Risks and Opportunities of Social Media Data

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Fact Box
Module Web and Society
Benjamin Krumnow
Credits 3
Term Term 1, Term 2
Course is not required
Current course page Summer 2017
Active Yes

The big idea

In today’s online society, social media gives users a wealth of opportunities to be connected with other people, to express themselves to a bigger audience or to gain all kind of relevant information from other users. Thereby, all these activities produce data, that is collected by the service providers. It is often not clear, in what extent a service provider makes use of this data. On the one hand, personal data collections can serve a useful and necessary purpose, such as the improvement of the user experience or the facilitation of core functionality of the service. It can give also the opportunity for new businesses and research fields to emerge. On the other hand personal data collections has led to a widely applied practice of data analysis and personal information trading, which can highly compromise the users’ privacy and lead to many further problems[1].

In face of these contradictions, this course deals with the implications for the society from personal data collections. Therefore the main subjects are the discussion of business models based on personal data[2], common practices to analyse personal data collections and alternatives to the current data driven social media landscape.

Intended learning outcomes

By the completing this course the participants will:

  • be familiar with the interest groups of social media services and know their revenue models.
  • be aware of threats which can appear with the collection of personal data in social media and implications of these threats.
  • be able to lead a discussion about different infrastructures for social media services and will know their strengths.
  • know methods of protecting users’ privacy in social network services with a single service provider and their benefits and limitations.
  • have a basic understanding of technical aspects of data mining as well as machine learning and its usage
  • know ethical issues and problems, that arise with the usage of data mining for processing personal information

Structure of the course

The Social Media Industry

Regarding today's social media landscape many popular social media services are provided by commercial companies.[3] Those companies have got expenses for maintaining and extending their server infrastructure, implementing new features in their services, employing qualified staff and so forth. Even though many of these services remain free to use. This aspect often affects the design of the service, which tends to collect personal data for commercial purposes and threatens the users’ privacy.[4]

For this reason this lecture will concentrate on different options to generate revenue streams, their impacts on users as well as the explanation of basic terms and characteristics of a server based infrastructure. At first the term "social media"[5] will be clarified and an understanding distinction between the terms service, service provider and technology will be developed. Afterwards business models of popular social media services and alternative approaches will be discussed. As a part of this the concepts of homophily, influencer and ad targeting based on user information[5] will be introduced followed by a discussion about their impacts on users.

Threats, preventions and dilemmas in social media

Besides the known threats for privacy in social media services, additional threats arise with the involvement of adverse parties. This includes technology-based threats as well as organisational threats like impersonation attacks or social engineering. Users of these services might not be aware of these threats or are unable to protect themselves due to limited technical knowledge or insufficient prevention methods. On the other hand leaving a service can imply several consequences like the affection of social life.[4]

Therefore the analysis of social dilemmas like leaving a social media service and rules defined by a service provider is taking place in this course. Furthermore the course will deal with common risks and their prevention apart from privacy violations.[6] Beyond that users’ trust in service providers and the efficiency of recent developments like encryption in messengers to protect the users’ privacy will be discussed.

Decentralisation of Social Network Services (SNS)

After exploring possible threats and preventions this lecture will focus on approaches to avoid personal data collections by a single service provider and associated problems. This attempt can be addressed by decentralisation of SNS. Especially in the area of SNS, scientific approaches and production systems use peer-to-peer networks or decentralised servers to distribute the burden of single service provider across several participants. Those approaches often reduce economic incentives; offer more scalability, openness and protection of users’ privacy.[7] Though, services with a single service provider have a lot of advantages in relation to security, which cannot be archived by decentralised services in the same manner. Furthermore, decentralized services bring new challenges to researchers in order to make these services open, secure, reliable, scalable and easy to use.[1][8]

In order to get into this topic the course will first and foremost deal with the infrastructure of SNS. Therefore, possible decentralized infrastructures like peer-to-peer systems and decentralised servers will be introduced and compared with a centralised infrastructure in the context of SNS. After that it will be discussed which problems can be addressed by these solutions and how the data is protected. In addition the course will analyse decentralised SNS for limitations in protecting the users’ privacy.

Data Mining and Machine Learning

The collecting of social media data by a service provider is just the first step because the vast amount of data has to be processed to gain useful information. This is where data mining comes into play. With this technology it is possible to discover patterns in the data. Therefore, data mining allows researchers to analyse a huge amount of data and to gain information for what individuals wouldn’t be able to do or would need a long time to achieve. Nevertheless, further steps (like preparation, interpretation, selection and evaluation) are required to gain valuable information.[9] This process (also known as Knowledge Discovery in Databases) is the topic of this workshop. Additionally machine learning and classifiers will be introduced and their relevance for social media data will be outlined. Afterwards ethical issues, which arise with the usage of this technology on social media data, will be explored with use cases by the participants.[10]

Didactic Concept, Schedule and Assignments

As a didactic concept this course contains online and on-site seminars with presentations by the lecturer and basic readings that will be discussed during the workshops. Furthermore, activities and case studies will be used to figure out critical issues. Advanced topics are going to be explored within one 7 minute video presentation per student. The presentation will be uploaded and shared with the other participants. Possible fields as topics may be countermeasures against the gathering of personal data, a specific usage of social media data collections, analytical processing or other interesting phenomena in this field.

Introductory lecture

The first lecture begins with a recent case study to motivate the topic and give an overview of applied technologies as well as ethical issues and conflicts in the field of social media data. Afterwards the participant and the lecturer will explore the notions of technical terms and revenue models in an interactive way. The session ends with a short discussion about conflicts and alternatives of advertisement based revenue models and organisational aspects.

1st online Workshop

The course participants will work in groups and do a practical example to explore different threats and perceived security in social media services. Afterwards the lecture presents risks and their preventions, which will be discussed with the course participant. For the next workshop the paper from Schwittmann et al.[11] will be prepared by the students.

2nd online Workshop

At first the paper from Schwittmann et al.[11] is going to be discussed to study different types of decentralised SNS. Additionally the participants will experience use cases and user needs in several contexts for decentralised services. The students will prepare chapter 6 & 8 of the book from Han et al.[9].

3rd online Workshop

The course participant will delve into basic concepts of data mining and the KDD process on the basis to the paper from Han et al.[9]. Afterwards the students are going to work in groups examining and discussing three case studies to elaborate ethical issues in data mining and machine learning.

Wrap up session (on-site)

During this session the lecturer gives a brief summary and the participants can clarify open questions. The remaining time will be used for the examination and to reflect the course.


During the semester the participants will create one quick presentation about a specific topic, which is held as an online video and counts for 40% of the final grade. The remaining percentage will be determined by an exam, which will be written during the wrap up session.

The content of the exam will be composed of questions from the lecture’s presentation, discussed papers and activities, which will be performed during the workshops.


  1. 1.0 1.1 Zhang, Chi; Xiaoyan, Zhu; Yuguang, Fang, eds (2010) (in English). Privacy and security for online social networks: challenges and opportunities. Network, IEEE. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5510913&isnumber=5510907/. 
  2. Albarran, Alan, ed (2013) (in English). The Social Media Industries. Routledge. ISBN 978-0-415-52319-6. 
  3. Google+ and Youtube ⇒ Google, Skype ⇒ Microsoft, Facebook ⇒ Facebook Inc, Twitter ⇒ Twitter Inc., Xing ⇒ Xing AG>
  4. 4.0 4.1 Bruce, Schneier, ed (2013) (in English). Surveillance as a Business Model. Blog: Schneier on Security. https://www.schneier.com/blog/archives/2013/11/surveillance_as_1.html. 
  5. 5.0 5.1 Kaplan, Andreas, ed (2010) (in English). Users of the world, unite! The challenges and opportunities of Social Media. 53(1). Business Horizons, Elsevier. http://michaelhaenlein.com/Publications/Kaplan,%20Andreas%20-%20Users%20of%20the%20world,%20unite.pdf. 
  6. Honan, Matt, ed (2012) (in English). How Apple and Amazon Security Flaws Led to My Epic Hacking. www.wired.com. http://www.wired.com/2012/08/apple-amazon-mat-honan-hacking/all/. 
  7. Anwitaman, Datta; Strufe, Thorsten; Rzadca, Krzysztof, eds (2010) (in English). Decentralized online social networks. In Handbook of Social Network Technologies and Applications,. http://link.springer.com/book/10.1007%2F978-1-4419-7142-5. 
  8. Paul, Thomas; Sonja, Buchegger; Thorsten, Strufe, eds (2012) (in English). Exploring decentralization dimensions of social networking services: adversaries and availability. New York, NY, USA: In Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research, HotSocial ’12. http://dl.acm.org/citation.cfm?id=2392630/. 
  9. 9.0 9.1 9.2 Han, Jiawei; Pei, Jian, eds (2011) (in English). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. ISBN 978-0-12-381479-1. 
  10. Custers, Bart; Schermer, Bart; Zarsky, Tal, eds (2013) (in English). Discrimination and Privacy in the Information Society - Data Mining and Profiling in Large Databases. 3. Heidelberg: Springer Berlin Heidelberg. ISBN 978-3-642-30486-6. http://link.springer.com/book/10.1007/978-3-642-30487-3/. 
  11. 11.0 11.1 Schwittmann, Lorenz; Boelmann, Christopher; Weis, Torben, eds (2014) (in English). Privacy Preservation in Decentralized Online Social Networks. IEEE Internet Computing, vol. 18. http://www.computer.org/csdl/mags/ic/2014/02/mic2014020016-abs.html. 

Past Course Pages