132034 UE Social media data: extraction and analysis (2022S)
Prüfungsimmanente Lehrveranstaltung
Labels
DIGITAL
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 07.02.2022 08:00 bis Do 24.02.2022 23:59
- Abmeldung bis Do 31.03.2022 23:59
Details
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Dienstag
01.03.
11:30 - 13:00
Digital
Freitag
04.03.
09:45 - 11:15
Digital
Dienstag
08.03.
11:30 - 13:00
Digital
Dienstag
15.03.
11:30 - 13:00
Digital
Freitag
25.03.
09:45 - 11:15
Digital
Dienstag
29.03.
11:30 - 13:00
Digital
Freitag
01.04.
09:45 - 11:15
Digital
Freitag
08.04.
09:45 - 11:15
Digital
Dienstag
26.04.
11:30 - 13:00
Digital
Freitag
29.04.
09:45 - 11:15
Digital
Freitag
06.05.
09:45 - 11:15
Digital
Dienstag
10.05.
11:30 - 13:00
Digital
Freitag
13.05.
09:45 - 11:15
Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
The course uses continuous assessment and there is no final examination. Throughout the semester students develop a personal project and their progress will be evaluated through homework assignments (60% of grade) and the final submission of the project (30%).The remaining 10% of the course grade is awarded for engagement in the course activities.The final project could be also shared in groups of 2-3 students each, depending on how many students will attend the course. We will define this in the first lectures.
Mindestanforderungen und Beurteilungsmaßstab
Students will need to have access to a computer for the practical component of the course.No prior knowledge is assumed, but a basic familiarity with R/Python languages or general webscraping API will help.The course is evaluated according to a points system. You can achieve 100 points in the course. The research project is rated with up to 90 points (60 for homework assignments and 30 for final submission). Students participation will be rated with up to 10 points.The minimum for the positive completion of the courses is 51 points.Conversion of points to grades: 0-50 = insufficient, 51-60 = sufficient, 61-70 = satisfactory, 71-80 = good, 81-100 = very good
Prüfungsstoff
- basic R/Python exercises
- download and preprocess Twitter data
- analyse Twitter data (example: retweet/mention network analysis)
- download and preprocess Twitter data
- analyse Twitter data (example: retweet/mention network analysis)
Literatur
https://developer.twitter.com/en/products/twitter-api/academic-research/application-infoConover, Michael, et al. "Political polarization on twitter." Proceedings of the International AAAI Conference on Web and Social Media. Vol. 5. No. 1. 2011.González-Bailón, Sandra, et al. "The dynamics of protest recruitment through an online network." Scientific reports 1.1 (2011): 1-7.Pellert, Max, et al. "Dashboard of sentiment in Austrian social media during COVID-19." Frontiers in big Data (2020): 32.
Zuordnung im Vorlesungsverzeichnis
DH-S II, S-DH (Cluster III: Theatre, Film and Media)
Letzte Änderung: Do 11.05.2023 11:27
The more "horizontal" structure of social networks has therefore contributed to a greater democratization of the debate, but the decentralization that makes all users both actors and spectators, the great speed with which information is disseminated and the presence of bots have also posed major problems such as the veracity of the news, the reliability of individual users and the lack of control over phenomena such as hate speech and discrimination of various kinds.
In this context, social networks, on the other hand, offer us the unique opportunity to have at our disposal a large amount of data to analyze and understand the public debate around any topic.This course offers the basics to make a first and simple analysis of data from social networks, focusing not only on the texts of individual posts, but also on the information coming from the structure of the data itself.
Students will learn how to download data (including metadata) from Twitter through the API offered by the platform for academic use, preprocess it, and develop simple analyses such as building a retweet network and highlighting its most influential actors or exploring the presence of communities.Although the course will have a rather practical setting with hands-on sessions, students will still be provided with a theoretical background with some examples of recent research and publications related to these topics. The students will use R (but they are free to use Python for their project) as main programming languages and they will be offered a general introduction to it in the first lessons. Slides and R notebooks will be available after each lecture.The course will be online and will end in May, but the teacher is available on Skype for clarifications and questions related to the course.