messages published in various groups on the VKontakte social network. The following methodologies were used: UCLA Loneliness Scale (version 3) (D. Russell, L. A. Peplau, M. Ferguson); Beck Hopelessness Inventory; WHO-5 Well-being Index (1998); Cognitive Errors in Online Communication (I.S. Luchinkina); Sentence Completion for Events from the Past Test (SCEPT) (F.Raes et al.); Individual Typological Child Questionnaire (L.N. Sobchik). The data obtained lead to conclude about the characteristics of the profile related to presence of a distinct pre-suicidal condition: a tendency towards increase in the proportion between the number of subscriptions where suicide is mentioned (including those associated with corresponding emotions, as well communities collecting suicide-related art) and the total number of subscriptions. The number of comments on one's page is inversely proportional to the severity of one's pre-suicidal condition.

Psychological Profiles Prediction Using Online Social Network Behavior Data

2019 ◽  
pp. 75-87
Author(s):  
Nan Zhao ◽  
Tingshao Zhu
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Individual Differences ◽  
Predictive Accuracy ◽  
Daily Life ◽  
Online Social Network ◽  
Online Behavior ◽  
Huge Amount ◽  
Psychological Profiles ◽  
Data Source

As today's online social network (OSN) has become a part of our daily life, the huge amount of OSN behavior data could be a new data source to detect and understand individual differences, especially on mental aspects. Based on the findings revealing the relationships between personality and online behavior records, the authors tried to extract relevant features from both OSN usage behaviors and OSN textual posts, and trained models by machine learning methods to predict the OSN user's personality. The results showed fairly good predictive accuracy in Chinese OSN. The authors also reviewed the same kind of studies in more pervasive OSNs, focusing on what behavior data are used in predicting psychological profiles and how to use them effectively. It is foreseeable that more types of OSN data could be utilized in recognizing more psychological indices, and the predictive accuracy would be further improved. Meanwhile, the model-predicted psychological profiles are becoming an option of measurements in psychological studies, when the classical methods are not applicable.

scholarly journals Control your Facebook: An analysis of online privacy literacy

2020 ◽  
Author(s):  
Miriam Bartsch ◽  
Tobias Dienlin
Keyword(s):  
Social Network ◽  
Mediation Analysis ◽  
Personal Information ◽  
Social Network Sites ◽  
Online Privacy ◽  
Online Behavior ◽  
Online Questionnaire ◽  
Privacy Regulation ◽  
Privacy Settings ◽  
Per Se

For an effective and responsible communication on social network sites (SNSs) users must decide between withholding and disclosing personal information. For this so-called privacy regulation, users need to have the respective skills—in other words, they need to have online privacy literacy. In this study, we discuss factors that potentially contribute to and result from online privacy literacy. In an online questionnaire with 630 Facebook users, we found that people who spend more time on Facebook and who have changed their privacy settings more frequently reported to have more online privacy literacy. People with more online privacy literacy, in turn, felt more secure on Facebook and implemented more social privacy settings. A mediation analysis showed that time spend on Facebook and experience with privacy regulation did not per se increase safety and privacy behavior directly, stressing the importance of online privacy literacy as a mediator to a safe and privacy-enhancing online behavior. We conclude that Internet experience leads to more online privacy literacy, which fosters a more cautious privacy behavior on SNSs.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social

Abstract #1249: Novel Use of Physician Social Network Data to Map the Gap in the Endocrinology Workforce

2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban
Keyword(s):  
Social Network ◽  
Network Data ◽  
Social Network Data

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

Extraverts Have Larger Social Network Layers

2011 ◽  
Vol 32 (3) ◽  
pp. 161-169 ◽  
Author(s):  
Thomas V. Pollet ◽  
Sam G. B. Roberts ◽  
Robin I. M. Dunbar
Keyword(s):  
Social Network ◽  
Social Relationships ◽  
Outer Layer ◽  
Network Size ◽  
Emotional Intensity ◽  
Emotional Closeness ◽  
Network Layer ◽  
Network Layers ◽  
Social Network Size

Previous studies showed that extraversion influences social network size. However, it is unclear how extraversion affects the size of different layers of the network, and how extraversion relates to the emotional intensity of social relationships. We examined the relationships between extraversion, network size, and emotional closeness for 117 individuals. The results demonstrated that extraverts had larger networks at every layer (support clique, sympathy group, outer layer). The results were robust and were not attributable to potential confounds such as sex, though they were modest in size (raw correlations between extraversion and size of network layer, .20 < r < .23). However, extraverts were not emotionally closer to individuals in their network, even after controlling for network size. These results highlight the importance of considering not just social network size in relation to personality, but also the quality of relationships with network members.

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