Analyzing information sharing strategies of users in online social networks

Studies of collaborative information use in electronic environments suggest that virtual communities share characteristics with face-to-face communities. The authors expand on an existing model to present an analytic framework for examining online social networks. The framework emphasizes the information sharing behaviors that are critical in building critical relationships in these online communities.Les études sur l’utilisation collaborative de l’information dans les environnements électroniques suggèrent que les communautés virtuelles partagent des caractéristiques communes avec les communautés réelles. Les auteurs développent un modèle existant afin de présenter un cadre analytique pour l’exploration des réseaux sociaux en ligne. Le cadre souligne l’importance des comportements de partage de l’information qui sont essentiels pour la construction de relations indispensables dans ces communautés virtuelles.

Download Full-text

Trust and Share: Trusted Information Sharing in Online Social Networks

2012 IEEE 28th International Conference on Data Engineering ◽

10.1109/icde.2012.127 ◽

2012 ◽

Cited By ~ 3

Author(s):

Barbara Carminati ◽

Elena Ferrari ◽

Jacopo Girardi

Keyword(s):

Social Networks ◽

Information Sharing ◽

Online Social Networks

Download Full-text

Follow Me Too: Determinants of Transitive Tie Formation on Twitter

Social Media + Society ◽

10.1177/2056305120939248 ◽

2020 ◽

Vol 6 (3) ◽

pp. 205630512093924

Author(s):

Parul Malik ◽

Seungyoon Lee

Keyword(s):

Social Networks ◽

Information Sharing ◽

Network Structure ◽

Online Social Networks ◽

Strong Predictor ◽

Key Factors ◽

Factors Affecting ◽

Strength Of Ties ◽

Longitudinal Dataset

Transitivity, defined as the tendency for node A to be connected to node B given that A is connected to node X and X is connected to B, has been found to be a strong predictor of tie formation in various types of social networks. As transitive ties can influence information sharing, diffusion, and attitudes toward messages, understanding the motivations and mechanisms behind transitive tie formation in online social networks (OSNs) is important. Using a large longitudinal dataset from a popular OSN, Twitter, we examine the factors affecting transitivity. Results show that the strength of ties, activity like the number of tweets, and most importantly, the number of common connections are key factors affecting transitive tie formation. Theoretical implications regarding the evolution of network structure and polarization of views as well as practical suggestions for organizations aiming to accumulate followers for information sharing are discussed.

Download Full-text

Comparison of twitter spam detection using various machine learning algorithms

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.3.9268 ◽

2017 ◽

Vol 7 (1.3) ◽

pp. 61

Author(s):

M. Sangeetha ◽

S. Nithyanantham ◽

M. Jayanthi

Keyword(s):

Machine Learning ◽

Social Networks ◽

Information Sharing ◽

Online Social Networks ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Spam Detection ◽

Critical Problem ◽

Short Text ◽

True Blue

Online Social Networks(OSNs) have mutual themes such as information sharing, person-to-person interaction and creation of shared and collaborative content. Lots of micro blogging websites available like Twitter, Instagram, Tumblr. A standout amongst the most prominent online networking stages is Twitter. It has 313 million months to month dynamic clients which post of 500 million tweets for each day. Twitter allows users to send short text based messages with up to 140-character letters called "tweets". Enlisted clients can read and post tweets however the individuals who are unregistered can just read them. Due to the reputation it attracts the consideration of spammers for their vindictive points, for example, phishing true blue clients or spreading malevolent programming and promotes through URLs shared inside tweets, forcefully take after/unfollow valid clients and commandeer drifting subjects to draw in their consideration, proliferating obscenity. Twitter Spam has become a critical problem nowadays. By looking at the execution of an extensive variety of standard machine learning calculations, fundamentally expecting to distinguish the acceptable location execution in light of a lot of information by utilizing account-based and tweet content-based highlights.

Download Full-text