Security Issues and Challenges in Online Social Networks (OSNs) Based on User Perspective

The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

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Artificial and Natural Topic Detection in Online Social Networks

iSys - Brazilian Journal of Information Systems ◽

10.5753/isys.2017.329 ◽

2017 ◽

Vol 10 (1) ◽

pp. 80-98

Author(s):

Sylvio Barbon Jr ◽

Gabriel Marques Tavares ◽

Guilherme Sakaji Kido

Keyword(s):

Social Networks ◽

Social Interactions ◽

Online Social Networks ◽

Topic Detection ◽

Privacy And Security ◽

Security Issues ◽

Textual Content

Online Social Networks (OSNs), such as Twitter, offer attractive means of social interactions and communications, but also raise privacy and security issues. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside a huge volume of data from several themes, topics, and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for TopicModeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph community architecture (density and degree) allows the indication of a topic strength and the classification of it as natural or artificial. The later created by the spammers on OSNs.

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Detecting misinformation in online social networks: A think-aloud study on user strategies

Studies in Communication and Media ◽

10.5771/2192-4007-2019-4-471 ◽

2019 ◽

Vol 8 (4) ◽

pp. 471-496

Author(s):

Isabelle Freiling

Keyword(s):

Social Networks ◽

Online Social Networks ◽

Ecological Validity ◽

Qualitative Interviews ◽

Think Aloud ◽

False Information ◽

User Perspective ◽

Information Evaluation ◽

Evaluation Strategies ◽

Personal Accounts

Although online social networks (OSN) facilitate the distribution of misinformation, one way of reducing the spread of false information in OSN is for users to detect it. Building on the framework of how audiences act to authenticate information, this study provides a user perspective on which strategies people use in evaluating information in OSN. In 15 qualitative interviews, participants were asked to think aloud while evaluating whether the content of posts from their own newsfeeds and of interviewer-supplied posts was true or false. Their answers were analyzed to determine which evaluation strategies they used. Analyzing participants’ thoughts as they evaluate information is more reliable than directly asking participants which strategies they think they use. Results show that users’ strategies in information evaluation are searching for more information, knowledge of account or content carries the most weight, and every detail needs to fit. A comparison of strategy usage for posts from befriended versus unknown personal accounts as well as for posts from followed news outlets versus not followed news outlets shows that for posts from followed news outlets, knowledge of the account was the most-used strategy followed by knowledge of the content. For other types of posts, strategy usage varied more widely and depended on each post. This highlights the importance and possible higher ecological validity of research on posts from news outlets that users actually follow, as users’ experiences with previous posts seem to play a major role in how they go about evaluating information in new posts.

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A Graph-Based Approach to Model Privacy and Security Issues of Online Social Networks

Social Network Engineering for Secure Web Data and Services ◽

10.4018/978-1-4666-3926-3.ch009 ◽

2013 ◽

pp. 184-205 ◽

Cited By ~ 1

Author(s):

Luca Caviglione ◽

Mauro Coccoli ◽

Alessio Merlo

Keyword(s):

Social Networks ◽

Social Organization ◽

Online Social Networks ◽

Personal Information ◽

Security Policies ◽

Privacy And Security ◽

Web Based ◽

Security Issues ◽

Cultural Phenomenon ◽

Usage Patterns

With millions of users, Online Social Networks (OSNs) are a huge cultural phenomenon. Put briefly, they are characterized by: i) an intrinsic sharing of personal information, ii) a rich set of features to publish, organize and retrieve contents, especially for emphasizing their social organization, iii) the interaction with a heterogeneous set of devices, e.g., ranging from desktops to mobile appliances, and iv) the mix of Web-based paradigms and sophisticated methodologies for processing data. However, if not properly implemented, or without effective security policies, i) – iv) could lead to severe risks in terms both of privacy and security. In this perspective, this chapter analyzes the major peculiarities of OSN platforms, the preferred development methodologies, and usage patterns, also by taking into account how personal information can be exploited to conduct malicious actions. Then, a graph-based modeling approach is introduced to reveal possible attacks, as well as to elaborate the needed countermeasures or (automated) checking procedures.

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