SOCIAL MEDIA IN MODERN BUSINESS

Social media help companies to reach new customers. New areas where companies can use social media include web-based training, team-based projects, distribution of updates about plans and activities to employees, search for new offers and verification of information during staff recruitment. The purpose of this article is to identify possible trends in the use of social media for enhancing the performance of modern business ventures. This paper compares selected classifications of the Internet development phases. The rule of content cocreation and sharing, typical of Web 2.0, remains valid during the subsequent stage of development, i.e. Web 3.0. A qualitative difference consists in adding a new function of using semantic analysis of messages posted in the virtual space, most notably in the social media. Semantic analysis is applied primarily in order to adjust the products offered to consumers’ needs. Application of semantic tools may also be associated with information exclusion. This paper also analyzes the implications of semantic web in the new context, the effect of information extraction from the social media

Electronic footprint analysis and cluster analysis techniques for information security risk research of university digital systems

In the article there are presented results of the study of the state of user competencies for different specialties of the university digital educational environment (UDEE) on issues related to information security (IS). The methods of cluster analysis and analysis of digital (electronic) traces (DT) of users are used. On the basis of analyzing the DTs of different groups of registered users in the UDEE, 6 types of users are identified. These types of users were a result of applying hierarchical classification and k-means method. Users were divided into appropriate clusters according to the criteria affecting IS risks. For each cluster, the UDEE IS expert can determine the probability of occurrence of high IS risk incidents and, accordingly, measures can be taken to address the causes of such incidents. The algorithms proposed in this study enable research during log file analysis aimed at identifying breaches of information security within the university\u27s DEE

When the Social Meets the Semantic: Social Semantic Web or Web 2.5

The social trend is progressively becoming the key feature of current Web understanding (Web 2.0). This trend appears irrepressible as millions of users, directly or indirectly connected through social networks, are able to share and exchange any kind of content, information, feeling or experience. Social interactions radically changed the user approach. Furthermore, the socialization of content around social objects provides new unexplored commercial marketplaces and business opportunities. On the other hand, the progressive evolution of the web towards the Semantic Web (or Web 3.0) provides a formal representation of knowledge based on the meaning of data. When the social meets semantics, the social intelligence can be formed in the context of a semantic environment in which user and community profiles as well as any kind of interaction is semantically represented (Semantic Social Web). This paper first provides a conceptual analysis of the second and third version of the Web model. That discussion is aimed at the definition of a middle concept (Web 2.5) resulting in the convergence and integration of key features from the current and next generation Web. The Semantic Social Web (Web 2.5) has a clear theoretical meaning, understood as the bridge between the overused Web 2.0 and the not yet mature Semantic Web (Web 3.0).Pileggi, SF.; Fernández Llatas, C.; Traver Salcedo, V. (2012). When the Social Meets the Semantic: Social Semantic Web or Web 2.5. Future Internet. 4(3):852-854. doi:10.3390/fi4030852S85285443Chi, E. H. (2008). The Social Web: Research and Opportunities. Computer, 41(9), 88-91. doi:10.1109/mc.2008.401Bulterman, D. C. A. (2001). SMIL 2.0 part 1: overview, concepts, and structure. IEEE Multimedia, 8(4), 82-88. doi:10.1109/93.959106Boll, S. (2007). MultiTube--Where Web 2.0 and Multimedia Could Meet. IEEE Multimedia, 14(1), 9-13. doi:10.1109/mmul.2007.17Fraternali, P., Rossi, G., & Sánchez-Figueroa, F. (2010). Rich Internet Applications. IEEE Internet Computing, 14(3), 9-12. doi:10.1109/mic.2010.76Lassila, O., & Hendler, J. (2007). Embracing «Web 3.0». IEEE Internet Computing, 11(3), 90-93. doi:10.1109/mic.2007.52Dikaiakos, M. D., Katsaros, D., Mehra, P., Pallis, G., & Vakali, A. (2009). Cloud Computing: Distributed Internet Computing for IT and Scientific Research. IEEE Internet Computing, 13(5), 10-13. doi:10.1109/mic.2009.103Mangione-Smith, W. H. (1998). Mobile computing and smart spaces. IEEE Concurrency, 6(4), 5-7. doi:10.1109/4434.736391Greaves, M. (2007). Semantic Web 2.0. IEEE Intelligent Systems, 22(2), 94-96. doi:10.1109/mis.2007.40Bojars, U., Breslin, J. G., Peristeras, V., Tummarello, G., & Decker, S. (2008). Interlinking the Social Web with Semantics. IEEE Intelligent Systems, 23(3), 29-40. doi:10.1109/mis.2008.50Definition of Web 2.0http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.htmlZhang, D., Guo, B., & Yu, Z. (2011). The Emergence of Social and Community Intelligence. Computer, 44(7), 21-28. doi:10.1109/mc.2011.65Pentlan, A. (2005). Socially aware, computation and communication. Computer, 38(3), 33-40. doi:10.1109/mc.2005.104Staab, S., Domingos, P., Mika, P., Golbeck, J., Li Ding, Finin, T., … Vallacher, R. R. (2005). Social Networks Applied. IEEE Intelligent Systems, 20(1), 80-93. doi:10.1109/mis.2005.16The Semantic Webhttp://www.scientificamerican.com/article.cfm?id=the-semantic-webDecker, S., Melnik, S., van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., … Horrocks, I. (2000). The Semantic Web: the roles of XML and RDF. IEEE Internet Computing, 4(5), 63-73. doi:10.1109/4236.877487OWL Web Ontology Language Overviewhttp://www.w3.org/TR/owl-features/Vetere, G., & Lenzerini, M. (2005). Models for semantic interoperability in service-oriented architectures. IBM Systems Journal, 44(4), 887-903. doi:10.1147/sj.444.0887Fensel, D., & Musen, M. A. (2001). The semantic web: a brain for humankind. IEEE Intelligent Systems, 16(2), 24-25. doi:10.1109/mis.2001.920595Shadbolt, N., Berners-Lee, T., & Hall, W. (2006). The Semantic Web Revisited. IEEE Intelligent Systems, 21(3), 96-101. doi:10.1109/mis.2006.62Dodds, P. S., & Danforth, C. M. (2009). Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents. Journal of Happiness Studies, 11(4), 441-456. doi:10.1007/s10902-009-9150-9Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. doi:10.1561/1500000011Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63(1), 163-173. doi:10.1002/asi.21662Blogmeterhttp://www.blogmeter.it/Christakis, N. A., & Fowler, J. H. (2010). Social Network Sensors for Early Detection of Contagious Outbreaks. PLoS ONE, 5(9), e12948. doi:10.1371/journal.pone.0012948Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188. doi:10.1002/asi.21149Bernal, P. A. (2010). Web 2.5: The Symbiotic Web. International Review of Law, Computers & Technology, 24(1), 25-37. doi:10.1080/13600860903570145Mikroyannidis, A. (2007). Toward a Social Semantic Web. Computer, 40(11), 113-115. doi:10.1109/mc.2007.405Jung, J. J. (2012). Computational reputation model based on selecting consensus choices: An empirical study on semantic wiki platform. Expert Systems with Applications, 39(10), 9002-9007. doi:10.1016/j.eswa.2012.02.03

Getting Real: A Naturalistic Methodology for Using Smartphones to Collect Mediated Communications

This paper contributes an intentionally naturalistic methodology using smartphone logging technology to study communications in the wild. Smartphone logging can provide tremendous access to communications data from real environments. However, researchers must consider how it is employed to preserve naturalistic behaviors. Nine considerations are presented to this end. We also provide a description of a naturalistic logging approach that has been applied successfully to collecting mediated communications from iPhones. The methodology was designed to intentionally decrease reactivity and resulted in data that were more accurate than self-reports. Example analyses are also provided to show how data collected can be analyzed to establish empirical patterns and identify user differences. Smartphone logging technologies offer flexible capabilities to enhance access to real communications data, but methodologies employing these techniques must be designed appropriately to avoid provoking naturally occurring behaviors. Functionally, this methodology can be applied to establish empirical patterns and test specific hypotheses within the field of HCI research. Topically, this methodology can be applied to domains interested in understanding mediated communications such as mobile content and systems design, teamwork, and social networks

Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks

Semantic Analysis of Messages Containing Peer to Peer Lending Issues on Instagram and Twitter

AbstrakIndustri pinjaman online mulai berkembang di Indonesia pada tahun 2016. Terdapat dua jenis pinjaman online yang berkembang di Indonesia yaitu pinjaman online ilegal dan pinjaman online legal. Bertambahnya jumlah kasus pinjaman online ilegal berdampak pada menurunnya tingkat customer trust masyarakat Indonesia terhadap industri pinjaman online. Tujuan penelitian yaitu melakukan analisis semantik untuk melihat behaviour pihak perusahaan pinjaman online dari isi pesan pinjaman online yang terdapat dalam UGC (User Generated Content) yang menyebabkan banyak konsumen pinjaman online mengalami penurunan tingkat kepercayaan kepada perusahaan pinjaman online. Penelitian ini menggunakan text mining yaitu analisis semantik. Analisis semantik akan dilakukan dengan menggunakan software Wmatrix5. Data diperoleh dari hasil crawling menggunakan Google Collab dan web scraping Phantombuster pada sosial media Instagram dan Twitter.Hasil analisis menunjukkan terdapat 15 kelompok semantik yang ada dalam pesan pinjaman online, kelompok tersebut antara lain yaitu crime (G2.1-), giving (A9+), paper documents and writing (Q1.2), knowledge (X2.2), polite (S1.2.4+), knowledgeable (X2.2+), unmatched (Z99), law and order (G2.1), getting and possession (A9+), money: debts (I1.2), personal relationship: general (S3.1), speed: fast (N3.8+), helping (S8+), information technology and computing (Y2), dan business: selling (I2.2). Kata Kunci: Semantik Analisis, Pinjaman Online, Kepercayaan Pelanggan, Perilaku Pelanggan, Wmatrix5. AbstractPeer to peer lending industry began to develop in Indonesia in 2016. There are two types of peer to peer lending industry that are developing in Indonesia, namely illegal and legal. The increasing number of cases of illegal peer to peer lending industry has an impact on the decline in the level of customer trust of customer peer to peer lending in Indonesia.The purpose of the research is to conduct a semantic analysis to see the behavior of peer to peer lending companies from the content of peer to peer lending messages contained in UGC (User Generated Content) which causes many peer to peer lending consumers to experience a decrease in the level of trust in peer to peer lending companies. This research uses text mining, namely semantic analysis. Semantic analysis will be carried out using Wmatrix5 software. The data is obtained from crawling using Google Collab and web scraping Phantombuster on Instagram and Twitter social media.The results of the analysis show that there are 15 semantic groups in peer to peer lending messages, these groups include crime (G2.1-), giving (A9+), paper documents and writing (Q1.2), knowledge (X2.2), polite (S1.2.4+), knowledgeable (X2.2+), unmatched (Z99), law and order (G2.1), getting and possession (A9+), money: debts (I1.2), personal relationship: general (S3.1), speed: fast (N3.8+), helping (S8+), information technology and computing (Y2), and business: selling (I2.2). Keywords: Semantic Analysis, Peer to Peer Lending, Customer Trust, Customer Behaviour, Wmatrix5