DOI:10.20894/IJBI.
Periodicity: Bi Annual.
Impact Factor:
SJIF:5.065
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded
Social media and, in particular, Online Social Networks (OSNs) acquired a huge popularity and represent one of the most important social and Computer Science phenomena in these years. Social networks allow users to collaborate with others. People of similar backgrounds and interests meet and cooperate using these social networks, enabling them to share information across the world. The social networks contain millions of unprocessed raw data. By analyzing this data, new knowledge can be gained. Since this data is dynamic and unstructured traditional data mining techniques will not be appropriate. Web data mining is an interesting field with vast amount of applications. With the growth of online social networks have significantly increased data content available because profile holders become more active producers and distributors of such data. This paper identifies and analyzes existing web mining techniques used to mine social network data. This dissertation presents a comprehensive study of the process of mining information from Online Social Networks and analyzing the structure of the networks themselves. To this purpose, several methods are adopted, ranging from Web Mining techniques, to graph-theoretical models and finally statistical analysis of network features, from a quantitative and qualitative perspective. The origin, distribution and sheer size of the data involved makes each of them either moot or inapplicable to the required scale.
