Research on Patterns of User Interactions and Media Popularity on Online Social Networks

2012 ◽  
Vol 7 (2) ◽  
pp. 269-276 ◽  
Author(s):  
Renjie Zhou ◽  
Huiqiang Wang ◽  
Guangsheng Feng ◽  
Bingyang Li ◽  
Wenjin Jin ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Interactions

Analysis of User Interactions in Online Social Networks

2011 ◽  
Author(s):  
Seokchan Yun ◽  
Heungseok Do ◽  
Jinuk Jung ◽  
Song Mina ◽  
Namgoong Hyun ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
User Interactions

The Alt-social Network of Natalie Bookchin’s Testament

2016 ◽  
Vol 18 (5) ◽  
pp. 459-477
Author(s):  
Sarah Whitcomb Laiola
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Online Social Networks ◽  
Social Web ◽  
User Interactions ◽  
Media Criticism ◽  
Direct Translation ◽  
The Social

This article addresses issues of user precarity and vulnerability in online social networks. As social media criticism by Jose van Dijck, Felix Stalder, and Geert Lovink describes, the social web is a predatory system that exploits users’ desires for connection. Although accurate, this critical description casts the social web as a zone where users are always already disempowered, so fails to imagine possibilities for users beyond this paradigm. This article examines Natalie Bookchin’s composite video series, Testament, as it mobilizes an alt-(ernative) social network of vernacular video on YouTube. In the first place, the alt-social network works as an iteration of “tactical media” to critically reimagine empowered user-to-user interactions on the social web. In the second place, it obfuscates YouTube’s data-mining functionality, so allows users to socialize online in a way that evades their direct translation into data and the exploitation of their social labor.

Scientific Dissemination on Facebook: The Performance of Public Health Journals (Preprint)

2020 ◽  
Author(s):  
Maria Thereza Viana Reis ◽  
Angela Maria Belloni Cuenca
Keyword(s):  
Public Health ◽  
Social Networks ◽  
Online Social Networks ◽  
Editorial Policy ◽  
Primary Source ◽  
Scientific Journals ◽  
User Interactions ◽  
The Social ◽  
Facebook Pages ◽  
Marketing Teams

BACKGROUND Since it was created, social networks on the internet have attracted millions of users who have incorporated them into their routines and use them as the primary source of news, texts, concepts, definitions and any need for information. In scientific communication, the innovations of science, then restricted to researchers networks, with online social networks came to the domain of society. OBJECTIVE This study characterized the Facebook pages of Brazilian scientific journals in the field of public health. METHODS The social network activities of four journals were studied with regard to the number of followers, type and frequency of posts, topics and user interactions (likes, comments and shares). In interviews, the journal editors indicated the motivation to use online social networks to disseminate their articles and described the available infrastructure for this purpose. RESULTS More aesthetically complex posts, with explanatory texts and images, draw more attention and, therefore, generate more interaction than simpler posts, i.e., those containing links only. The findings indicate that there is interest in scientific dissemination on Facebook on the part of editors; in several cases, the editors themselves perform this activity. In Brazil, the majority of public health journals do not have an established editorial policy for social networks, mainly because they do not have marketing teams or financial and human resources to improve online social networks. CONCLUSIONS The academic editors agree with the importance of these highly popularized networks in disseminating scientifically validated information on public health to society.

scholarly journals A METRIC TO EVALUATE INTERACTION OBFUSCATION IN ONLINE SOCIAL NETWORKS

2012 ◽  
Vol 20 (06) ◽  
pp. 877-892 ◽  
Author(s):  
ERO BALSA ◽  
CARMELA TRONCOSO ◽  
CLAUDIA DIAZ
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Online Social Networks ◽  
User Interaction ◽  
Sensitive Information ◽  
Interaction Patterns ◽  
User Interactions ◽  
The Real ◽  
Network Topologies ◽  
The Cost

Online social networks (OSNs) have become one of the main communication channels in today's information society, and their emergence has raised new privacy concerns. The content uploaded to OSNs (such as pictures, status updates, comments) is by default available to the OSN provider, and often to other people to whom the user who uploaded the content did not intend to give access. A different class of concerns relates to sensitive information that can be inferred from the behavior of users. For example, the analysis of user interactions augments social network graphs with potentially privacy-sensitive details on the nature of social relations, such as the strength of user relationships. A solution to prevent such inferences is to automatically generate dummy interactions that obfuscate the real interactions between OSN users. Given an adversary that observes the obfuscated interactions, the goal is to prevent the adversary from recovering parameters of interest (e.g., relationships strength) that accurately describe the real user interactions. The design and evaluation of obfuscation strategies requires metrics that express the level of protection they would offer when deployed in a particular OSN with its underlying user interaction patterns. In this paper we propose mutual information as obfuscation metric. It measures the amount of information leaked by the (observable) obfuscated interactions in the system on the (concealed) real interactions between users. We show that the metric is suitable for comparing different obfuscation strategies, and flexible to accommodate different network topologies and user communication patterns. Obfuscation comes at the cost of network overhead, and the proposed metric contributes to enabling the optimization of strategies to achieve good levels of privacy protection at minimum overhead. We provide a detailed methodology to compute the metric and perform experiments that illustrate its suitability.

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