Crawling Facebook for Social Network Analysis Purposes

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.\u

Exploratory Analysis of Pairwise Interactions in Online Social Networks

In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where "friends" on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users' interaction via different ways of OSN interaction. In this paper we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3,200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight in online social network interaction for future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4 on pages 422 to 42

On Facebook, most ties are weak

Pervasive socio-technical networks bring new conceptual and technological challenges to developers and users alike. A central research theme is evaluation of the intensity of relations linking users and how they facilitate communication and the spread of information. These aspects of human relationships have been studied extensively in the social sciences under the framework of the "strength of weak ties" theory proposed by Mark Granovetter.13 Some research has considered whether that theory can be extended to online social networks like Facebook, suggesting interaction data can be used to predict the strength of ties. The approaches being used require handling user-generated data that is often not publicly available due to privacy concerns. Here, we propose an alternative definition of weak and strong ties that requires knowledge of only the topology of the social network (such as who is a friend of whom on Facebook), relying on the fact that online social networks, or OSNs, tend to fragment into communities. We thus suggest classifying as weak ties those edges linking individuals belonging to different communities and strong ties as those connecting users in the same community. We tested this definition on a large network representing part of the Facebook social graph and studied how weak and strong ties affect the information-diffusion process. Our findings suggest individuals in OSNs self-organize to create well-connected communities, while weak ties yield cohesion and optimize the coverage of information spread.Comment: Accepted version of the manuscript before ACM editorial work. Check http://cacm.acm.org/magazines/2014/11/179820-on-facebook-most-ties-are-weak/ for the final versio

Brief Announcement: Non-Blocking Dynamic Unbounded Graphs with Worst-Case Amortized Bounds

This paper reports a new concurrent graph data structure that supports updates of both edges and vertices and queries: Breadth-first search, Single-source shortest-path, and Betweenness centrality. The operations are provably linearizable and non-blocking

A Modified Weight Balanced Algorithm for Influential Users Community Detection in Online social Network (OSNs)

In the modern era online users are increasing day by day. Different users are using various social networks in different forms. The behavior and attitude of the users of social networking sites varies U2U (User to User). In online social networking users join many groups and communities as per interests and according to the groups'/Communities' influential user. This paper consist of 7 sections , first section emphasis on introduction to the community evelotion and community. Second section signify movement between communities ,third section involve related work about the research.. Fourth section includes Problem Definition and fifth section involve Methodology (Proposed Algorithm Process ,Get Community Matrix, Community detetcion).Sixth section involve Implementation. Furthermore implementation include Datasets ,Quantitative performance, Graphical Results, Enhancement in the existing work..Last section include Conclusion and then references. In this paper,we are implementing and proposing the community detection in social media .In the proposed we have deployed a Longest Chain Subsequence metric for finding the number of connections to the kernel community

SISTEM PREDIKSI KEPRIBADIAN MANUSIA BERDASARKAN STATUS MEDIA SOSIAL MENGGUNAKAN SUPPORT VECTOR MACHINE

Currently, social media is a forum for exchanging information widely used by the public, such as Facebook and Twitter. Social media users exchange information to find out the condition of one another. Some companies use social media to explore the personality potential of prospective employees to be recruited. However, to dig up this information takes a very long time because the company has to open prospective employees' social media one by one. To dig up information automatically, a personality detection system is needed from social media users. This study develops a person's personality prediction system based on social media status using the support vector machine. The data sets evaluated in this study were 300 Facebook social media status data and 2067 Twitter social media status data. Based on the evaluation results, we obtained a high level of accuracy in detecting a person's personality based on social media status, namely 100% for Facebook user status and 99.3% for Twitter user status.Keywords: Personality, Social Media, Support Vector Machine, Facebook, Twitter ABSTRAKSaat ini, media sosial merupakan salah suatu wadah pertukaran informasi yang banyak digunakan oleh masyarakat, seperti Facebook maupun Twitter. Pengguna media sosial saling bertukar informasi untuk mengetahui kondisi satu dengan lainnya. Beberapa perusahaan memanfaatkan media sosial untuk menggali potensi kepribadian dari calon pegawai yang akan direkrut. Namun, untuk menggali informasi tersebut memerlukan waktu yang sangat lama karena perusahan harus membuka media sosial dari calon pegawai satu per satu. Agar dapat menggali informasi secara otomatis, maka diperlukan sistem deteksi kepribadian dari pengguna media sosial. Penelitian ini mengembangkan sistem prediksi kepribadian seseorang berdasarkan status media sosial menggunakan metode Support Vector Machine. Set data yang dievaluasi dalam penelitian ini yaitu 300 data status media sosial Facebook dan 2067 data status media sosial Twitter. Berdasarkan hasil evaluasi yang dilakukan diperoleh tingkat akurasi yang tinggi dalam mendeteksi kepribadian seseorang berdasarkan status media sosial, yaitu 100% untuk status pengguna Facebook dan 99,3% untuk status pengguna Twitter.  Kata Kunci: Kepribadian, Media Sosial, Support Vector Machine, Facebook,  Twitter

Membangun Web Crawler Berbasis Web Service Untuk Data Crawling Pada Website Google Play Store

At this time, Google Play Store is not providing API that can be used for accessing datas from applications on it's application store. With that plenty application's data, it could be used to make it a good research object, specially on data mining field. In this research, the system that is built is the system that can retrieve that applications' data. For multiplatform's purpose, web services are used for being an interface between client and server. Finally, the built system is working as expected. The system can retrive data from Google Play Store and it is suitable from requirements of data analysis stage. It can also integrated with REST web service to provide multiplatform access

Exploration and Optimization Of Friends’ Connections In Social Networks

One paragraph only. Over the past few years, the rapid growth and the exponential use of social digital media has led to an increase in popularity of social networks and the emergence of social computing. In general, social networks are structures made of social entities (e.g., individuals) that are linked by some specific types of interdependency such as friendship. Most users of social media (e.g., Facebook, LinkedIn, MySpace, Twitter, Flickr, YouTube) have many linkages in terms of friends, connections, and/or followers. Among all these linkages, some of them are more important than others. This paper discusses related work on social networks and method use in crawling online social network graph