Successfully reported this slideshow.
Your SlideShare is downloading. ×
Upcoming SlideShare
Computational Social Science
Computational Social Science
Loading in …3

Check these out next

1 of 53 Ad

More Related Content

Slideshows for you (19)

Viewers also liked (20)


Similar to Diff6b eng (20)


Recently uploaded (20)

Diff6b eng

  1. 1. Diffusion Marketing: An Innovative Marketing Approach for Social Media Aykut Arikan Yeditepe University @aarikan
  2. 2. The Very Basics…
  3. 3. We live, work, think, and act in an unusually amorphous ecosystem called Social Media, without even being totally aware of its settings, dynamics and structures...
  4. 4. “Social media is the democratization of information, transforming people from content readers into publishers. It is the shift from a broadcast mechanism, one-to-many, to a many-to-many model, rooted in conversations between authors, people, and peers” (Solis: 2011).
  5. 5. «… to deliver a descriptive and theoretical framework for the concept of Diffusion Marketing as a proposed innovative approach for social media marketing.»
  6. 6. «How can a prototypical framework for Diffusion Marketing be defined?»
  7. 7. Diffusion Marketing is not a new category proposal, but an innovative approach to Social Media Marketing as its category. (Premise 1)
  8. 8. These are the initial exploratory outcomes of an ongoing research that is in the proposal phase. (Premise 2)
  9. 9. The research is empirically unproven yet. (Premise 3)
  10. 10. Our intention is to deliver a proof-of- concept that provides an innovative approach to Social Media Marketing. (Premise 4)
  11. 11. Feel free to contribute critically: @aarikan . (Premise 5)
  12. 12. Social Media A Theoretical Framework for an Emerging Discipline
  13. 13. Diffusion Theory Graph Theory Game Theory Big Data
  14. 14. A foundation to reveal dynamics of social media structures and processes in which an unconventionally novice approach to marketing is based. Everett Rogers, 1931-2004
  15. 15. (Rogers: 2003)
  16. 16. “serve as mathematical models of network structures” “study of network structures” (Easley & Kleinberg: 2010)
  17. 17. “a theory of decision making” (Morton 2012) • All diffusion patterns are results of such games. • Delivers a fundamental framework to analyze decisions that trigger dynamics of social structures.
  18. 18. Network Science A Small World of Six Degrees
  19. 19. Walk through the city by crossing each bridge once. Euler’s Seven Bridges of Königsberg Problem
  20. 20. «Sociogram» Jacob L. Moreno
  21. 21. Model of Erdös & Rényi, 1959
  22. 22. Small World Hypothesis (Six Degrees Experiment) Stanley Milgram (1933-1984)
  23. 23. Albert-László Barabási
  24. 24. “Interactions of individuals in a large system can generate greater complexity than the individuals themselves display, and sometimes much less” (Watts: 2004). • Larger complexity is the elementary state of social media. • Dynamics of social structures (as free-agents) that cause diffusion of ideas, emotions, sentiment, etc. also cause this kind of complexity. A New Science: Idea Machines in a Random Universe
  25. 25. A New Science: Idea Machines in a Random Universe (Cont’d) • Complexity in social networks and social media is generated by human beings as free-agents that have the ability to make choices. • It is us who make up social networks with our choices on relationships. • It is again us who cause dynamics in social networks and social media by producing, reproducing, and diffusing our ideas, emotions, sentiment etc. around along our choices. • We are so-called idea machines that generate these diffusion patterns along our choices.
  26. 26. A New Science: Idea Machines in a Random Universe (Cont’d) • It is a game plan that constructs random networks on physical ones. • We as idea machines, propose and supply a random universe on social networks and social media, along our choices. • And finally, it is again us idea machines, to show our demand for this random universe by liking, sharing, retweeting, favoriting, etc. other machines supply as our Engagement.
  27. 27. New Rules of Engagement • While conventional media and publishing is based on synchronous or asynchronous but linear messages, in social media the message is something that emerges from content. • It is not the message that is disseminated, but the content. • Thus, while content might be linear, the message is not linear, but rather structural.
  28. 28. New Rules of Engagement (Cont’d) • It is the audience that actively interacts with the content and engages to the message, positively or negatively. • While conventional publishing and media is about speaking and broadcasting, social media is about listening and monitoring.
  29. 29. The Monitoring Issue • Monitoring is a way of listening actively to social media content and defining positive or negative engagement. • This might look very simple or mechanical but language problems have a major stake in problems that are observed in computerized approaches. • Analysing content and discourse to define sentiment is not always that easy.
  30. 30. Influence: Pathohen vs. DNA • Although there is insufficient empirical evidence of direct relationship between diffusion and engagement, especially businesses and commercial applications preferred to concentrate on content instead of engagement. • The result was contagious content examples in the form of visuals or video: so called viral content. • Indeed, the contagiousness of some viral content is certainly high, which unfortunately does not necessarily assure that its effect or engagement potential is consequently high. • As a matter of fact, measured engagement of some highly contagious viral content is naively very low, if not null.
  31. 31. Viral Epidemics: Two Principal Features Contagiousness Infectiousness
  32. 32. Viral Epidemics: Two Principal Features Pathogen DNA
  33. 33. Viral Epidemics: Two Principal Features Diffusion (Speed of Dissemination) Engagement (Replication Ability)
  34. 34. Viral Epidemics: Two Principal Features Diffusion CONTENT Engagement MESSAGE
  35. 35. Big Data and Beyond BIG DATA Interactions (vector data) Relationships (edge data) Users (node data)
  36. 36. Diffusion Marketing Creating New Capacities
  37. 37. Diffusion Marketing: Creating New Capacities
  38. 38. Social Network Analyses • Network Structure Analysis, • Active and Passive Engagement Analysis, • Structural Organization Hole Analysis, • Network Partitioning Analysis, • Structural Balance Analysis, • Strategic Reasoning Analysis, • Business Network Cluster Analysis.
  39. 39. Mapping the Diffusion Game • Influencer Mapping, • Network Activity Mapping, • Key Audience Mapping, • Shortest Path Mapping, • Flow Determination Mapping, • Homophily Mapping, • Heterophily Mapping, • Best Response Mapping, • Viral Mapping, • Key Audience Mapping • Boundary Spanner, • Information Broker, • Peripheral Specialist, • Central Connector, • Voting Action Mapping, • Epidemic Mapping, • Networked Coordination Game Mapping.
  40. 40. Creating the Critical Mass: Synthesizing Social Epidemics in the Lab Epidemic Modelling Networked Coordination Game Modelling
  41. 41. Some Empirical Observations
  42. 42. Some Empirical Observations • Collected on October 15th, 2014. • The dataset was extracted through the Netvizz application on Facebook. • The data file was analyzed and visualized the open source application Gephi (Version 0.8.2 beta for Windows). • Due to ethical reasons person’s names were anonymized. • Coverage of 1.181 people (nodes) and 17.371 relationships (edges).
  43. 43. 72% 20% 6% 1% 1% Location Distribution Turkey US UK Germany Other
  44. 44. 57% 37% 6% Gender Distribution Male Female No Data
  45. 45. Conclusion
  46. 46. Conclusion • Diffusion Marketing, as an innovative marketing approach for social media delivers new capacities to the field by utilizing the above mentioned approaches that make all sense for constructing a controlled social epidemic as a marketing campaign. • Consequently, the innovative and prototypical nature of Diffusion Marketing, makes further clarification and research a necessity, as well it is due to be developed further in practice. • Accordingly, the enormous speed of development of social media, both quantitatively and qualitatively, makes further research and modelling a necessity. • Besides, analysis and modelling techniques that are in constant development themselves, deliver a positive contribution to this necessity.
  47. 47. Bibliography • Barabási, A. L. (2002), Linked: the new science of networks. NY: Perseus. • Cialdini, R. B. (2007), Influence: the psychology of persuasion. London: Collins. • Davis, M. D. (1983), Game theory: a nontechnical introduction. Mineola; NY: Dover • Mayer-Schönenberger, V. & K. Cukier (2013), Big data: a revolution that will transform how we live, work and think. London: John Murray. • Rogers, E. (2003), Diffusion of innovations. NY: Free Press. • Solis, R. (2010), Engage!: The Complete Guide for Brands and Businesses to Build, Cultivate, and Measure Success in the New Web. NY: Wiley. • Watts, D. J. (2004), Six degrees: the science of a connected age. London: Vintage.
  48. 48. Thank you… Aykut Arikan @aarikan