Luka Humski

Using the interaction on social networks to predict real life friendship

In todays world, social networks are one of the most popular ways of communication. Communication and relations among people can be monitored based on interaction on social networks. The question is the exact meaning of this interaction and can the real life relationship be interpreted from interaction on social networks. In this study, we used the most popular social network Facebook with the aim of finding the correlation between the interaction among users on Facebook and friendship in real life. In this paper, we propose a model for calculating the weight of friendship among users of social networks based on their interactions on Facebook. The model takes into account the general significance of a particular form of interaction (like, comment, etc.) and the specific significance of this form of interaction for each user. Apart from creating a model for calculating the weight of friendship, in this paper, general significance ratio of each communication parameter was experimentally determined. The model was built and evaluated by searching intersection of two sets, a set of users 10 best friends that he himself cited and a set of 10 best friends obtained using the proposed model. Average overlapping of these two sets was 70.9%. Additionally, the overlap level of these two sets for different demographic groups was analyzed.

Determination of Friendship Intensity between Online Social Network Users Based on their Interaction

Online social networks (OSN) are one of the most popular forms of modern communication and among the best known is Facebook. Information about the connection between users on the OSN is often very scarce. Its only known if users are connected, while the intensity of the connection is unknown. The aim of the research described was to determine and quantify friendship intensity between OSN users based on analysis of their interaction. We built a mathematical model, which uses: supervised machine learning algorithm Random Forest, experimentally determined importance of communication parameters and coefficients for every interaction parameter based on answers of research conducted through a survey. Taking user opinion into consideration while designing a model for calculation of friendship intensity is a novel approach in opposition to previous researches from literature. Accuracy of the proposed model was verified on the example of determining a better friend in the offered pair.

Applying the binary classification methods for discovering the best friends on an online social network

Online social networks (OSN) are one of the most widely adapted services of the Internet infrastructure, Facebook being one of the most popular among them. Facebook models connections between its users through the concept of “friendship”. However, the type and intensity of these connections between different people on Facebook vary significantly. In most cases, friends on Facebook correspond to mere acquaintances in real-life, with only a smaller subset representing actual close friends. The aim of research presented in this paper is to provide a method for estimating the intensity of Facebook friendships, i.e., to distinguish connections representing close friends from others. The study was performed by analyzing Facebook interactions between users (e.g. number of mutual likes, comments, shared photos, etc.) using supervised learning algorithms for binary classification of data. Among the chosen algorithms, the best results were gained by using random forest algorithm - accuracy of 84.73%.

Exploratory analysis of pairwise interactions in online social networks

ABSTRACT 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 3200 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 into OSN interaction for future researches aimed at this field of study.

Automated extraction and visualization of learning concept dependencies using Q-matrices and exam results

Information systems of educational organizations often represent a potential well of useful information which can be discovered and interpreted by using specific methods. Exam results in particular are commonly used as a single-use measure of individual knowledge states, after which they are archived and subsequently never used again. Our approach suggests using past exam results as a rich data source for extracting knowledge about learning concepts, especially regarding their mutual relationships. To achieve this goal, we adopt our method for interactive visualization of patterns in transactional data and apply it to knowledge state matrices generated from real-life exam results and Q-matrices constructed by domain experts, providing the end user with rich, easily interpretable and visually engaging dendrogram structures.

The potential of SEPA credit transfer implementation in Croatia

After joining the European Union, the Republic of Croatia became obliged to consolidate its laws and regulations with the EUs. European Commissions Regulation No 260/2012, obligatory for Croatia too, has set the 31st of October 2016 as the deadline for replacement of national euro credit transfer and direct debit schemes by SEPA Credit Transfer (SCT) and SEPA Direct Debit for the EU member countries that do not use euro as their currency. From that date, use of SCT will be obligatory for the credit transfers in euro, but it has been decided that it will be obligatory for national currency kuna, too. Since SEPA is based on ISO 20022 norm, which is built upon XML language, it is not realistic to expect from business subjects, i.e. future SEPA users, the understanding of XML technologies and ISO 20022 norm necessary for creating SCT electronic payment order. For that reason, we have built a web application, which provides for users a simple web interface with forms to fill and facilitate their transit towards SCT. This paper explains SEPA and describes its implementation in Croatia, structure of SCT electronic payment initiation order format and the web application we designed for its creation.