Display and control in online social spaces: Towards a typology of users

2016 ◽  
Vol 20 (3) ◽  
pp. 845-861 ◽  
Author(s):  
Alexandre Fortier ◽  
Jacquelyn Burkell
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Q Methodology ◽  
Personal Information ◽  
Public Spaces ◽  
Information Control ◽  
Different Types ◽  
Social Display ◽  
And Control

Earlier research using qualitative techniques suggests that the default conception of online social networks is as public spaces with little or no expectation of control over content or distribution of profile information. Some research, however, suggests that users within these spaces have different perspectives on information control and distribution. This study uses Q methodology to investigate subjective perspectives with respect to privacy of, and control over, Facebook profiles. The results suggests three different types of social media users: those who view profiles as spaces for controlled social display, exerting control over content or audience; those who treat their profiles as spaces for open social display, exercising little control over either content or audience; and those who view profiles as places to post personal information to a controlled audience. We argue that these different perspectives lead to different privacy needs and expectations.

Contextualizing how teens manage personal and interpersonal privacy on social media

2019 ◽  
Vol 22 (6) ◽  
pp. 1058-1075
Author(s):  
Ralf De Wolf
Keyword(s):  
Social Media ◽  
Personal Information ◽  
Survey Study ◽  
Information Control ◽  
Privacy Management ◽  
Communication Privacy Management ◽  
Control Information ◽  
Different Types ◽  
Communication Privacy ◽  
Individual Information

Many researchers have been studying teens’ privacy management on social media, and how they individually control information. Employing the theoretical framework of communication privacy management (CPM) theory, I argue that individual information control in itself is desirable but insufficient, giving only a limited understanding of teens’ privacy practices. Instead, I argue that research should focus on both personal and interpersonal privacy management to ultimately understand teens’ privacy practices. Using a survey study ( n = 2000), I investigated the predictors of teens’ personal and interpersonal privacy management on social media and compared different types of boundary coordination. The results demonstrate that feelings of fatalism regarding individual control in a networked social environment, which I call networked defeatism, are positively related with interpersonal privacy management. Also, interpersonal privacy management is less important when coordinating boundaries with peers than it is when coordinating sexual materials, and dealing with personal information shared by parents.

scholarly journals Privacy study on images uploaded in social networks

2018 ◽  
Vol 7 (1.7) ◽  
pp. 142
Author(s):  
Hemalatha D ◽  
Almas Begum ◽  
Alex David S
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Mobile Devices ◽  
Public Interest ◽  
Online Social Networks ◽  
Personal Information ◽  
Personal Characteristics ◽  
The Social ◽  
Study Results ◽  
Privacy Settings

Presently, the growth of Social media is explosive among the users. Increasingly developed social websites like Flickr, Facebook, Google+, LinkedIn etc permits the users to create, share and view the post. Confidentiality is a leading factor required in Social Networks. The social users upload their photos to the social sites that intend to gain public interest for social purposes. The exposure of personal information leads to slipping process like identity stealing, morphing etc, which are against the privacy violations. Relied upon the personal characteristics of users, the privacy settings of each user should be defined. In this paper, a relational study about the privacy settings in Online Social structure is examined. Initiated by the importance of social networks among the social users and their behavior towards Online Social Networks, which is followed by the privacy techniques suggested by other researchers are explored. At last, an overview about the merits and demerits of privacy designs and schemes for the user-uploaded images are presented. The study results a new privacy system that controls the confidential information from being accessed from different devices, including mobile devices and computers.

scholarly journals Understanding the Users Personal Attributes Similarity Across Online Social Networks

2019 ◽  
Vol 8 (11) ◽  
pp. 3902-3908
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Criminal Behavior ◽  
Personal Information ◽  
Personal Attributes ◽  
The Social ◽  
Media Services ◽  
The Relationship ◽  
Modern Era

In this modern era of technology, everyone accessing the Internet is obsessed with social media. A User accesses different social media services to fulfill his diverse needs. For instance, Instagram is mainly used for sharing personal visual content while Twitter is known for finding latest news and trends, similarly Facebook for personal posts. Such services lead to the distribution of personal information of an Internet user on these platforms. In this paper, we build a framework to discover the relationship among the attributes of a user across the social media.We use different fuzzy string matching algorithms to find the similarities between the attributes. We extract the ‘name’ and ‘username’ from a publicly shared dataset and apply two character based and token based algorithms on these features. The results are indicative of the fact that only a limited number of users share the same name and username across the sites. On further analysis, it is found that although name and username of most of the users do not exactly match, they tend to be similar with the infinitesimal difference like; underscore, period, one digit numbers, etc. This study provides an analysis of the typical variations in names and usernames, which can further be studied for the extension to other social networks This profile will help in behavior analysis of a user, which will further help us to improve recommendations and analyze for criminal behavior and similar applications.

scholarly journals Goffman's Theory as a Framework for Analysis of Self Presentation on Online Social Networks

2019 ◽  
Vol 13 (2) ◽  
pp. 243-276 ◽  
Author(s):  
Lucie Merunková ◽  
Josef Šlerka
Keyword(s):  
Social Networks ◽  
Impression Management ◽  
Online Social Networks ◽  
Personal Information ◽  
Structured Interviews ◽  
Public Authorities ◽  
Self Presentation ◽  
Face To Face ◽  
The Social ◽  
And Control

To investigate how people form their identity on social networks and control the impressions they invoke in their audiences, we analyzed personal profiles of 50 university student Facebook users using Erving Gofmann´s dramaturgical theory. We identified five basic forms through which users create and present their identities: The Public diary, The Influencer, The Entertainer, Job and education and Hobby, as well as the appropriate secondary roles performed by users who interact with them.These findings are corroborated by 8 semi-structured interviews with respondents, which enable a more in-depth exploration of the way they use Facebook, the social interactions they participate in, their motivation for posting contributions, and how they engage in impression management, perceive privacy and resolve issues caused by multiple audiences.A better understanding of how privacy is conceived and what motivates users to share their personal information online is essential for public authorities’ cooperation on shaping company privacy policies and creation of appropriate legal regulations.The key results confirm the presence of conscious effort to make a desired impression and prove Goffman’s theory of face-to-face interactions to be relevant in the context of online social networks.

Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks

2021 ◽  
pp. 1-13
Author(s):  
C S Pavan Kumar ◽  
L D Dhinesh Babu
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Online Social Networks ◽  
Learning Model ◽  
Feature Engineering ◽  
Machine Learning Model ◽  
Social Media Platforms

Sentiment analysis is widely used to retrieve the hidden sentiments in medical discussions over Online Social Networking platforms such as Twitter, Facebook, Instagram. People often tend to convey their feelings concerning their medical problems over social media platforms. Practitioners and health care workers have started to observe these discussions to assess the impact of health-related issues among the people. This helps in providing better care to improve the quality of life. Dementia is a serious disease in western countries like the United States of America and the United Kingdom, and the respective governments are providing facilities to the affected people. There is much chatter over social media platforms concerning the patients’ care, healthy measures to be followed to avoid disease, check early indications. These chatters have to be carefully monitored to help the officials take necessary precautions for the betterment of the affected. A novel Feature engineering architecture that involves feature-split for sentiment analysis of medical chatter over online social networks with the pipeline is proposed that can be used on any Machine Learning model. The proposed model used the fuzzy membership function in refining the outputs. The machine learning model has obtained sentiment score is subjected to fuzzification and defuzzification by using the trapezoid membership function and center of sums method, respectively. Three datasets are considered for comparison of the proposed and the regular model. The proposed approach delivered better results than the normal approach and is proved to be an effective approach for sentiment analysis of medical discussions over online social networks.

scholarly journals Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 492
Author(s):  
Valentina Y. Guleva ◽  
Polina O. Andreeva ◽  
Danila A. Vaganov
Keyword(s):  
Social Networks ◽  
Sampling Method ◽  
Building Blocks ◽  
Global Dynamics ◽  
Diffusion Dynamics ◽  
Different Types ◽  
Prediction And Control ◽  
Path Lengths ◽  
And Control ◽  
Diffusion Prediction

Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base.

Online Social Networks: Why We Disclose

2010 ◽  
Vol 25 (2) ◽  
pp. 109-125 ◽  
Author(s):  
Hanna Krasnova ◽  
Sarah Spiekermann ◽  
Ksenia Koroleva ◽  
Thomas Hildebrand
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Information Disclosure ◽  
Structural Equation ◽  
Personal Information ◽  
Equation Model ◽  
Self Disclosure ◽  
Network Provider ◽  
Privacy Calculus ◽  
Privacy Risks

On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of Industry players and policymakers worldwide. Despite the Impressive scope of this phenomenon, very little Is understood about what motivates users to disclose personal Information. Integrating focus group results Into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose Information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

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