Reinforcement of marketing content spreading processes in social media (in Polish: Wspomaganie procesów rozprzestrzeniania treści marketingowych w mediach społecznościowych)


Polish National Science Centre, Grant No. 2016/21/B/HS4/01562


Bródka, P., Musial, K., Jankowski, J., Interacting spreading processes in multilayer networks: a systematic review, IEEE Access 8, 10316-10341, 2020

Karczmarczyk, A., Wątróbski, J., Jankowski, J., Multi-criteria Approach to Planning of Information Spreading Processes Focused on Their Initialization with the Use of Sequential Seeding, Information Technology for Management: Current Research and Future, 2019

Pazura, P., Jankowski, J., Bortko, K., Bartkow, P., Increasing the diffusional characteristics of networks through optimal topology changes within sub-graphs, Proceedings of the 2019 IEEE/ACM International Conference ASONAM 2019

Karczmarczyk, A., Jankowski, J., Watrobski, J., Parametrization of Spreading Processes Within Complex Networks with the Use of Knowledge Acquired from Network Samples, Procedia Computer Science 159, 2279-2293, 2019

Karczmarczyk, A. , Jankowski, J., Watróbski, J., Multi-criteria approach to viral marketing campaign planning in social networks, based on real networks, network samples and synthetic networks. FedCSIS 2019: 663-673

Karczmarczyk, A., Jankowski, J., Wątróbski, J., Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks, PloS one 13 (12), e0209372, 2018

Jankowski, J., Waniek, M., Alshamsi, A., Bródka, P., Michalski, R., Strategic distribution of seeds to support diffusion in complex networks, PloS one 13 (10), e0205130, 2018

Karczmarczyk, A., Bortko, K., Bartków, P., Pazura, P., Jankowski, J., Influencing Information Spreading Processes in Complex Networks with Probability Spraying, 2018 IEEE/ACM International Conference on Advances in Social Networks, 2018

Jankowski, J., Zioło, M., Karczmarczyk, A., Wątróbski, J., Towards sustainability in viral marketing with user engaging supporting campaigns, Sustainability 10 (1), 15, 2017

Jankowski, J., Michalski, R., Bródka, P., A multilayer network dataset of interaction and influence spreading in a virtual world, Scientific data 4, 170144, 2017

Jankowski, J., Bródka, P., Michalski, R., Kazienko, P., Seeds buffering for information spreading processes, International Conference on Social Informatics, 628-641, 2017

Jankowski, J., Michalski, R., Bródka, P., Karczmarczyk, A., Increasing Coverage of Information Diffusion Processes by Reducing the Number of Initial Seeds, Proceedings of the 2017 IEEE/ACM International Conference on Advances, 2017

Jankowski, J., Michalski, R., Increasing coverage of information spreading in social networks with supporting seeding, International Conference on Data Mining and Big Data, 209-218, 2017

Jankowski, J., Bródka, P., Kazienko, P., Szymanski, B.K., Michalski, R., T Kajdanowicz, Balancing speed and coverage by sequential seeding in complex networks, Scientific reports 7 (1), 891, 2017

Jankowski, J., Mixture seeding for sustainable information spreading in complex networks, Asian Conference on Intelligent Information and Database Systems, 191-201, 2017

Jankowski, J., Dynamic rankings for seed selection in complex networks: Balancing costs and coverage, Entropy 19 (4), 170


Network structures and information spreading processes are observed in many aspects of the surrounding world. For example; in the biological systems, signals are transmitted in networks at molecular level [Bray, 2003], within genetic interaction networks [Tong et al., 2004] or neural connections [Bullmore & Sporns, 2009]. Propagation of viruses among organisms is observed when infectious diseases are spreading, and they take the form of epidemics after reaching an epidemic threshold [Allen, 1994]. From the social perspective, information is transmitted during communication between individuals connected within social networks, and is resulting in knowledge diffusion [Rozewski & Jankowski, 2015] or spreading the rumors [Nekovee et al., 2007]. Apart from being natural systems, networks are direct effects of human activity in a form of transportation connections [Bell & Lida, 1997], computer networks [Broido, 2001], telecommunication systems [Barnett, 2001] or online social networking platforms [Ellision, 2007].

Network structures create routes and physical or logical infrastructures for spreading marketing content, information, products in intentional and unintentional ways. The power of spreading mechanisms especially in social media is used for propagation information about products [Schulze, 2014], diffusion of innovation [Peres, 2010], as an important mechanism for political marketing campaigns [Garcia et al., 2012] and takes form of viral marketing [Watts et al. 2007].

Analysis of the spreading processes was not easy a few decades ago because of the inability to collect detailed data from marketing campaigns. Nowadays, electronic systems and social media deliver detailed information about processes and their performance. Research in this field is concentrated on the analysis of real systems [Zaman, 2010], identification of factors affecting content spreading processes [Ho & Dempsey, 2010], mathematical models describing and predicting processes [Khelil et al., 2002], simulations with the agent based environments [Hui et al. 2010] and selection of initial nodes within networks for starting diffusion and marketing campaigns [Hinz et al., 2011]. Selection of initial nodes is analyzed from the perspective of marketing and from the methodological point of view is backed by social sciences [Kimura et al. 2007] as well as by combinatorial optimization [Ackerman et al., 2010][Ben-Zwi et al. 2011][Chiang et al., 2013] and theoretical methods from physics [Morone & Makse, 2015] for quantitative approaches. Presented project has the potential for knowledge transfer from the computer science and computational social science to marketing science towards better understand processes of marketing content spreading. In research will be used background from computer science for creation of new approaches for supporting viral marketing processes.

Existing problems. Typically, the modeling of spreading marketing content within social media is based on the assumption that once a process is started through initial seeds selection, it continues without any additional support [Hinz et al., 2011]. Decisions related to planning the process and selection of initial candidates are taken before the process starts and are based on the incomplete knowledge and theoretical models [Liu-Thompkins, 2012]. At the beginning of the process, only predictions are available about future process characteristics. Running processes deliver more knowledge about dynamics, characteristics of activated and not activated nodes within the network. As a result, additional decisions influencing the process can be more accurate than those taken at the beginning. However, in most solutions, only initially selected seeds are the main catalyst of the process and they launch information cascades within the network. After the start, the process is based only on natural propagation mechanisms. A typical process lasts until there are no more nodes activated.

The problem appears when initially selected seeds do not deliver the estimated number of activations and the natural diffusion processes in a form of viral marketing were not launched with enough intensity. In such cases, total coverage and a number of activations of nodes in the network can be much lower than expected. Another problem is related to the selection of too few initial seeds or their wrong allocation within the social network structures. It can result low dynamics of marketing process. Minimizing the initial seed set was the subject of earlier research; however, it assumes stable conditions within the network and unchanging process parameters. Changing process parameters, like propagation probabilities, affect the dynamics of the process and final results can be smaller than was initially planned.

Most of earlier models assume the same propagation probabilities for all nodes and during the entire process; however, in real systems, this kind of assumption is rarely met.

For dynamic networks with temporal characteristics, conditions after launching the marketing content spread, can be different than expected. For example, the availability of nodes (potential customers) within the social network can be low but it is crucial for the spreading processes and final coverage. Low activity of network nodes with not enough engagement in marketing campaign, negatively influence the whole process. Varying over the time conditions within a social network can be observed and its structure can be changing. Edges between network nodes based in example on communication, phone calls can appear and disappear and it affects the dynamics of the process as well.

Apart from process characteristics, the performance is related to specific social network structures. For example, with many groups of customers which are not communicated and segments which are difficult to reach, the process will not achieve high dynamics. Other problems are related to reaching individual customers within networks with specific attributes. Typical seed selection methods use centrality measures, but they don’t represent structural characteristics of specific regions within the network. As a result, achieving less communicated segments of the network can be difficult and target groups of customers will be not contacted.

The solutions proposed in this project, planned research and experiments will increase knowledge about the processes of spreading marketing content in social networks. The introduced analytical methods will allow analysis of phenomena occurring in them, as well as the selection of appropriate methods of supporting marketing processes. The project fills important gaps and introduces its own methods, which complement the current state of research.


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