Social networks such as Facebook, Instagram or WhatsApp have become an integral part of modern life and have long since become an integral part of everyday life. But free use also has its price. Operators of such social networks collect the personal data of users in order to earn money with this information. According to scientific studies, operators are even able to create shadow profiles of users. Shadow profiles contain information that a user has never provided or wanted to provide.

During this CAMMP day, middle school students will learn how simple mathematical rules can be used to predict a person's age. They work with original data from the social network Friendster, the pioneer of Facebook.

In order to be able to reliably predict the age of a user, the data is processed independently by the students. They then apply various prediction rules for a user's age, based mainly on location and scattering measures. A main objective of the CAMMP day is to encourage students to critically reflect on their own use of social networks. The CAMMP day also provides them with an example of the practical relevance of mathematics in their everyday lives.

Version with two-level tree diagrams:

Optionally, the third worksheet can also be used to look at a random prediction rule. With a certain probability q, a given age interval is assigned to the user here. For this rule, the SuS determine the experimental and theoretical hit probability. To this end, the SuS should already have gained initial experience with two-stage random experiments and tree diagrams. If there is interest in this version, this can be communicated by mail when registering for this CAMMP day.

Duration: 5 - 6 hours (incl. lunch break)
Contents: relative frequency, arithmetic mean, median, span, quartile distance, box plots
Previous knowledge: First experiences with Boxplots
Target group: 7th grade and older
Created by: Nils Steffen, Maike Sube
Registration: Appointments can be made individually by e-mail at KIT or RWTH Aachen University.

Source of the image: https://pixabay.com/de/photos/menschen-google-polaroid-pinterest-3175027/

Material

The interactive learning material can be accessed via the online platform workshops.cammp.online. How to create an account on the platform and use the
material is explained in this video. In addition, accompanying material is provided for teachers on the online platform, which can be accessed via a password that can be requested by e-mail.

• Steffen, N.: Sicherheit der Privatsphäre in sozialen Netzwerken - Wie Mathematik die Nutzer ausspioniert. Ein Lehr-Lern-Modul im Rahmen eines mathematischen Modellierungstages für Schülerinnen und Schüler der Sekundarstufe I, Master thesis, RWTH Aachen, 2019.

• Sube, Maike.: Komplexe Modellierung: Kann man mit Mathematik Wahlen gewinnen? Big Data Analysen von sozialen Netzwerken mit Schülerinnen und Schülern der Sek. II (presentation, conference proceedings contribution), GDMV, Paderborn, 2018.

• Sube, M.: Komplexe Modellierung: Kann man mit Mathematik Wahlen gewinnen? Big Data Analysen von sozialen Netzwerken (workshop), ISTRON-Tagung, Hamburg, 2017.

• Sube, M.: Wie sicher ist meine Privatsphäre in sozialen Netzwerken? Unterrichtsmaterial zur Thematik Big Data (workshop), MNU Aachen, 2017.

• Frank, M.; Roeckerath, C.; Sube, M.: Komplexe Modellierung: Datensicherheit in sozialen Netzwerken, GDM-Tagung, Potsdam, 2017.

• Sube, M.: Wie sicher ist meine Privatsphäre in sozialen Netzwerken? …und was hat das mit Mathe zu tun?, Master thesis, RWTH Aachen, 2016.