Modelling the Complex of Automation of Company Marketing Activity in Online Communities Oksana Peleshchyshyn 1 [0000-0002-1641-7340], Kateryna Molodetska3[0000-0001-9864-2463], Alla Solianyk2 [0000-0002-7167-6695], Ruslan Kravets 1 [0000-0003-2837-9190] 1 Lviv Polytechnic National University, Ukraine 2 Kharkiv State Academy of Culture, Ukraine 3 Head of educational and scientific center of IT, Zhytomyr National Agroecological University, Zhytomyr, Ukraine oksana.p.peleshchyshyn@lpnu.ua, kmolodetska@gmail.com, allasolyanik164@gmail.com, ruslan.b.kravets@lpnu.ua Abstract. This paper deals with actual problem of investigation the usage of online communities in the company marketing activities. Based on a formal de- scription of characteristics, data on online communities and discussions and their analysis results, an information model of a community for online market- ing has been built, which serves as the basis for a database structure for ac- counting of information flows in online communities. The task of determination of indicators of relevance and importance of online communities marketing was proposed. The use of tproposed indicators in solving community selection tasks for representatives’ participation of the company was considered. The usage of the database provisioning in the process of creating, verifying and distributing marketing messages in online communities was suggested. By formalizing and computerizing the online communication process proposed methods in this work helps defined threats in discussions and violations of online rules and tra- ditions. Keywords: online marketing, online community, information threat, marketing, information model. 1 Introduction Modern companies and organizations often use online communities to share market- ing information about a company and its products. In this case, the active involvement of the marketer in online communities involves work, which relates to the accounting and analysis of online communities and their content. By formalizing and computeriz- ing the communication process, you can avoid undesirable risks for the company. Threats may arise from unqualified discussions or violations of online rules and tradi- tions. Among the typical tasks, the key ones with regard to the activities in online com- munities are the choice of the strategy for using communities in marketing; selection Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attrib- ution 4.0 International (CC BY 4.0) CMiGIN-2019: International Workshop on Conflict Management in Global Information Networks. of online communities for certain activities; analysis of the effectiveness of marketing information flows in the virtual environment. A significant aspect of effective online marketing in an information-intensive envi- ronment of the online communities is the use of the modern information technology solutions, user-orianted mathematical services and software. These services process the large volumes of data in real-time. The main up-to-date investigations of marketing activities in online community are divided into the following fields:  information retrieval and web content analysis [1-3];  approaches for creating and managing of online communities [4];  web sites positioning in the web, information activities management of the compa- ny in the sites networks [5-7];  approaches of online communities marketing [8-10];  quality management system in universities [ 11-15];  conception of relevant and important for marketing online communities and dis- cussions [17-23];  resources and risks in marketing strategies for online communities [24-27]. 2 Database for accounting for marketing communications For the full accounting of marketing communications in online communities, we use an expanded Information Flows Database. The database consists of the following components:  Community Database is used for basic information about online communities;  Discussion Database is used for information about discussions in online communities;  Service Database is used for accounting of service information. Depending on the functional duties and the area of responsibility, data bases are filled by specialists in different workplaces of the system complex. This process is gradual and continuous if the activities of business representatives in online communities are constant. Let us consider the structure of each component of the extended database. 2.1 Accounting for online communities The online Communities Database is used for accounting of the online communities covered by the company's marketing activities, their technical and statistical charac- teristics. Database filling is performed in the process of performing tasks:  search and account of relevant online communities based on the elected strategy;  accounting and analyzing rules, statistics and semantic characteristics of relevant online communities, keeping them topicality;  forming and accounting of standard for evaluating the importance of the online community in terms of specific marketing activities (advertising, public action, positioning of the site of the company, work with clients, etc.);  selection of important online communities in accordance with defined standards for marketing activities. Community Rules - User Profile Community Rules - Posting PK,FK1 Community ID PK,FK1 Community ID Using real name, last name Admissibility of publication of advertising materials Using a nickname Affordability of attached Files Using the Latin alphabet Maximum size of attachments Using Cyrillic Allowed types of attachments Using the site address Maximum size of image objects Using a business or product name Allowed types of graphic objects Validity of avatars in a user's signature Allowed links Avatar size Admissibility of referrals Admissibility in the signature of graphic images Admissibility in signature links Maximum number of lines in a signature Maximum number of characters in a signature Language of communication PK Language ID Online Community Language Name Types of online communities Comment PK Community ID PK Community type ID Community Name Community type Name Website address Comment Community Description Community Rules - Working languages Website management system Markup language PK,FK1 Community ID FK1 Community type ID PK,FK2 Language ID Is Working language Priority Community statistics PK,FK1 Community ID Recommendations for work - Style of discussion PK Statistics Date PK,FK1 Community ID Number of participants Number of discussions Optimal message size Number of posts Feasibility of using promotional graphic objects Number of external links Advisability of using advanced formatting Number of views of posts Optimum number of links in the message Frequency of discussions Admissibility of advanced site citation Frequency of posts in discussions Optional UserName Approximate daily attendance Special linguistic characteristics (slang, etc.). Fig. 1. ER-diagram of the “Community” database. 2.2 Database for accounting data and discussion characteristics In addition to the characteristics of online accounting communities, discussions that contain valuable marketing information and reflect the activities of business repre- sentatives in online communities are also a subject for discussion. Discussion data- base is used for accounting for discussion information. The result is:  research and recording relevant discussions;  accounting for technical and semantic characteristics of relevant discussions. On Fig. 2 is an ER diagram of the "Discussion" Database. Technical description of Discussions PK,FK1 Discussion ID PK Characteristic Date Number of posts in the discussion Number of views Date of the last post in the discussion Date of the last view Number of external links Online Communities Average daily intake of posts Average daily views intensity PK Community ID Community Name Author ... PK Author ID Discussion Author Name PK Discussion ID Is Employee E-mail FK1 Community ID Autor's site Content of the discussion Date of discussion creation Discussion type FK2 Author PK Discussion type ID FK3 Discussion type ID Discussion type Name Comment Post Semantic characteristics of the discussion PK Discussion ID PK,FK1 Discussion ID PK Characteristic Date FK1 Post ID Post content Rate of the discussion Post Date Reactions to marketing messages FK2 Author Completeness of the discussion. Date of the last view Fig. 2. ER diagram of the "Discussion" Database. 2.3 Database for accounting of business information Singly from the chosen strategy for using online communities, for the effective work of the marketer, all the results of the analysis of communities and discussions must be recorded in the databases and actively used in future work. This especially concern with the information of the relevance and importance of the community and the data needed to evaluate them. In Fig. 3 is an ER diagram of the database in part of accounting for the relevance and importance of online communities and discussions. Community relevancy Community importance PK,FK1 Community ID PK,FK1 Community ID Compliance with OC and MT topics PK Analysis Date PK,FK2 Task ID PK Analysis Date PK,FK1 Community ID Community Relevancy PK,FK2 Marketing term ID Comment Community Importance Comment Factors of community importance Online community (OC) PK Factor ID Marketing term Group PK Community ID FK1 Task ID Factor weight PK MP Group ID Community Name ... MT Group Name Factor of discussion importance Comment PK Factor ID Marketing activity FK1 Task ID PK Task ID Factor weight Marketing term (MT) Task Name Discussion iimportance PK Marketing term ID Description Begin Date PK,FK2 Community ID Marketing Name End Date PK,FK1 Task ID Comment Comment PK Analysis Date FK1 Marketing term Group ID Discussion Importance Relevance of D and MT Discussion relevance Discussion (D) PK,FK2 Marketing term ID PK,FK1 Discussion ID PK Discussion ID PK Analysis Date PK,FK1 Discussion ID FK1 Community ID Discussion Relevance Comment ... Comment Fig. 3. ER diagram of the database in part of accounting for the relevance and importance of online communities and discussions In general, all the activities of the marketer, the activities he took part in, the results of the analysis and evaluation of the online content of online communities should also be recorded in the database. This will also add transparency and validity to the - process of making decision of marketing in online communities. It is also important to accumulate and save the information related to process of finding data and creating content for online communities. Particulary, following in- formation is a subject for accounting:  multiple marketing terms for search communities and discussions;  types of marketing activities and types of community outreach activities;  global search engine query templates;  regular expression templates for parse the content of the pages;  options for assessing the importance of online communities and discussions;  templates for typical posts. In addition, in this database, it is advisable to account the distribution of executors on siteswhich are important for marketing online communities. An important moment in the process of filling the database "Service Information" is to form comments about the conditions of obtaining and using practical experience: when a particular query was used, template or set of parameters, etc. This data can be served as a knowledge base for contractors and experts. 3 Architecture of the Complex of Automation of Company Marketing Activity in Online Communities The software for support the company marketing activity in online communities con- tains the following components:  "Searcher" is use for search online communities and discussions;  "Analyst" is use for analyzing the information flows in online communities;  "Online Marketer" is use for interaction of the marketer with online communities;  "Coordinator" is use for organizing and coordinating activities of marketers in communities;  “Manager” is use for strategic planning and overall process control. Figure 3 shows the functionality and basic information flows of the system. Depending the chosen strategy for using online communities in your marketing activi- ties, some features may not be involved. In this case of an analytical strategy, all ac- tivities are limitedfor finding relevant and important online communities, monitoring and analyzing their content, and therefore the functionality of "Coordinator" and "Online Marketer" is not required. In case of limited human resources one specialist can work in several workplaces, provided the functionality does not violate the logic of the system in a whole. Particulary, it's possible to combine the following features:  “Searcher” and “Analyst”;  “Analyst” and “Coordinator”;  “Manager” and “Coordinator”. However, combining a “Coordinator” and an “Online Marketer” by same person can lead to abuse on her part and, as a consequence, a poor coordination of the activities of marketers in online communities. The search of online communities and discussions is carried on the early stages of organizing the company's marketing activities in virtual environments. In the future, part of work is about monitoring selected online communities and discussions. In addition, when marketing topic changes or need to be in another (possibly larger) set of online communities, there is a need for repeated or expanded community searches and discussions that can be further used to retrieve and disseminate information. The Searcher provides the following main features:  accounting of primary and adjuvant information, including:  accounting of the multiple marketing terms that are searched;  formation and accounting of search query templates;  rating of the relevance of found by marketing communities terms;  accounting for relevant online communities and their characteristics;  assessment of the relevance of the discussions found;  consideration of relevant discussions and their characteristics;  updating statistics of online communities and discussions;  monitor company web-sites and online communities to identify suspicious activity and likely information attacks on the company. Determination of marketing strategy, key metrics and Manager performance Analyzing criteria the use of online communities in marketing Analyzing Organizing the content of marketing relevant online activities in online communities communities Analyst Coordinator Interaction of representatives in online Analysis communities online- communities WWW Searcher Online marketer - 1 Online marketer - 2 ... Online marketer - N Онлайн-спільноти WWW Онлайн-спільноти Site access logs Services Online-communities Fig. 4. The scheme of functionality and basic information flows of the system. The analyst processes the content of online communities and performs the following functions:  formation and analysis of multiple marketing terms;  analysis of online community statistics and discussions;  determining the criteria for selecting important online communities and discussions;  identifying the importance of online communities and discussions;  selection of important online communities and discussions to accomplish specific marketing tasks;  analyzing the activity of competitors in online communities and taking them into account in the planning of the company activity;  аnalyzing critical messages in online communities and identifying problem areas in their business. An important function of the analyst is to analyze information flows on company sites and relevant communities and to identify threats for organization that arise from un- qualified and malicious activity in online communities. In case of confirmation of the information attack on the company in social media, the analyst sends the necessary information to the Coordinator to plan the counterac- tion measures and their further implementation by the online marketer. Guided by the strategy chosen by the management, the analyst assesses the suffi- ciency of the many found relevant online marketing and statistical data. Next, the specialist analyzes the importance of online community and discussion characteristics for marketing tasks and determines the weighting factors that willbe used for evalua- tion the online community's importance for discussions and discussions. An online marketer uses the “Information Streams” database to perform the fol- lowing functions:  forming and posting new messages, comments and replies to requests from users on community sites;  monitoring discussions in online communities;  interaction with the administration of online communities;  accounting and analysis of their own marketing communications;  analyzing the reaction of other online community members to actions of marketer. The efficiency and transparency of the communication process and it independence from the contractors are determined by the completeness of accounting of the activi- ties of marketers in the database "Information flows" and the activity of using their data (characteristics of online communities, supporting information, etc). The coordinator provides support for the following features:  analyzing the effectiveness of using online communities in marketing;  determining restrictions on the activities of marketers in online communities;  ensuring the best distribution of marketers across multiple communities;  assessing the effectiveness of marketers in their assigned communities;  coordinating of marketers' engagement with online communities. "Coordinator" and "Analyst" generate analytic data to evaluate the effectiveness of the online community's communication capabilities to disseminate marketing infor- mation and consumer communication. Analyzing the effectiveness of communica- tions, combined with the cost analysis of business representatives in online communi- ties, allows management to review the strategy of using virtual environments, to plan marketing activities and resources for their implementation. 4 Conclusions Computer support for marketing activities in a virtual environment can be done using a database that takes into account the search results of relevant and important online communities and the information flows of the company's interactions with the com- munity. The strategy of using online communities determines the functionality of the system and the list of tasks for marketers, during the execution of which information content of the database is formed. The proposed database model can be the basis for building a knowledge base for communications in online communities. The imple- mentation of the complex of automation supports the planning of marketing actions and decisions making to coordinate the activity of marketers and increases the effi- ciency of using virtual communities in marketing. References 1. Kim, J., Hastak, M.: Social network analysis: Characteristics of online social networks af- ter a disaster. International Journal of Information Management, vol. 38, 1, 86–96 (2018). 2. See-Toa, E., Ho, K.: Value co-creation and purchase intention in social network sites. A theoretical analysis. Computers in Human Behavior, vol. 31, 182–189 (2014). 3. Rains, S., Brunner, S.: What can we learn about social network sites by studying Face- book? 17, 1, 114–131 (2014). 4. Sloboda, K., Peleshchyshyn, P.: Peculiarities of positioning and online marketing of Lviv Polytechnic National University in social networks. European Applied Sciences, 5, 2, 24– 32 (2013). 5. Fedushko S., Peleschyshyn O., Peleschyshyn A., Syerov Y: The Verification of Virtual Community Member's Socio-Demographic Profile. Advanced Computing: An Internation- al Journal (ACIJ), vol. 4, no. 3, 29–38 (2013). 6. Russell, M.: Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. O'Reilly Media, Inc. (2013). 7. Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. (2011). 8. Plume, C., Dwivedi, Y., Slade, E.: Online Brand Communities, Social Media in the Mar- keting Context, A State of the Art Analysis and Future Directions, 41–78 (2017). 9. Bandias S.: Social media: the new tool in business education. Public Interest and Private Rights in Social Media, Chandos Publishing Social Media Series, 115–128 (2012). 10. Rath, M.: Application and Impact of Social Network in Modern Society. Hidden Link Pre- diction in Stochastic Social Networks, 30–49 (2018). 11. Myna, Zh., Yarka, U., Peleschyshyn, O., Bilushchak, Т.: Using International Standards of Quality Management System in Higher Educational Institutions. In: XIIIth International Conference “Modern Problems of Radio Engineering, Telecommunications and Computer Science” (TCSET'2016), 834–837, Lviv (2016). 12. Korzh, R., Peleshchyshyn, A., Fedushko, S., Syerov, Y.: Protection of University Infor- mation Image from Focused Aggressive Actions. Advances in Intelligent Systems and Computing: Recent Advances in Systems, Control and Information Technology, SCIT 2016, vol. 543, pp. 104–110. Springer (2017). DOI: 10.1007/978-3-319-48923-0_14. 13. Korzh, R., Peleshchyshyn, A., Syerov, Yu., Fedushko, S.: University’s Information Image as a Result of University Web Communities’ Activities. Advances in Intelligent Systems and Computing: Selected Papers from the International Conference on Computer Science and Information Technologies, CSIT 2016, vol. 512, pp. 115- 127. Springer (2017). DOI: 10.1007/978-3-319-45991-2_8. 14. Korzh, R., Peleschyshyn, A., Syerov, Yu., Fedushko, S.: Principles of University’s Infor- mation Image Protection from Aggression. In: Proceedings of the 11th International Scien- tific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2016, pp. 77–79. Lviv (2016). DOI: 10.1109/STC-CSIT.2016.7589873. 15. Markovets, O., Peleshchyshyn, A.: Stages of implementation of citizens' appeals pro- cessing system in heterogeneous web environments, International Scientific and Technical Conference on Computer Sciences and Information Technologies, Vol. 1, 75-78. (2017). 16. Messik, R.: Social media: blessing or curse? – a business perspective. Public Interest and Private Rights in Social Media, Chandos Publishing Social Media Series, 145–152 (2012). 17. Christensen, K., Liland, K., et al.: Mining online community data: The nature of ideas in online communities. Food Quality and Preference, vol. 62, 246–256 (2017). 18. Hussain, S., Guangju, W., Jafar R., Ilyas, Z., Mustafa, G., Jianzhou, Y.: Consumers' online information adoption behavior: Motives and antecedents of electronic word of mouth communications. Computers in Human Behavior, vol. 80, 22–32 (2018). 19. Zeng, M.: Foresight by online communities – The case of renewable energies. Technologi- cal Forecasting and Social Change, vol. 129, 27–42 (2018). 20. Taiminen, H.: How do online communities matter? Comparison between active and non- active participants in an online behavioral weight loss program. Computers in Human Be- havior, vol. 63, 787–795 (2016). 21. Fedushko S., Syerov Y., Kolos S.: Hashtag as а Way of Archiving and Distributing Infor- mation on the Internet. CEUR Workshop Proceedings. Vol. 2386: Workshop Proceedings of the 8th International Conference on “Mathematics. Information Technologies. Educa- tion. 274–286 (2019). http://ceur-ws.org/Vol-2386/paper20.pdf 22. Hryshchuk R., Molodetska K., Syerov Y.: Method of Improving the Information Security of Virtual Communities in Social Networking Services. CEUR Workshop Proceedings. 2019. Vol 2392: Proceedings of the 1st International Workshop on Control, Optimisation and Analytical Processing of Social Networks, COAPSN-2019. p. 23–41 (2019).. 23. Davydova I., Marina O., Slianyk A., Syerov Y.: Social Networks in Developing the Inter- net Strategy for Libraries in Ukraine. CEUR Workshop Proceedings. Vol 2392: Proceed- ings of the 1st International Workshop on Control, Optimisation and Analytical Processing of Social Networks, COAPSN-2019. 122–133 (2019). 24. Yavorska T., Prihunov O., Syerov Y.: Libraries in Social Networks: Opportunities and Presentations. CEUR Workshop Proceedings. Vol 2392: Proceedings of the 1st Interna- tional Workshop on Control, Optimisation and Analytical Processing of Social Networks, COAPSN-2019. 242–251 (2019). 25. Cheng, F., Wu, C., Chen, Y.: Creating customer loyalty in online brand communities. Computers in Human Behavior (2018). 26. Boon, E., Pitt, L., Salehi-Sangaria, E.: Managing information sharing in online communi- ties and marketplaces. Business Horizons, vol. 58, issue 3, 347–353 (2015). 27. Teo, H., Johri, A., Lohani, V.: Analytics and patterns of knowledge creation: Experts at work in an online engineering community. Computers & Education, 112, 18–36 (2017).