Making friends and communicating on Facebook: Implications for the access to social capital

Fuzzy systems have been used widely thanks to their ability to successfully solve a wide range of problems in different application fields. However, their replication and application require a high level of knowledge and experience. Furthermore, few researchers publish the software and/or source code associated with their proposals, which is a major obstacle to scientific progress in other disciplines and in industry. In recent years, most fuzzy system software has been developed in order to facilitate the use of fuzzy systems. Some software is commercially distributed, but most software is available as free and open-source software, reducing such obstacles and providing many advantages: quicker detection of errors, innovative applications, faster adoption of fuzzy systems, etc. In this paper, we present an overview of freely available and open-source fuzzy systems software in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well-founded future work. To accomplish this, we propose a two-level taxonomy, and we describe the main contributions related to each field. Moreover, we provide a snapshot of the status of the publications in this field according to the ISI Web of Knowledge. Finally, some considerations regarding recent trends and potential research directions are presented.

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A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Suppressed fuzzy c-means clustering algorithm (S-FCM) is one of the most effective fuzzy clustering algorithms. Even if S-FCM has some advantages, some problems exist. First, it is unreasonable to compulsively modify the membership degree values for all the data points in each iteration step of S-FCM. Furthermore, duo to only utilizing the spatial information derived from the pixel's neighborhood window to guide the process of image segmentation, S-FCM cannot obtain satisfactory segmentation results on images heavily corrupted by noise. This paper proposes an optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation to solve the above drawbacks of S-FCM. Firstly, an optimal-selection-based suppressed strategy is presented to modify the membership degree values for data points. In detail, during each iteration step, all the data points are ranked based on their biggest membership degree values, and then the membership degree values of the top r ranked data points are modified while the membership degree values of the other data points are not changed. In this paper, the parameter r is determined by the golden section method. Secondly, a novel gray level histogram is constructed by using the self-tuning non local spatial information for each pixel, and then fuzzy c-means clustering algorithm with the optimal-selection-based suppressed strategy is executed on this histogram. The self-tuning non local spatial information of a pixel is derived from the pixels with a similar neighborhood configuration to the given pixel and can preserve more information of the image than the spatial information derived from the pixel's neighborhood window. This method is applied to Berkeley and other real images heavily contaminated by noise. The image segmentation experiments demonstrate the superiority of the proposed method over other fuzzy algorithms.

Optimal-selection-based suppressed fuzzy c-means clustering algorithm with self-tuning non local spatial information for image segmentation

In the current era of automation, machines are constantly being channelized to provide accurate interpretations of what people express on social media. The human race nowadays is submerged in the idea of what and how people think and the decisions taken thereafter are mostly based on the drift of the masses on social platforms. This article provides a multifaceted insight into the evolution of sentiment analysis into the limelight through the sudden explosion of plethora of data on the internet. This article also addresses the process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks. The techniques of communalizing user data have also been surveyed in this article. Data, in its different forms, have also been analyzed and presented as a part of survey in this article. Other than this, the methods of evaluating sentiments have been studied, categorized, and compared, and the limitations exposed in the hope that this shall provide scope for better research in the future.

A Survey of Sentiment Analysis from Social Media Data

How do motives affect attitudes and behaviors toward internet advertising and Facebook advertising

Currently, social networks such as Facebook or Twitter are getting more and more popular due to the opportunities they offer. As of November 2009, Facebook was the most popular and well known social network throughout the world with over 316 million users. Among the countries, Turkey is in third place in terms of Facebook users and half of them are younger than 25 years old (students). Turkey has 14 million Facebook members. The success of Facebook and the rich opportunities offered by social media sites lead to the creation of new web based applications for social networks and open up new frontiers. Thus, discovering the usage patterns of social media sites might be useful in taking decisions about the design and implementation of those applications as well as educational tools. Therefore, in this study, the factors affecting “Facebook usage time” and ”Facebook access frequency” are revealed via various predictive data mining techniques, based on a questionnaire applied on 570 Facebook users. At the same time, the associations of the students’ opinions on the contribution of Facebook in an educational aspect are investigated by employing the association rules method.

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Identification of User Patterns in Social Networks by Data Mining Techniques: Facebook Case

The purpose of this article is to identify the order of significance of the variables that affect science and mathematics achievement in middle school students. For this aim, the study deals with the relationship between science and math in terms of different angles using the perspectives of multiple causes-single effect and of multiple causes-multiple effects. Furthermore, the study examines and reveals how the reading skills, problem solving skills, cognitive and affective variables influence the math and science achievement. The data was collected from the results of Turkish students who participated in three international examinations; TIMSS 1999, PISA 2003 and PISA 2006. We analyzed the data using two data-mining methods (decision trees and clustering). The findings show that science or mathematics achievement is not influenced by the course-specific variable alone but also by other related variables. The following variables are the most important; the students’ reading and problem-solving skills affected both mathematics and science achievement; the mathematics achievement affected the science achievement; and the science achievement affected the mathematics achievement. It is also found that the affective variables have almost equally significant effects on the science and mathematics achievement.

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Identifying the Factors Affecting Science and Mathematics Achievement Using Data Mining Methods.

As it is known, fuzzy clustering is a kind of soft clustering method and primarily based on idea of segmenting data by using membership degrees of cases which are computed for each cluster. However, most of the current fuzzy clustering modules packaged in both open source and commercial products have lack of enabling users to explore fuzzy clusters deeply and visually in terms of investigation of different relations among clusters. Furthermore, without a decision maker or an expert, it is hard to decide the number of clusters in fuzzy clustering studies. Therefore, in this study, a desktop software, namely FUAT, is developed to analyze, explore and visualize different aspects of obtained fuzzy clusters which are segmented by fuzzy c-means algorithm. Moreover, to obtain and inform possible natural cluster number, FUAT is equipped with Expectation Maximization algorithm.

FUAT - A fuzzy clustering analysis tool

With the advent of the Internet and reduction of the costs in digital communication, spam has become a key problem in several types of media (i.e. email, social media and micro blog). Further, in recent years, email spamming in particular has been subjected to an exponentially growing threat which affects both individuals and business world. Hence, a large number of studies have been proposed in order to combat with spam emails. In this study, instead of subject or body components of emails, pure use of hyperlink texts along with word level n-gram indexing schema is proposed for the first time in order to generate features to be employed in a spam/ham email classifier. Since the length of link texts in e-mails does not exceed sentence level, we have limited the n-gram indexing up to trigram schema. Throughout the study, provided by COMODO Inc, a novel large scale dataset covering 50.000 link texts belonging to spam and ham emails has been used for feature extraction and performance evaluation. In order to generate the required vocabularies; unigrams, bigrams and trigrams models have been generated. Next, including one active learner, three different machine learning methods (Support Vector Machines, SVM-Pegasos and Naive Bayes) have been employed to classify each link. According to the results of the experiments, classification using trigram based bag-of-words representation reaches up to 98,75% accuracy which outperforms unigram and bigram schemas. Apart from having high accuracy, the proposed approach also preserves privacy of the customers since it does not require any kind of analysis on body contents of e-mails.

A. Selman Bozkir

Papers

Identification of User Patterns in Social Networks by Data Mining Techniques: Facebook Case

Identifying the Factors Affecting Science and Mathematics Achievement Using Data Mining Methods.

FUAT - A fuzzy clustering analysis tool

Spam E-Mail Classification by Utilizing N-Gram Features of Hyperlink Texts