Make Fake News Visible - Visualization of social networks

This work focuses on the visualization of processes in social networks. The emphasis here is on finding and implementing adequate methods to illustrate the dissemination of misinformation in social media.

In recent years, the issue of influencing people by disseminating false information has been a recurrent topic in the media. For example, the US presidential election in 2016 sparked a broad debate about the influence of Russian bots in social media. Furthermore Donald Trump, who popularized the term "Fake News" , is also a considerable source of misinformation himself (Sweden(4), Inauguration(1) etc). The consequences of such Fake News are not only reflected in election results but can also have other effects, For example, the claim made in 2013 by the "Syrian Electronic Army" about a terrorist attack on the White House in which President Obama was injured, caused a crash on the stock markets. Meanwhile, in WhatsApp had to deactivate the group share function, as it caused lynchings of innocent people in India. When fast spreading misinformation has severe real-world consequences, we speak of "Digital Wildfires". We have created the UMOD(5) project, which aims at understanding such Digital Wildfires on all forms of electronic news platforms. The work described here moves within the scope of social networks, especially Twitter.


We are interested in questions concerning the visualization of social networks, primarily the following two:

  1. Visualization of particularly large graphs.
  2. Interactive Visualization.

For the visualization of particularly large graphs, there are a handful of tricks that can be used to merge groups of nodes, for example. In addition, there is a huge number of layout algorithms that arrange a graph in a way that makes it look particularly beautiful. Here force-directed graph algorithms(3) should be evaluated and implemented. Another topic we consider in this context are distributed implementations of these algorithms since the social networks we deal with are often too large to process on a single machine. Beyond that we look for solutions to visualize special scenarios. As an example, consider the two visualizations in Figure 3. The purpose of this proposal is simply to give you a guideline, a concrete plan for your work, we would make according to your interests and ideas. Please send us an email if you are interested.


We expect self-motivation, initiative and the ability to work independently,as well as working knowledge of a programming language such as Python or Java. Knowledge of social network analysis is not required, although familiarity with Twitter would be helpful.


  • Pål Halvorsen
  • Carsten Griwodz
  • Johannes Langguth
  • Daniel Thilo Schroeder

Collaboration partners

TUB - Technical University of Berlin
UiO - University of Oslo


[1] Inauguration of Donald Trump. N.d. Accessed: 2019-01-22.

[2] Kreil, Michael. N.d. “34C3 Social Bots, Fake News und Filterblasen.” Accessed: 2019-01-22.

[3] Mchedlidze, Tamara. N.d. “Algorithms for Graph Visualization.” Accessed: 2019-01-22.

[4] Sweden to Trump What happened last night? N.d. Accessed: 2019-

[5] UMOD: Understanding and Monitoring Digital Wildfires. N.d. Accessed:2019-01-22.

Annoucement at UiO