Development of a program to estimate the time of posting a message in the online social network VKontakte

The relevance of the study is due to the high popularity of social networks for the transmission of information, as well as the influence of the time of posting a message on the number of views and, accordingly, the degree of its distribution. The article presents a description of the program for estimating the time of posting messages in groups of the social network Vkontakte. Algorithms of data collection and processing are presented. The description of used requests to VK API by means of standard methods is given. The developed program takes into account the activity indicators of participants and their individual characteristics (the number of friends, groups, posts on the wall, etc.), as well as the number of messages published by other members of the social network. Linear convolution was applied to obtain the integral characteristic . The program is implemented using the C# language. Microsoft Excel spreadsheet processor was used to store data about subscribers and results. An example of estimating the time of posting a message in the selected group of the social network Vkontakte is considered. The developed program can be used by administrators of social network communities to estimate the time of posting in groups of the online social network and to choose the best moment of publication.

1. Goyal S., Gagnon J. Social networks and the firm. Revista de Administracao. 2016;51(2):240-243.

2. Gribanova E.B., Katasonova A.V. System to evaluate social network groups for the implementation of marketing activities. Proceedings of TUSUR. 2017;20(2):68-72.

3. Bakshy E., Hofman J., Mason W., Watts D. Everyone’s an Influencer: Quantifying Influence on Twitter. Proceedings of the 4-th International Conference on Web Search and Web Data Mining. Hong Kong. 2011:1-10.

4. Bhagat S., Goyal A., Lakshmanan, L. Maximizing product adoption in social. Proceedings of the 5-th ACM International Conference on Web Search and Data Mining. Seattle. 2012: 603-612.

5. Chen W., Collins A., Cummings R., Ke T., Liu Z., Rincon D., Sun X., Wei W., Wang Y., Yuan Y. Influence maximization in social networks when negative opinions may emerge and propagate. Proceedings of the 2011 SIAM International Conference on Data Mining. Mesa. 2011:379-390.

6. Gribanova E.B., Logvin I.N., Shirenkov I.V. Algorithm for evaluating the marketing activities of the online social network Vkontakte based on the cascade model of information dissemination. Proceedings of TUSUR. 2018;21(3):66-73.

7. Booth J.A., Howard J., Rankin A. System and Methods for Generating Optimal Post Times for Social Networking Sites. United States Patent. US 0275348 A1. 2013:1-8.

8. Benevenuto F., Rodrigues T., Cha M., Almeida V. Characterizing User Behavior in Online Social Networks. Proceedings of the 9th ACM conference on Internet measurement. Chicago. 2009:49-62.

9. Chumak А.А, Ukustov S.S, Kravets A.G., Voronin, J.F. Social Networks Message Posting Support Module. World Applied Sciences Journal. 2013;24(24):191-195.

10. Savitskii A.S. Model' otsenki vremeni razmeshcheniya soobshchenii v gruppakh sotsial'noi seti s uchetom skorosti obnovleniya novostnoi lenty. Materialy mezhdunarodnoi nauchnotekhnicheskoi konferentsii studentov, aspirantov i molodykh uchenykh «Nauchnaya sessiya TUSUR-2019». Chast' 3. Tomsk. 2018:114-116.

11. Savitskii A.S. Model' vybora vremeni razmeshcheniya soobshchenii v gruppakh onlainovoi sotsial'noi seti. Sbornik nauchnykh trudov nauchnoi konferentsii «Informatsionnye tekhnologii v nauke, upravlenii, sotsial'noi sfere i meditsine». Chast' 2. Tomsk. 2018:90-92.